If you moved to Iowa from another state, you must register to vote in Iowa to be able to vote. You may register on Election Day by going to your local polling place and completing a registration application, taking an oath, and providing proof of identity and residence. Unless you take advantage of election-day registration, the last day to register in time to vote in the next primary or general election is by 5 p.m. ten days before the election.
If you moved from out of state, you do not have to be an Iowa resident for a certain period of time before registering to vote.
If you moved within the same county you must notify your county auditor or you may report the change on Election Day at the polls. If you moved to a new address in a different county, you must complete a new registration form to update your registration records. That form is available here: http://www.sos.state.ia.us/pdfs/elections/voteapp.pdf .
Iowa allows voters to register on Election Day.
If you moved to a new address in the same county since you last voted, you should contact your local election office to determine whether your registration is current and to find out the location of your current polling place. Election officials may have changed your registration record to reflect your new address even if you did not notify the election official about the move. If your registration information has been changed to your current address, you should go to the polling place with that address to vote.
Many registered Iowa voters who move are still entitled to cast a ballot that will be counted — even if they did not notify the appropriate election official about their move before Election Day and the election official has not changed their registration.
If you moved to a new address that is covered by the same polling place as your old address, you can vote at that polling place after filling out a voter registration form. This is true regardless of how close to the election you moved.
A list of the various acceptable forms of identity is available on the Secretary of State’s website.
If you do not provide proof of identity or residency, a registered voter in the precinct may attest to the fact that you are who you say you are and that you live in the precinct.
If you do not provide proof of identity or residency and have no one to attest for you, you may cast a provisional ballot. A special board will meet after Election Day to review your registration record and the information provided, and will decide if the ballot can be counted.
 Iowa Code Ann. §§ 48A.5(1) (West 2010).
 Iowa Code Ann. § 48A.7A(1)(a).
 Iowa Code Ann. § 48A.9(1).
 Iowa Code Ann. § 48A.5(2)(b).
 Iowa Code Ann. § 48A.27 (West 2010); see also Voter Registration, supra at 5.
 Iowa Code Ann. § 48A.10.
 Iowa Code Ann. § 48A.7A(1)(a) (2010).
 Iowa Secretary of State, Iowa Precinct Election Official Guidebook 19 (2010), available at http://www.sos.state.ia.us/PDFs/publications/Guide.pdf .
 See Iowa Secretary of State, Election Day Registration Guide, available at http://www.sos.state.ia.us/elections/voterinformation/voterregistration.html#2 .
 Iowa Code Ann. § 48A.7A(1)(c).
 See Election Day Registration Guide, supra note 13.


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The Family Reunification Directive does not make a distinction between whether a family relationship arose before or after the entry of the resident into the Member State. An application for family reunification may not be refused where the sponsor, the resident within EU territory, has proved that he has stable and regular resources which are sufficient to maintain himself and the members of his family, but who, given the level of his resources, will, nevertheless, be entitled to claim special assistance in order to meet exceptional, individually determined, essential living costs, tax refunds, or income-support measures.
Mr Chakroun had lived in the Netherlands for two years, holding a residence permit for an indefinite period, when he married in 1972. In 2005 he became unemployed and received unemployment benefits. In 2006, Ms Chakroun applied for a Dutch residence permit in order to live with her husband. The application was denied, as her husband’s unemployment benefits were below the required minimum income of 120 percent of the minimum wage. Ms Chakroun appealed the decisions of the Minister and of the Hague District Court. She challenged the compatibility of the Dutch law on the minimum wage and minimum holiday allowance with the Family Reunification Directive, as well as the distinction drawn between family formation and reunification, i.e. whether a family relationship arose before or after the entry of the resident, under Dutch law.
‘1. Should the phrase “recourse to the social assistance system” in Article 7(1)(c) of the Family Reunification Directive be interpreted as permitting a Member State to make an arrangement in respect of family reunification which results in family reunification not being granted to a sponsor who has provided evidence of having stable and regular resources to meet general subsistence costs, but who, given the level of such resources, will nevertheless be entitled to claim special assistance to meet exceptional, individually determined, essential living costs, income-related remission of charges by municipal authorities, or income-support measures in the context of municipal minimum income policies (“minimabeleid”)?
As to the first question, the Court highlighted that Art. 4(1) of the Family Reunification Directive imposes precise positive obligations on Member States, requiring family reunification of certain members of the sponsor’s family, without being left a margin of appreciation. However, Art. 7(1)(c) allows Member States to require evidence that the sponsor has stable and regular resources which are sufficient to maintain themselves and the family members without recourse to the social assistance system of the Member State. For the purpose of evaluating the sponsor’s resources, Member States may take into account the level of minimum wages and pensions as well as the number of family members. Since the needs of individuals vary greatly, Member States may indicate a certain sum as a reference amount, but not as meaning that they impose a minimum income level below which all family reunifications will be refused, irrespective of an actual individual examination. Art. 7(1)(c) should not be interpreted so as to undermine the objective of the Directive, which is to promote the effectiveness of family reunification, in line with international law. The Court pointed to the independent meaning of “social assistance” in the European Union, which refers to social assistance granted by the public authorities, whether at national, regional or local level. This interpretation is reflected in Art. 7(1)(c) as the concept of “social assistance” is understood as assistance to compensate for a lack of stable, regular and sufficient resources and not as assistance which enables exceptional or unforeseen needs. In the Netherlands, where a family relationship was formed before the resident’s entry into EU territory, minimum wage is considered to fulfil the purposes of Art. 7(1)(c); this criterion shall be applied also to family formation after entry.
As to the second question, the Court interpreted Art. 2(d) of the Family Reunification Directive to mean family reunification without drawing any distinction based on the time of marriage of the spouses, i.e. whether the family relationship arose before or after the resident’s entry, as the Directive seeks to protect the family and to establish or preserve family life. Art. 9(2) contains an exception to this rule, with regards to refugees, requiring family relationships to predate their arrival on EU territory in order to fall within the terms of the Directive. This interpretation is in line with human rights documents (the Charter and ECHR). The capacity of a sponsor to have regular resources which are sufficient to maintain themselves and the family members cannot in any way depend on the point in time at which their family was formed.
The answer to the first question is that the phrase “recourse to the social assistance system” in Art. 7(1)(c) of the Family Reunification Directive must be interpreted as precluding a Member State from adopting rules in respect of family reunification which result in such reunification being refused to a sponsor who has proved that he has stable and regular resources which are sufficient to maintain himself and the members of his family, but who, given the level of his resources, will nevertheless be entitled to claim special assistance in order to meet exceptional, individually determined, essential living costs, tax refunds granted by local authorities on the basis of his income, or income-support measures in the context of local-authority minimum-income policies (“minimabeleid”).
The answer to the second question is that the Family Reunification Directive, in particular Article 2(d) thereof, must be interpreted as precluding national legislation which, in applying the income requirement set out in Article 7(1)(c) of the Directive, draws a distinction according to whether the family relationship arose before or after the sponsor entered the territory of the host Member State.

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Art. 4
Art. 7
Art. 7
Art. 7
Art. 7
Art. 2
Art. 9
Art. 7


Chapter 31C - Uniform Disposition of Community Property Rights at Death Act.
§ 31C-3. Disposition of community property upon death.
§ 31C-4. Perfection of title of surviving spouse.
§ 31C-5. Perfection of title of personal representative, heir or devisee; duty of personal representative.
§ 31C-7. Purchaser for value or lender.
§ 31C-9. Severing or altering of married persons.
§ 31C-10. Limitations on testamentary disposition.
§ 31C-11. Uniformity of application and construction.


This case arose in connection with the education of Shannon Carter, a learning disabled student at the Timmonsville High School in Florence County School District Four, South Carolina. Shannon Carter and her parents challenge the Individualized Education Program (IEP) designed for Shannon and appeal the decision of a local education agency administrative hearing officer and the affirmance on appeal of that decision by a state education agency administrative hearing review officer that the school district met its obligation to provide Shannon a free appropriate public education (FAPE) pursuant to the requirements of the Education of the Handicapped Act (EHA), 20 U.S.C. §§ 1400, et seq.
Trial was had before the court without a jury on July 13, 1988, and July 14, 1988, and again on May 9, 1989. The parties thereafter made supplemental filings, and the matter became ripe for decision. Having given careful consideration to the record in this case as well as the arguments and other submissions made by counsel for the parties, the court, pursuant to Rule 52 of The Federal Rules of Civil Procedure, herein publishes its Findings of Fact and Conclusions of Law.
2. At the beginning of the 1982-1983 school year, Shannon re-entered public school in Timmonsville and was enrolled in the seventh grade.
4. In the fall of 1984 Shannon entered the ninth grade at Timmonsville High School and was placed in a regular classroom. Shannon continued to perform poorly as reflected by her first semester averages.3 This continued poor performance prompted Shannon’s parents to seek additional testing for her. In February 1985, Mrs. Carter contacted the school district to request another evaluation.
5. On April 13, 1985, Shannon was again tested by Mr. Ken Bailey, a state certified school psychologist. Following the evaluation it was agreed that a meeting would be held to discuss its results. At the meeting held on April 18, 1985, Bailey reported Shannon’s evaluation results concluded that, based on State Department of Education criteria, Shannon qualified for a learning disability (LD) placement. He explained that there existed “a significant discrepancy between ability and achievement according to the one and one-half standard deviation difference formula. . . .”4 This evaluation indicates a serious learning disability, with a variance between Shannon’s verbal I.Q. and performance I.Q. of 36 points.5 Various subtests administered by Bailey yielded reading ability levels from 4.7 grade level equivalency to a 6.8 level.
6. Shannon suffers from a serious and significant learning disability. Testimony from Dr. James Ward and Dr. Allison Grant indicate that Shannon’s learning disability is on the severe end of the scale. According to Shannon’s parents and Dr. Grant, Shannon also suffered from significant emotional overlay manifested by depression, feelings of low self-worth and self-esteem, as well as suicidal thoughts. Testimony from Ms. Margaret Mitchell of Trident Academy indicates that Shannon entered Trident Academy in the fall term in 1985 as a functional illiterate.
7. Subsequent to the evaluation performed by Bailey, an IEP meeting was scheduled for May 1, 1985. Numerous personnel from Florence County School District Four were present as well as the parents of Shannon and Ms. Linda Summer.6 During that meeting, the IEP proffered by the district was read to the Carters. The school district was of the opinion that an L.D. resource class would be the appropriate placement for Shannon. The parents disagreed and requested an L.D. itinerant program.7 According to the parents’ testimony, their reasoning was that it would be inappropriate for Shannon to be in a resource class with other special education students with emotional illnesses and/or mental retardation. Lisa Free, the L.D. teacher at Timmonsville High School, testified that the majority of her students that year were mentally retarded and that segregation between these students and her L.D. students was improbable.
8. Despite the disagreements that existed between Shannon’s parents and the school district, the IEP document was nonetheless prepared and submitted to the Carters for signature.8 Implementation of the plan commenced on May 2, 1985, and by its clear language was to run until June 1986.9 Shannon’s ultimate placement under this IEP was three periods of L.D. itinerant classes per week. A period in district four is a little less than one hour.
9. Several long range goals were written into the IEP. The reading goal specified four months of progress during the year, from a 5.4 level to a 5.8 level. The mathematics goal was similarly one for four month’s progress, from a 6.4 level to a 6.8 level. In light of the expert testimony of Ms. Mitchell, Dr. Grant and Dr. Ward, it is clear that these goals were wholly inadequate. Much more progress was necessary in order to provide Shannon an appropriate education. Shannon was in need of an intensive self-contained L.D. environment to afford her with an individualized special education to meet her unique needs.
10. Shannon’s parents were ultimately dissatisfied with the district’s plan and sought a due process hearing pursuant to 34 CFR § 300.506(a). The hearing was held on August 20, 1985, in Timmonsville, South Carolina. In a decision dated September 5, 1985, the hearing officer held for the district. The Carters appealed this decision to the state level. The hearing officer’s decision was upheld on October 14, 1985.
11. In response to their dissatisfaction with the IEP Shannon’s parents began a search for an appropriate placement for Shannon. Ultimately, they selected Trident Academy in Mt. Pleasant, South Carolina. Trident Academy is a private day school specializing in the education of learning disabled children. Shannon began courses at Trident in September 1985, and remained there until her graduation in the spring of 1988.
13. In light of conflicting testimony as to the appropriateness of Trident, the court’s appointed expert, Dr. Richard Nagle, tested Shannon in the spring of 1988. His tests revealed that Shannon made significant progress at Trident. Her reading comprehension had risen from a level of 4.7 in 1985 to 7.8 in 1988. Based on these tests, Dr. Nagle concluded that Trident had done a good job of meeting the individualized needs of Shannon. The court agrees with Dr. Nagles’ assessment as well as the assessments of Dr. Ritter, Dr. Ward, Dr. Grant and Ms. Mitchell that the education offered at Trident Academy has been an appropriate special education for Shannon.
15. The court finds that the tuition and fees charged by Trident were reasonable for the services it provided. The court is persuaded by the fact that Trident offers these services on the open market and that the economics of supply and demand are in operation. Further, the defendant has offered no evidence that Shannon could have received the special educational services provided by Trident at any facility closer to her home or at a lower cost.11 Therefore, the Carters are entitled to reimbursement for tuition and fees in the amount of $23,615.70, for the three school years that Shannon attended Trident.
16. For the 1985-86 and 1986-87 school years, Shannon boarded with a Mt. Pleasant resident, Ms. Mary Ann Griffin. During the 1987-88 school year, Shannon roomed in a private apartment with her older brother in the Charleston area. Since Timmonsville and Trident Academy are a considerable distance from one another, it was reasonable for Shannon to board somewhere in the area of Trident. The room and board expense of $9,069.69 was a logical and legitimate consequence of Shannon’s attending a school away from her home. We find that said amount is a reasonable additional expense necessitated by Shannon attending an out of town school.
17. During the 1985-86 and 1986-87 school years Shannon traveled approximately 20 miles per week to attend Trident Academy at a cost each year of $158.40. This expense increased to 52 miles per day and a total of $2059.20 during the school year 1987-88. The court finds these mileage expenses for Shannon’s transportation to and from school to be reasonable and recoverable.12 Therefore, the Carters are entitled to a total of $2376.00 for Shannon’s transportation to and from school.
18. The four trips home each year were a reasonable and necessary expense and shall be allowed. Therefore, the Carters are entitled to reimbursement for those expenses in the amount of $654.72.
19. It is impossible to compute with accuracy the amount of prejudgment interest that the plaintiff is entitled to recover in this action. Her father borrowed some of the money spent for education related expenses from the Pee bee State Bank, Timmonsville, South Carolina. The record reflects that the interest paid or obligated to be paid on that sum through December 31, 1989 is $6519.04.13 The amount of plaintiff’s expenses paid from funds other than those borrowed from Pee Dee State Bank cannot be determined, and no interest can be awarded therefor. Likewise, the record is silent as to any interest paid by plaintiff’s father after December 31, 1989. From that day through December 31, 1990, all sums required herein to be paid by the defendant to the plaintiff shall accrue interest at the rate of 7.91% per annum14 During the month of January 1991, the prejudgment interest rate shall be 6.62% per annum which is the yield of Fifty-two Week Treasury Bills during that month.
A. Jurisdiction is conferred upon the court by 28 U.S.C. § 1331 and 28 U.S.C. § 1343(3) and (4).
B. The Education of the Handicapped Act, 20 U.S.C § 1401 et seq. (1976 ed. and Supp. IV), provides federal money to assist state and local agencies in educating handicapped children, and conditions such funding upon a state’s compliance with extensive goals and procedures. The EHA represents an ambitious federal effort to promote the education of handicapped children and was passed in response to Congress’s perception that a majority of handicapped children in the United States “were either totally excluded from schools or [were] sitting idly in regular classrooms awaiting the time when they were old enough to ‘drop out.’” H. R. Rep. No. 94-332, p.2 (1975).
(A) a statement of the present levels of educational performance of such child, (B) a statement of annual goals, including short-term instructional objectives, (C) a statement of the specific educational services to be provided to such child, and the extent to which such child will be able to participate in regular educational programs, (D) the projected date for initiation and anticipated duration of such services, and (E) appropriate objective criteria and evaluation procedures and schedules for determining, on at least an annual basis, whether instructional objectives are being achieved. § 1401(19).
D. In the leading case on the subject, Hendrick Hudson District Board of Education v. Rowley, 458 U.S. 176 (1981), the Supreme Court has held that the requirement that the state provide a handicapped child with a “free appropriate public education” is met by providing personalized instruction with sufficient support services to permit the child to benefit educationally from that instruction. Such instruction and services must be provided at public expense, must meet the state’s educational standards, must approximate the grade levels used in the state’s regular education, and must comport with the child’s IEP. In addition, the IEP, and, therefore, the personalized instruction should be formulated in accordance with the requirements of the EHA and, if the child is being educated in the regular classrooms of the public school system, should be reasonably calculated to enable the child to achieve passing marks and advance from grade to grade. Rowley, at 204-205.
E. The EHA permits “[a]ny party aggrieved by the findings and decision” of the state administrative hearings “to bring a civil action” in “any State court of competent jurisdiction or in a district court of the United States without regard to the amount in controversy.” § 1415(e)(2). When a suit is brought under the EHA, the court’s inquiry is twofold. First, has the State complied with the procedures set forth in the EHA? And second, is the individualized educational program developed through the EHA’s procedures reasonably calculated to enable the child to receive educational benefits?
F. In the present case the plaintiff contends that the school district’s behavior was afflicted with “serious procedural infirmity” regarding compliance with the elaborate procedural requirements of the EHA, thereby invalidating the May 1, 1985, IEP. Such compliance is critical to the efficient operation of the EHA, and serious procedural noncompliance can by itself support a finding that the child has not been provided with a FAPE. Hudson by and through Tyree v. Wilson, 828 F.2d 1059 (4th Cir. 1987), citing, Hall v. Vance County Board of Education, 774 F.2d 629, 635 (4th Cir. 1985). The Carters contend that they were confronted at the May 1, 1985, IEP meeting with a completed IEP as if it were a fait accompli, with no opportunity to contribute to the content or development of the document.
G. It has been held “appropriate for agency staff to come prepared with evaluation findings, statements of present levels of educational performance, and a recommendation regarding annual goals, short term instructional objectives, and the kind of special education and related services to be provided. However, the agency must make it clear to the parents at the outset of the meeting that the services proposed by the agency are only recommendations for review and discussion with the parents.” B.G. by F.G. v. Cranford Board of Education, 702 F.Supp. 1158, 1165 (D.N.J. 1988). While there is considerable dispute as to what actually took place at the meeting, the court concludes that there has been no serious procedural non-compliance on the part of the school district.
H. Turning now to the court’s second inquiry, it should be noted that there exists no single substantive standard describing exactly how much educational benefit is sufficient to satisfy the EHA. Each case must be determined on a case-by-case basis, Hall v. Vance County Board of Education, 774 F.2d 629, 635 (4th Cir. 1985), but, as the Rowley decision makes clear, “[w]hen the handicapped child is being educated in the regular classrooms of a public school system, the achievement of passing marks and advancement from grade to grade will be one important factor in determining educational benefit.” Rowley, at 207, N. 28.
I. The IEP prepared on May 1, 1985, did not provide Shannon with a free appropriate public education as required by the EHA. Even if all of the goals of the document had been met, Shannon would continue to fall behind her classmates at an alarming rate. The stated progress of only four months in her reading and math skills over an entire school year ensured the program’s inadequacy from its inception. Furthermore, the district’s offer of only three periods of itinerant study a week failed to meet Shannon’s educational needs. At a minimum, which is all the EHA requires, the district was obligated to provide Shannon an individualized program that would allow her to receive passing marks and advance from grade to grade. Having failed to do this, the court concludes that Florence County School District Four failed to provide Shannon Carter with a free appropriate public education.
J. It is now settled that parents may be entitled to tuition reimbursement despite the unilateral withdrawal of their child from the public school. Burlington School Committee v. Department of Education, 471 U.S. 359 (1985). The parents, of course, withdraw the child at the risk that the subsequent administrative and judicial review will establish that the contested placement provided a FAPE. In Burlington the court noted the cruel dilemma an arguably inappropriate placement offers the parents if they have no possibility of reimbursement for expenses resulting from an unilateral withdrawal. They may either leave the child in an inappropriate classroom pending the long process of review or remove the child and waive any right to tuition reimbursement for alternative schooling while review is taking place. The court accordingly held that a prior unilateral withdrawal did not waive the child’s right to such an equitable remedy at the end of the review process. Burlington, at 370.
K. There appears no doubt, then, that the Carters were entitled to withdraw their child from the public school because of its failure to provide a FAPE. The remaining issue concerns whether or not the Carters had a right to place Shannon at Trident Academy. From the outset it should be noted that the evidence established that Trident Academy provided Shannon an excel lent education in substantial compliance with all the substantive requirements of the EHA. Although it did not comply with all of the procedures outlined in the EHA, it evaluated Shannon quarterly, not yearly as mandated in the EHA, it provided Shannon with low teacher-student ratios, and it developed a plan which allowed Shannon to receive passing marks and progress from grade to grade. The court concludes that Shannon received an appropriate education, within the meaning of the EHA, at Trident Academy.
L. The defendants, relying on the recent case of Schimmel by Schimmel v. Spillane, 819 F.2d 477 (4th Cir. 1987), argue that the plaintiff herein is not entitled to tuition reimbursement because they placed her in a school that had not been approved by the South Carolina State Department of Education. Spillane is clearly distinguishable from the present case. In Spillane, the parents and the school were in agreement that the child needed to be placed in a residential school, and the school system agreed to pay the cost of such placement at the Little Keswick School near Charlottesville, Virginia. Little Keswick is a private residential school which is approved by the Virginia Department of Education as a school for the handicapped. The parents declined to enroll their child at Little Keswick and instead enrolled him at a private school in Vermont. After the school refused to place the child at the Vermont school, the parents sought a due process hearing. At that hearing it was held that the Virginia school offered an appropriate education. This decision was upheld on appeal to the state reviewing officer. The parents then brought suit in the district court where judgment was entered for the school district. The Fourth Circuit Court of Appeals affirmed. Schimmel by Schimmel v. Spillane, 819 F.2d 477 (4th Cir. 1987). This court has no quarrel with the Schimmel decision and agrees with the same.
In the instant case the situation is quite different. We are not dealing with referral to a private school with a public agency so as to make § 1413(a)(4)(B) of the EHA and 34 CFR § 300.401(a)(3) applicable. This case involves a situation where the school district failed to provide the child with a free appropriate public education, and the only way the parents could provide the same to their daughter within a meaningful time frame was to enroll her in a private school. Nothing in the existing law or regulations convinces this court that the stringent requirements advanced by the defendants should be applied to such a unilateral placement.
M. Congress has provided that in all actions brought in this court under the EHA “the court shall receive the records of the administrative proceedings, shall hear additional evidence at the request of a party, and, basing its decision on the preponderance of the evidence, shall grant such relief as the court deems is appropriate.” 20 U.S.C. § 1415(e)(2)(c). The United States Supreme Court has said that “equitable considerations are relevant” in determining the appropriate relief mandated by the statute. This court, therefore, concludes that the plaintiff should be entitled to recover all reasonable expenses incurred by her parents in enrolling her in Trident Academy for the last three years of her high school education. These expenses should, of course, include prejudgment interest.
The plaintiff shall, therefore, have judgment against the defendant in the amount of $35,716.11 plus prejudgment interest computed in the manner herein specified15 and costs.
1 While Shannon had received passing marks in the first grade at Timmonsville Elementary School her mother decided it would be in Shannon’s best interest to repeat it at Roy Hudgens Academy.
2 The actual test results were not available at trial. Hill stated that after a thorough search of his files he was unable to locate Shannon’s 1983 test results. This is most unfortunate because all of the experts who testified on the subject were in agreement that, absent a traumatic injury to the head, a learning disability does not naturally develop in a child who previously did not suffer from one.
3 Shannon’s first semester averages were as follows: English 9--D; Pre-Algebra--F; Physical Science--D; Civics and Geography--F; and Physical Education--F.
4 Report of Dr. Bailey, plaintiff’s exhibit 3.
5 The verbal I.Q. was 81 and the performance I.Q. was 117.
6 Ms. Summer is a psychiatric social worker from Florence, South Carolina. She was employed by the plaintiff’s parents and first examined the plaintiff on February 19, 1985. She testified in the administrative phase of this proceeding and was present during the trial in this court but did not testify.
7 Pursuant to 20 U.S.C. § 1412 the South Carolina State Board of Education adopted Regulations 43-243 which at page 144 explained the difference between an itinerant program and a resource class as follows: In an itinerant program the specialist will provide continual ongoing services to handicapped pupils and teachers taught in regular classroom settings. A resource room program shall involve pupils who will remain in regular classrooms for the major part of the day and will be scheduled into the resource room for one or more periods of individualized instruction contingent on pupil’s needs.
8 There was considerable dispute during the trial as to whether the parents signed the document knowingly and voluntarily. This issue is of no import; clearly the Carters were not satisfied with the plan. The IEP is not a binding contract and either party can seek changes at any time. The fact that their signature appears on the document does not effect the outcome of this case to any extent whatsoever.
9 The school district would have this court believe that the IEP was only intended for the remainder of the school year. However, the IEP states in clear and unambiguous language that it was to run until June 1986.
10 Although Dr. Black testified in the abstract about possible disapproval of the school because it employs one non-certified teacher, this is not fatal to the plaintiff’s case.
11 There was some suggestion that Shannon could have received special educational services at Florence School District One. However, there was no evidence that the defendant ever presented this as an option. The parents were, of course, prevented by state law from independently seeking a placement for Shannon in any public school outside of her district.
12 The mileage expenses for the school year 1987-88 are substantially greater than those for the preceding two years. In finding the same to be reasonable we have also considered the fact that room and board expenses for the same period were correspondingly lower than those for the preceding two years.
13 Plaintiff’s exhibit 36 reflects the amount of interest paid by the Carters through December 31, 1988. Plaintiff’s exhibit 37 indicates the per diem interest on the loan. Since the loans were made from January 1 through December 31 of each year, interest was computed at the per diem rate through December 31, 1989.
14 Fifty-two Week Treasury Bills are sold by auction on a monthly basis. Records of those sales are maintained in the office of the clerk of this court, and the 7.91% rate is an average of the monthly yields of said bills during 1990 as computed from those records.
15 Prejudgment interest shall also be computed in accordance with 28 U.S.C. § 1961(b).


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For the full list of publications, visit my CV Lattes (in Portuguese).
Other sources: Google Scholar Profile, DBLP.
SANTANA, E. F. Z.; CHAVES, A. P.; GEROSA, M. A., KON, F.; MILOJICIC, D.. Software Platforms for Smart Cities: Concepts, Requirements, Challenges, and a Unified Reference Architecture. ACM Computing Surveys (CSUR), v. 50(6), 2018.
STEINMACHER, I.; CHAVES, A. P.; GEROSA, M. A.. Awareness Support in Distributed Software Development: A Systematic Review and Mapping of the Literature. Computer Supported Cooperative Work, v. 22, p. 113-158, 2013.
LEAL, G. C. L.; CHAVES, A. P.; HUZITA, E. H. M.; DELAMARO, M. E.. An Integrated Approach of Software Development and Test Processes to Distributed Teams. Journal of Universal Computer Science (Print), v. 18, p. 2686, 2012.
CHAVES, A. P.; STEINMACHER, I.; HUZITA, E. H. M.; LEAL, G. C. L.; BIASAO, A.B.. OntoDiSENv1: an Ontology to Support Global Software Development. CLEI Electronic Journal, v. 14, p. 1-12, 2011.
WESSEL, M.; DE SOUZA, B. M.; STEINMACHER, I., WIESE, I. S.; POLATO, I.; CHAVES, A. P.; GEROSA, M. A.. The Power of Bots: Characterizing and Understanding Bots in Open Source Software Projects. In: ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2018), New York, USA. 2018.
CHAVES, A. P.; GEROSA, M. A.. Single or Multiple Conversational Agents? An Interactional Coherence Comparison. In: ACM CHI Conference on Human Factors in Computing Systems, Montréal, 2018.
CHAVES, A. P.; STEINMACHER, I.; GEROSA, M. A.. Por onde quero ir? Interesses pessoais na escolha de um trajeto urbano. In: XIII Simpósio Brasileiro de Sistemas Colaborativos, Porto Alegre, 2016.
PRUDENCIO, M. M. V.; CHAVES, A. P.. Análise da Influência de Grupos de Inclusão de Mulheres em Comunidades de Software Livre. In: CSBC WIT - Women in Information Technology, Porto Alegre, 2016.
CHAVES, A. P.; GEROSA, M. A.. Using Collective Intelligence to Improve Urban Mobility in Smart Cities. In: CSCW 2015 Workshop: Doing CSCW Research in Latin American: differences, opportunities, challenges, and lessons learned, Vancouver, 2015. p. 1-4.
BATISTA, P.; RIBEIRO, L.; COGO, F. R.; CHAVES, A. P.. Um Survey sobre Soluções de Computacação Urbana Como Meio de Promoção da Socialização Face-a-Face. In: Simpósio Brasileiro de Sistemas Colaborativos, Salvador, 2015.
RAMOS, F. V.; CHAVES, A. P.; GEROSA, M. A.; STEINMACHER, I.. Um estudo exploratório sobre contribuições casuais em projetos de software livre: caso do projeto LibreOffice. In: IX Workshop em Desenvolvimento Distribuído de Software, Ecossistemas de Software e Sistemas-de-Sistemas, Belo Horizonte, 2015.
PRUDENCIO, M. M. V; COGO, F. R.; CHAVES, A. P.; STEINMACHER, I.. Participação de Mulheres em Projetos de Software Livre: Caso da Comunidade Brasileira da Mozilla Foundation. In: XVI Workshop Internacional de Software Livre (WSL 2015), Porto Alegre, 2015.
STEINMACHER, I.; CHAVES, A. P.; CONTE, T. U.; GEROSA, M. A.. Preliminary Empirical Identification of Barriers Faced by Newcomers to Open Source Software Projects. In: 2014 Brazilian Symposium on Software Engineering (SBES), p. 51-60, Maceio, 2014.
ANTONIO, M. F.; COGO, F. R.; STEINMACHER, I.; CHAVES, A. P.. FIltragem Colaborativa de Rotas de Ônibus Usando Dados sobre a Acessibilidade das Vias Urbanas. In: Simpósio Brasileiro de Sistemas Colaborativos, 2014, Curitiba, 2014.
STEINMACHER, I.; WIESE, I. S.; CHAVES, A. P.; GEROSA, M. A.. Why do newcomers abandon open source software projects?. In: 2013 6th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE), p. 25, San Francisco, 2013.
STEINMACHER, I.; SILVA, M. A. G.; COGO, F. R.; CHAVES, A. P.; GEROSA, M. A. . Tópicos de pesquisa e rede de coautoria no Simpósio Brasileiro de Sistemas Colaborativos. In: Simpósio Brasileiro de Sistemas Colaborativos, Manaus, 2013.
VIEIRA, V.; FERRAZ, C.; TEDESCO, P.; TIMES, V. C; SALGADO, A. C.; HUZITA, E. H. M.; CHAVES, A. P.; STEINMACHER, I.. The UbiBus Project: Using Context and Ubiquitous Computing to build Advanced Public Transportation Systems to Support Bus Passengers. In: VIII Simpósio Brasileiro de Sistemas de Informação (SBSI 2012), São Paulo, 2012.
STEINMACHER, I.; WIESE, I. S.; CHAVES, A. P.; GEROSA, M. A.. Newcomers Withdrawal in Open Source Software Projects: Analysis of Hadoop Common Project. In: 2012 Brazilian Symposium on Collaborative Systems (SBSC), 2012, Sao Paulo, 2012.
NISHI, MARCELO KOTI; CHAVES, A. P.; STEINMACHER, I.. A Collective Intelligence Based System for Visualizing Problems in Public Roads. In: 2012 Brazilian Symposium on Collaborative Systems (SBSC), p. 94-98, São Paulo, 2012.
CHAVES, A. P.; STEINMACHER, I.; VIEIRA, V.. Social networks and collective intelligence applied to public transportation systems: A survey. In: Simpósio Brasileiro de Sistemas Colaborativos, 2011, Parati - RJ, 2011.
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VIVIAN, R. L; HUZITA, E. H. M.; LEAL, G. C. L.; CHAVES, A. P.. Context-Awareness on Software Artifacts in Distributed Software Development: A Systematic Review. In: CRIWG Conference on Collaboration and Technology, 2011, Parati - RJ. Lecture Notes in Computer Science. v. 6969. p. 30-44. Berlin/Heidelberg: Springer, 2011.

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Matthew is a partner whose practice has focused on First Nations litigation. He is one of the leading litigators in the field of aboriginal rights, aboriginal title, fiduciary duties, reserve lands issues and governance matters. His cases that include major commercial fishing rights litigation, recent court challenges for First Nations to the Trans Mountain Pipeline project, and active treaty rights infringement litigation for a Treaty 8 First Nation. He has appeared in the Supreme Court of Canada and before all levels of court in British Columbia, the Federal Courts and several administrative tribunals on a wide range of cases for indigenous clients.
Matthew’s litigation work for First Nations includes high-level appellate work, judicial reviews, trials and administrative proceedings. Among his trial work are three major trials that each have exceeded 120 days. He was lead counsel or co-lead counsel of two of those.
Matthew served as a Law Clerk in the British Columbia Supreme Court (1997-1998). He has written several articles on aboriginal rights and title issues for legal education conferences. He has presented papers on these subjects at conferences sponsored by the Continuing Legal Education Society and Pacific Business and the Law. Matthew has served on the board of directors of the Georgia Strait Alliance, a citizens group aimed at protecting the marine environment around the Strait of Georgia, and the Gambier Island Conservancy. Matthew lives in Vancouver with his wife Lisa Osoba and their children Ella and Charlie.
Squamish Nation v. Minister of Environment and Kinder Morgan Canada (BC Supreme Court, decision pending) – challenge by the Squamish Nation to the Provincial government’s approval of the Trans Mountain Pipeline project.
Coldwater Indian Band v. Canada and Kinder Morgan Federal Court of Appeal, decision pending) – challenge by the Coldwater Indian Band to the Government of Canada’s approval of the Trans Mountain Pipeline project.
Squamish Nation v. Canada and Kinder Morgan (Federal Court of Appeal, decision pending) – challenge by the Squamish Nation to the Government of Canada’s approval of the Trans Mountain Pipeline project.
Ahousaht Indian Band and Nation v. Canada (2018, BC Supreme Court) – Crown efforts to justify an infringement of commercial fishing rights).
Blueberry River First Nation v. British Columbia (2018, BC Supreme Court) – Secured a stay of First Nation’s obligation to pay court hearing fees for a treaty rights trial.
Fort Nelson First Nation v. Oil and Gas Commission (2017, BC Supreme Court) – set aside an approval for a gas pipeline for Province’s failure to consult properly with Treaty 8 First Nation.
Coldwater Indian Band v. Minister of Indian Affairs and Kinder Morgan Canada (2017, Federal Court of Appeal) – Breach of fiduciary duty by Canada for failing to protect Coldwater Band’s best interest in assignment of the Trans Mountain Pipeline easement to Kinder Morgan Canada.
Tsilhqot’in Nation v. British Columbia (2014, Supreme Court of Canada) – Intervenor in historic land claims case establishing Aboriginal title to a portion of the Chilcotin region of British Columbia.
Ahosuaht First Nation v. Canada (Fisheries and Oceans) (2014, Federal Court) – secured an injunction to stop a commercial herring fishery on the West Coast of Vancouver Island that threatened the restoration of the stocks and was thus opposed by the local First Nations.
Ehattesaht First Nation v. British Columbia, (2014, BC Supreme Court) – Breach of the duty to consult by British Columbia in reallocating undercut in a Tree Farm Licence area in Ehattesaht territory.
Ahousaht Indian Band and Nation v. Canada (Attorney General) (2009, BC Court of Appeal) - Aboriginal commercial fishing rights.
Lax Kw'alaams Indian Band v. Canada (Attorney General) (2011, Supreme Court of Canada) – Aboriginal commercial fishing rights and asserted fishing rights associated with a reserve.
R. v. Kapp, (2008, Supreme Court of Canada) – Intervenor in case concerning challenge to Aboriginal commercial fishing programs.
Salt Spring Aquafarms v. Salt Spring Harbour Authority (2006, Federal Court of Appeal) – successfully defended the Harbour Authority’s right to prohibit the sale of farmed salmon from its docks.
Grismer v. Squamish Nation (2006 Federal Court) – defended the right of the Squamish Nation to determine its own customary laws for membership in the face of a Charter challenge to their right to do so.
Clifton v. Hartley Bay Indian Band (2005 Federal Court) – rights of off-reserve members to vote in customary band election.
Squamish Indian Band v. Canadian Pacific (2002, BC Court of Appeal) – secured the return of 10 acers of former reserve land in the Kitsilano area of Vancouver from the CPR for Squamish.
Osoyoos Indian Band v. Oliver (Town) (2001, Supreme Court of Canada) – Intervenor in decision finding that the band could levy tax on an irrigation channel in its reserve.
Mathias v. Canada (2000 Federal Court) – Entitlement to former reserve land in Kitsilano area of Vancouver and breach of fiduciary duty against Canada for disposing of the lands.

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1(A). Define Artificial Intelligence (AI).
The study of how to make computers do things at which at the moment, people are better.
1(B). Define Artificial Intelligence formulated by Haugeland.
The exciting new effort to make computers think machines with minds in the full and literal sense.
1(C). Define Artificial Intelligence in terms of human performance.
The art of creating machines that performs functions that require intelligence when performed by people.
1(D). Define Artificial Intelligence in terms of rational acting.
1(E). Define Artificial in terms of rational thinking.
of the computations that make it possible to perceive, reason and act-Winston.
2. What is meant by Turing test?
questioner, will be asking questions to one person and one machine. Three of them will be in a separate room.
Interrogator knows them just as A and B. so it has to identify which is the person and machine.
3. What is called materialism?
4. What are the capabilities, computer should posses to pass Turing test?
5. Define Total Turing Test?
6. What are the capabilities computers needs to pass total Turing test?
through sensors and acting upon that environment through actuators.
9. Define an Omniscient agent.
10. What are the factors that a rational agent should depend on at any given time?
1. The performance measure that defines degree of success.
3. When the agent knows about the environment.
4. The action that the agent can perform.
12. List the various type of agent program.
 Simple reflex agent program.
 Agent that keep track of the world.
 Goal based agent program.
13. Give the structure of agent in an environment?
Agent interacts with environment through sensors and actuators.
20. Define Agent Program. For each possible percept sequence. Define performance measures. Define Information Gathering. 18. (i. 24. An Omniscience agent knows the actual outcome of its actions and can act accordingly. corrections. Define Percept Sequence. 19. . Define Ideal Rational Agent. 15. They are How and When actions. Rational agent is one that does the right thing.) right actions will cause the agent to be most successful in the environment. It is a mathematical description which deals with the agent’s behavior that maps the given percept sequence into an action.Actuators S – Sensors Example Interactive English tutor performance measure maximize student’s score on test. Performance measure embodies the criterion for success of an agent’s behavior. 21. 22. Agent function for an agent will be implemented by agent program. 16. Environment Set of students testing Agency Actuators Display exercises suggestions. Doing actions in order to modify future percepts sometimes called information gathering.Environment A. Define Omniscience. How to measure the performance of an agent? Performance measure of an agent is got by analyzing two tasks.Performance measure E . Define Agent Function. What is important for task environment? PEAS → P. How agent should act? Agent should act as a rational agent.e. 23. given the evidence provided by the percept sequence and whatever built in knowledge the agent has. a rational agent should select an action that is expected to maximize its performance measure. It should learn what it can do to compensate for partial (or) in correct prior knowledge. An agent’s choice of action at any given instant can depend on the entire percept sequence observed to elate. 17. What is autonomy? A rational agent should be autonomous. 14.
Contingency problem. Competitive Multi agent b. List the properties of environments. They are i. 30. Operator vi. 32. i. state space vii. What are the different types of problem? Single state problem. What is the structure of intelligent Agent? Intelligent Agent = Architecture + Agent Program 29. What are the components of a problem? There are four components. Define problem solving agent. path . Solution v. 28. Path cost v. What is environment program? It defines the relationship between agents and environments. Execution phase 31. Successor function iii. multiple state problems. Problem solving agent is one kind of goal based agent.Co – operative Multi agent 27. EG – It selects the environment from environment class in which the agent has to Run. Goal formulation ii. List the steps involved in simple problem solving technique. 26. where the agent Should select one action from sequence of actions which lead to desirable states. Problem formulation iii. Search iv. o Fully Observable Vs Partially Observable o Deterministic Vs Stochastic o Episodic Vs Sequential o Static Vs Dynamic o Discrete Vs Continuous o Single Agent Vs Multi agent a. initial state ii. What is Environment Class (EC) and Environment Generator (EG)? EC – It is defined as a group of environment. Sensors Keyboard entry 25. Exploration problem. Goal test iv.
35. Define search node. Completeness ii. 40. i. Traveling salesman problem. this collection is called fringe or frontier. VLSI Layout problem v. iv. List the performance measures of search strategies. Optimality iii. iii. Automatic Assembly vii. 34. The collection of nodes that have been generated but not yet expanded. 41. Time complexity iv. 39. Robot navigation vi. Real world problem examples: i. The number of nodes which is connected to each of the node in search tree is called Branching factor. 8 – Queen problem 8 – Puzzle problem 38. which is the sum of the cost of the each action along the path. Define branching factor (b). Touring problem. ii. Define search tree. The root of the search tree that is the initial state of the problem is called search node. Define fringe. . A function that assigns a numeric cost to each path. Define State Space. Toy problems ii. Real world problems 37. Give example problems for Artificial Intelligence. i. The set of all possible states reachable from the initial state by any sequence of action is called state space. 43. Define Path. Airline travel problem. Differentiate Blind Search and Heuristic Search. Give example for real world end toy problems. Internet searching Toy problem Examples: Vacuum world problem. The tree which is constructed for the search process over the state space is called search tree. 33. Define Path Cost. A path in the state space is a sequence of state connected by sequence of actions. Space complexity 42. 36.
3. What is informed search? One that uses problem – specific knowledge beyond the definition of the problem itself and it can find solutions more efficiently than an uninformed strategy. partially expanded node remembers which successor generate next is called Backtracking search. 2.e. Define depth limited search. Mention the criteria for the evaluation of search strategy. path caused to move ii) Problem is solved with the information we which we know. UNIT – II 1. Blind search Heuristic search i) No information about the path cost i) We have some information like minimum from the current state to goal state. Different varieties of queuing fn produce different varieties of the search algorithm. ii) Problem can be solved by the information which is already given. space complexity. This is called Depth Limited search. What is the use of QUEUING_FN? QUEUING_FN inserts asset of elements into the queue. 47. The problem of unbounded tress can be avoided by supplying depth limit 1(i. time complexity. 45. optimality . Define Uniform cost search. There are 4 criteria: Completeness. The variant of depth first search called backtracking search. Only one successor is generated at a time rather than all successor. Define Depth first search.) nodes at depth 1 are treated as if they have no successors. 46. iii) Example a) Breadth first search iii) Example b) Uniform cost search a) Best first search c) Depth first Search b) Greedy search d) Depth limited search c) A* search e) Iterative deepening search f) Bi – Directional Search 44. It expands the deepest node in the current fringe of the search tree. Uniform cost search expands the node ‘n’ with the lowest path cost instead of expanding the shallowest node. Define Backtracking search.
because evaluation measures distance to the goal. h (n). f(n). Define Evaluation function. A* search evaluates nodes by combining g(n). BFS b. a. the cost to reach the node and h(n). What is Best First Search? Best First Search is an instance of the general TREE SEARCH or GRAPH SEARCH algorithm in which a node is selected for expansion based on an evaluation function.4. DFS d. Define Admissible heuristic h (n). if it is optimal then. Best first search –greedy search .e. 10. Iterative deepening search f. List the various informed search strategy. What is triangle inequality? It states that each side of a triangle cannot be longer than the sum of the other two slides of the triangle. Memory bounded A*(MA*) 14. Define Heuristic function. . It is done by using the heuristic function: f(n) = h(n). A node with the lowest evaluation is selected for expansion.A* search Memory bounded search-Iterative deepening A*search -simplified memory bounded A*search -Iterative improvement search –hill climbing -simulated annealing 6. 13. It expands the node that is closest to the goal (i. 8. Define A* search. What are the 2 types of memory bounded heuristic algorithms? i. f(n) = g(n) + h(n) 11. 7. Recursive Best First Search(RBFS) ii. Depth limited search e. Uniform cost search c. List the various search strategies. Differentiate BFS & DFS. f(n). 12. Bidirectional search 5. 9. In A* search. the cost to get from the node to the goal. h(n) is an admissible heuristic which means h(n) never overestimates the cost to reach the goal. Define Greedy Best First Search.) to reach solution in a quicker way. h (n) is defined as the estimated cost of the cheapest path from node n to a goal node.
BFS means breath wise search. Space complexity is less Gives optimal solution Queuing fn is somewhat different from queue operator. Iterative deepening is a strategy that sidesteps the issue of choosing the best depth limit by trying all possible depth limits: first depth 0. h2? The sum of the horizontal and vertical distances of the tiles from their goal positions in a 15 puzzle problem is called Manhattan distance (or) city block distance.then depth 2& so on. Define iterative deepening search. So dfs will never be able to recover from an unlucky choice at one of the nodes near the top of the tree. 24. Uniform tree of depth d would have to be in order to contain N+1 nodes is called branching factor. d-depth of tree . 20. So DFS should be avoided for search trees with large or infinite maximum depths 23. Time complexity =O (bd) . RBFS remembers the f-value of the best leaf in the forgotten sub tree and therefore decide whether its worth re expanding the sub tree sometimes later. What is RBFS? It keeps track of the f-value of the best alternative path available from any ancestor of the current node. then depth 1. What is metalevel state space? Each state in a metalevel state space captures the internal state of a program that is searching in an object level state space. 17. Memory bounded A*(MA*) ii. What is SMA* search? SMA* expands the best leaf until memory is full and it drops the oldest worst leaf node and expands the newest best leaf node. What is called as bidirectional search? The idea behind bidirectional search is to simultaneously search both forward from the initial state & backward from the goal & stop when the two searches meet in the middle. The drawback of DFS is that it can get stuck going down the wrong path. Space complexity is more. Define Branching factor b*. Give the drawback of DFS. What is Manhattan distance. b-branching factor. What are the 2 ways to use all available memory? i. Do not give optimal solution Queuing fn is same as that of queue operator DFS means depth wise search. Simplified Memory bounded A*(SMA*) 18. Many problems have very deep or even infinite search tree. 15. Write the time & space complexity associated with depth limited search. 19. 21. 22. 16.
If elevation corresponds to an objective function.) up hill and terminates when it reaches a “peak” where no neighbor has a higher value. If elevation corresponds to cost. What is a pattern database? This database is the storage of exact solution costs for every possible sub problem instance. 26. then the aim is to find the highest peak is called global maximum. Location defined by the state. Local maxima: A local maxima as opposed to a goal maximum is a peak that is lower that the highest peak in the state space. What is local search? It operates using a single current state rather than multiple paths and generally moves only to neighbors of that state. Define Global Maximum. Once a local maxima is reached the algorithm will halt even though the solution may be far from satisfactory. 35. 30. . 29. 27. Define Global minimum. What is a disjoint pattern database? The sum of the two costs is still a lower bound on the cost of solving the entire problem is called a disjoint pattern database. What is the meaning for greedy local search? It goals (picks) a good neighbor state without thinking ahead about where to go next. It is a loop that continually moves in a increasing value direction (i. Space complexity=o (bl) 25. 36. Define Local maxima. 33. 28. 31. Define Hill Climbing search. then the aim is to find the lowest valley is called global minimum. ii. Elevation defined by the value of the heuristic cost function (or) objective function. What are the 2 parts of Landscape? i. Define Optimization Problems. The aim of this problem is to find the best state according to an objective function.e. Plateaux: A plateaux is an area of the state space where the evaluation fn is essentially flat. List some drawbacks of hill climbing process. 32. The search will conduct a random walk. What is Released problems? A problem with fewer restrictions on the actions is called a relaxed problem. 34.
Stochastic beam search 38. Thus each iteration expands all nodes inside the contour for the current f-cost. If the move improves the situation. What is Genetic Algorithms? Genetic Algorithm is a variant of stochastic beam search in which successor states are generated by combining two parent states. Define Online Search agent. Define annealing. but lower than the global maximum. instead of picking the best move. What is the advantage of memory bounded search techniques? We can reduce space requirements of A* with memory bounded algorithm such as IDA* & SMA*. Give the procedure of IDA* search. Iterative improvement algorithms keep only a single state in memory. What are the variants of hill climbing? i. * It is optimal if enough memory is available to store the shallowest optimal solution path. 42. The depth first search is modified to use an f-cost limit rather than a depth limit. This algorithm. but can get stuck on local maxima. Annealing is the process used to harden metals (or) glass by heating them to a high temperature and then gradually cooling them. optimal. Its space complexity is still prohibitive. the search is optimally efficient. 43. 37. it picks a random move. A local maximum is a peak that is higher than each of its neighboring states. *Hill climbing. *Simulated annealing. Otherwise it returns the best solution that can be reached with the available memory. First choice hill climbing iii. 44. Local beam search v. . In this algorithm each iteration is a dfs just as in regular iterative deepening. Minimize f(n)=g(n)+h(n) combines the advantage of uniform cost search + greedy search A* is complete. * It avoids repeated states as for as its memory allow. Simulated annealing search iv. 39. List some properties of SMA* search. Agent operates by interleaving computation and action (i. and then it observes the environment and computes the next action. it is always accepted.) first it takes an action. 40. * It will utilize whatever memory is made available to it. Define simulated annealing. Stochastic hill climbing ii.e. * It is complete if the available memory is sufficient to store the shallowest path. rather than by modifying a single state. 41. *When enough memory is available for entire search tree. thus allowing the material to coalesce into a low energy crystalline state.
Constraint Satisfaction problem (CSP) is defined by a set of variables X1. It is the general term for propagating (i. Preference constraints.e. Define Alpha beta pruning. Define LCV. Define MRV. 49. 51. 52. Define CSP. A value can be assigned to any unassigned variable. c. Goal TEST(s) 46. Actions(s) b. provided that does not conflict with previously assigned variables.X2. a.…Cm. a. Define Cycle cut set. 50. This process is called tree decomposition. b. 53. d. Define Successor function.) spreading the implications of constraints on the variable on to other variable. s’) c. Unary constraints relates one variable. Step cost function C(s. The constraint graph is divided into a set of connected sub problems.…Xn and set of constraints C1. Each sub problem is solved independently and the resulting solutions are then combined. S is called a cycle cut set. Define constraint propagation. Least constraining value heuristic prefers the value that rules out the fewest choices for the neighboring variables in the constraint graph. 55. Define Tree decomposition. Absolute constraints. 47. e. Minimum remaining values heuristic chooses the variable with the fewest “legal” values. Alpha beta pruning eliminates away branches that cannot possibly influence the final decision . Define Conflict directed back jumping. What are the things that agent knows in online search problems? a.C2. The process of choosing a subset S from variables [CSP] such that the constraint graph becomes a tree after removal of S. Higher order constraints relate more than two variables. What are the types of constraints? There are 5 types. 48. A back jumping algorithm that uses conflict sets defined in this way is called Conflict directed back jumping. 54.45. A binary constraint relates two variables.
sometimes these objects are referred as domain elements. It is an expressive. The difference between propositional and first order logic is in the ontological commitment. 8. Define Epistemological commitment. 3. Define FOL. It assumes about the nature of reality.An inference procedure ‘i’ can be described by the sentences that it can derive. 5. Richard. Define Syntax? Syntax is the arrangement of words. Define a knowledge Base: Knowledge base is the central component of knowledge base agent and it is described as a set of representations of facts about the world. The logic that allows the possible states of knowledge with respect to each fact. 9. 7. Syntax of the language describes how to make sentences. Define domain and domain elements. show objects. Syntax of a knowledge describes the possible configurations that can constitute sentences. What are the three levels in describing knowledge based agent?  Logical level  Implementation level  Knowledge level or epistemological level 10. KB --Alpha is derived from KB or i derives alpha from KB. With an example. 6. England. Define an inference procedure An inference procedure reports whether or not a sentence is entiled by knowledge base provided a knowledge base and a sentence . If i can derive from knowledge base. properties functions and relations. King Functions : BROTHER OF 4. 1200 Relation : Ruled Properties : Evil.) Boolean Logic. It is a representational language of knowledge which is powerful than propositional logic (i. 2. The sentences are expressed in a language called as knowledge representation language. compositional language. Example “EVIL KING JOHN BROTHER OF RICHARD RULED ENGLAND IN 1200” Objects : John. Define Semantics . 11. declarative. The set of objects is called domain. FOL is a first order logic. UNIT – III 1. Define Ontological commitment. Define a Sentence? Each individual representation of facts is called a sentence.e. we can write.
Proof Theory – a set of rules for deducing the entailment of a set sentences. 15. The domain of family relationship is called kinship domain which consists of objects unary predicate. Define Atomic sentence. when a particular configuration exists with in an agent. The semantics of the language defines the truth of each sentence with respect to each possible world. 17. relation. Extension to the standard syntax (i. Define synchronic and diachronic sentence. Sentences that allow reasoning “a cross time” are called diachronic sentence. Define kinship domain. Atomic sentence is formed by both objects and relations. Quantifiers are used to express properties of entire collection of objects rather than representing the objects by name. 18. A formal system for describing states of affairs. ii. Existential Quantifier iii. function. Types: i. We use 3 symbols to build a term. Define Logic Logic is one which consist of i.e. 13. binary predicate. What are the two we use to query and answer in knowledge base? ASK and TELL. What are the 3 types of symbol which is used to indicate objects. Diagnostic rules .e. consisting of a) Syntax b)Semantics. 19.) meaning is called syntactic sugar. Nested Quantifier. relations and functions? i) Constant symbols for objects ii) Predicate symbols for relations iii) Function symbols for functions 14. the agent believes the corresponding sentence. Richard) William is the brother of Richard. A term is a logical expression that refers to an object. Define Quantifier and it’s types. Universal Quantifier ii. 21. With this semantics.) procedure that does not change the semantics (i. Example Brother (William. What are the 2 types of synchronic rules? i. Sentences dealing with same time are called synchronic sentences. Define syntactic sugar. 20. Define terms. 16. 12.
What are the basic Components of propositional logic? i.Define a Proof A sequence of application of inference rules is called a proof. The existential sentence says there is some object satisfying a condition. Lifted Inference rule require finding substitutions that make different logical expressions look identical (same). relations and functions are referred to by the constant predicate. Casual rules. . we can infer its disjunction with anything. 32 .What is truth Preserving An inference algorithm that derives only entailed sentences is called sound or truth preserving . ii. 22. 27. 30. and function symbols.Define a Complete inference procedure An inference procedure is complete if it can derive all true conditions from a set of premises. 23. 35. Finding proof is exactly finding solution to search problems. Logical Constants (True. If the successor function is defined to generate all possible applications of inference rules then the search algorithms can be applied to find proofs. 28. Define true sentence A sentence is true under a particular interpretation if the state of affairs it represents is the case. 29. Define Unification. 24 .Define Validity of a sentence A sentence is valid or necessarily true if and only if it is true under all possible interpretation in all posssible world. 26.Define Modus Ponen’s rule in Propositional logic? The standard patterns of inference that can be applied to derive chains of conclusions that lead to the desired goal is said to be Modus Ponen’s rule. This is called Unification.Define Interpretation Interpretation specifies exactly which objects. a sentence. False) 31 . OR-Introduction rule states that from.Define AND –Elimination rule in propositional logic AND elimination rule states that from a given conjunction it is possible to inference any of the conjuncts. The new name is called skolem constant.Define Satistiability of a sentence A sentence is satisfiable if and only if there is some interpretation in some world for which it is true . and the instantiation process is just giving a name to that object. That name must not belong to another object. Define skolem constant. 25.
37.36. There are 2 kinds. 39. 42. What are the 2 types of processes? i.e. Representational Frame Problem ii. This is called occur check. Extended Intervals . Define magic set. Liquid events . A set of parameters and sentences 43. Define Occur check. one must check whether the variable itself occurs inside the term. What are the divisions of knowledge in OTTER theorem? i. Inferential Frame Problem 44. This algorithm works backward from the goal. 40. Moments ii. Define backward chaining. Discrete events – it have definite structure ii. This is called pattern matching. so that only relevant variable bindings called magic set. When matching a variable in 2 expressions against a complex term. 41. i. Define fluent calculus. 45.Categories of events with process. Define pattern matching. It is a set of definite clauses written in a notation somewhat different from standard FOL. What are the 2 types of frame problem? i. 46. Define Prolog program. Rewrites (or) Demodulators iv. The inner loop of an algorithm involves finding all the possible unifiers with facts in the KB. using information from the goal. Usable axioms iii. Discard Situation Calculus and invent a new formalism for writing axioms is Called Fluent Calculus. 38.) intervals is important for agent to take an action. What is important for agent? Time (i. chaining through rules to find known facts that support the proof. Set of Support (SOS) ii. If it does the match fails. To rewrite the rule set. Explain the function of Rete Algorithm? This algorithm preprocess the set of rules in KB to constant a sort of data flow network in which each node is a literals from rule a premise.
7. 4. It is important to remember that p(a) can only be used when there is no other information. “Phone number (Divya)”. b.What is the need for probability theory in uncertainty ? Probability provides the way of summarizing the uncertainty that comes from our laziness and ignorance . The use utility theory to represent and reason with preferences. What Is Called As Decision Theory ? Preferences As Expressed by Utilities Are Combined with Probabilities in the General Theory of Rational Decisions Called Decision Theory.N). whose preferences the utility function is supposed to represent. 3. Agents cannot find a caterorial answer. Practical ignorance: even if we know all the rules. Define runtime variables.laziness: o it is hard to lift complete set of antecedents of consequence. Probability statements do not have quite the same kind of semantics known as evidences. and that the agent will prefer states with higher utility. logic fails to cope with that the mind like medical diagnosis. needed to ensure and exceptionless rule. Theoritical Ignorance: o medical science has no complete theory for the domain. 5. utility of a state is relative to the agents.Define Prior Probability? p(a) for the Unconditional or Prior Probability Is That the Proposition A is True. Dial (n)] UNIT – IV 1. incorrectness in agents understanding of properties of environment. 2. . we may be uncertain about a particular item needed. Example [Look up (Agent. Why does uncertainty arise ? Agents almost never have access to the whole truth about their environment. What is called as principle of maximum expected utility ? The basic idea is that an agent is rational if and only if it chooses the action that yields the highest expected utility. Decision Theory = Probability Theory + Utility Theory. or utility to in agent. 8. State the reason why first order. averaged over all the possible outcomes of the action.what is the need for utility theory in uncertainty? Utility theory says that every state has a degree of usefulness. Plans to gather and use information are represented using short hand Notation called runtime variables (n). 6. This is known as MEU. Uncertainty can also arise because of incompleteness. Three reasons a.". 47. Define the term utility? The term utility is used in the sense of "the quality of being useful .
Define conditional probability? Once the agents has obtained some evidence concerning the previously unknown propositions making up the domain conditional or posterior probabilities with the notation p(A/B) is used.--------xn) assigns probabilities to all possible atomic events. This type of notations simplifies many equations. 18.What is meant by belief network? A belief network is a graph in which the following holds A set of random variables A set of directive links or arrows connects pairs of nodes.T P(A ^ B) = P(A/B) P(B) -------------------------.7.0. What are called as Poly trees? The algorithm that works only on singly connected networks known as Poly trees.08. Here at most one undirected path between any two nodes is present. P(weather) = (0.where X1.X2------Xn 10 =variables.The joint probability distribution p(x1.02).0. 13.What is an atomic event? An atomic event is an assignment of particular values to all variables. 19.x2.Define joint probability distribution This completely specifies an agent's probability assignments to all propositions in the domain.9. WE GET P(B/A) = P(A/B) P(B) -------------------.2.0.P(A) 14.What is the basic task of a probabilistic inference? The basic task is to reason in terms of prior probabilities of conjunctions. in other words. 12. we will use conditional probabilities as a vehicle for probabilistic inference.Define evidential support . This is important that p(A/B) can only be used when all be is known. The conditional probability table for each node The graph has no directed cycles. but for the most part.Give the Baye's rule equation W. What are the ways in which one can understand the semantics of a belief network? There are two ways to see the network as a representation of the joint probability distribution to view it as an encoding of collection of conditional independence statements. 10. Define probability distribution: Eg.K.Define casual support E+X is the casual support for X. 11.the evidence variables "above" X that are connected to X through its parent. 17.1 P(A ^ B) = P(B/A) P(A) -------------------------. the complete specifications of the state of domain.2 DIVIDING BY P(A) . 16. 15.
What is called as multiple connected graph? A multiple connected graph is one in which two nodes are connected by more than one path. . Reinforcement Learning 24. it gives him a indication that his behaviour is undesirable. Define Reinforcement Learning. What are issues in learning element? i.the evidence variables "below" X that are connected to X through its children. Supervised Learning Unsupervised Learning It involves learning a function It involves learning patterns in From examples of its inputs the input when no specific And outputs output values are supplied. Supervised Learning ii. This Learning is rather than being told what to do by teacher. 20. Learning a discrete valued function is called is called classification learning. Differentiate Supervised and Unsupervised learning. 28. What is the purpose of learning? The idea behind learning is that percepts should be used not only for acting but also for improving the agent’s ability to act in the future. a reinforcement learning agent must learn from occasional rewards. 25. 27. Representation 23. Example If taxi driver does not get a tip at the end of journey. Learning a continuous valued function is called regression learning. 22. Component ii. Unsupervised Learning iii. Define Inductive Learning. Feedback iii. An algorithm for supervised learning is given as input the correct value of the unknown function for particular inputs and it must try to recover the unknown function. days” and “Bad traffic days” without any advice. What are the types of machine learning? i. we can even skip will learn about “Good traffic on the road is a result. 21. 26. Define Regression learning. E-X is the evidential support for X. Example: Applying Brake on Example: Day by day agent the wet road. Define Classification Learning.
It can be applied to any learning algorithm. . An learning algorithm that return hypotheses that are approximately correct is called PAC learning algorithm. 34. Define Decision list. If test succeeds. If test fails. It consists of a series of tests. Information gain from the attribute test is the difference between the original information requirement and the new requirement. Multivalued attributes iii. 36. n/ (p+n)) – Remainder(A) 32. 33. 37. value is returned. We do this on a set of examples is known as Test Set.29. What is parity and majority function? Parity Function : It Returns 1 if and only if an even number of inputs are 1. not just decision tree learning. What is ensemble learning? The idea of this learning is to select a whole collection or ensemble. one has to be careful not to use the resulting freedom to find meaningless “regularity” in the data. Mention the exercises which broaden the applications of decision trees. of hypotheses from the hypotheses space and combine their predictions. What is training set? The complete set of examples is called the training set. 35. 38. What is the purpose of cross validation? It reduces over fitting. What is over fitting? Whenever there is a large set of possible hypotheses. 30. processing continues with the next test in the list. What is test set? Prediction is good if it turns out to be true. Continuous and integer valued input attributes iv. It is a logical expression of a restricted form. Gain (A) = I(p/(p+n)). Missing data ii. Example Restaurant problem Goal predicate “will wait” 31. Majority function : It Returns 1 if more than half of its inputs are 1. This problem is called over fitting. The basic idea is to estimate how well each hypotheses will predict unseen data. each of which conjunction of literals. Define Information gain. i. Continuous valued output attributes. so can assess quality of hypotheses by Checking its predictions against the correct classification once we know it. Define PAC – Learning Algorithm.
ILP methods can learn relational knowledge that is not expressible in attribute based systems. 48. Allows the agent to infer a new. ILP techniques perform KBIL on knowledge that is expressed in first order logic. general rule that explains the observations. 41. 45. This is called generalization. Each boundary will not be a point but rather a set of hypotheses called a Boundary set. What is the purpose of memorization? Memorization used in computer science to speed up programs by saving the results of computation. it first checks the database to see whether it can avoid solving the problem. Define RBL. Show the relationship of an entailment constraint. Define EBL. when the function is called.39. The extension of the hypotheses must be increased to include it. What are the two boundary sets? i. Define Boundary set. the prior knowledge background concerns the relevance of a set of features to the goal predicate. Define knowledge based Inductive learning. we can infer a general rule. Differentiate generalization and specialization. Hypothesis ^ Description |= classifications. The basic idea of memo function is to accumulate a database of input and output pairs. S Set : a most specific boundary set. KBIL algorithm finds inductive hypotheses that explain sets of observations with the help of background knowledge. . The extension of the hypotheses must be decreased to exclude the example. 40. This knowledge together with the observations. G Set : a most general boundary set. Hypothesis ^ Descriptions |= classifications 44. What is the purpose of current best hypotheses search? This search is to maintain a single hypothesis and to adjust it as new examples arrive in order to maintain consistency. ii. 43. This kind of generalization process called explanation based learning (or) EBL. Relevance based Learning. What is the entailment constraints satisfied by EBL? Hypothesis ^ Description |= classification Background |= Hypothesis 46. 49. This is called specialization. Explanation based learning. Background ^ Description ^ classifications |= Hypothesis. Define Inductive Logic Programming (ILP). 42. 47. from the prior knowledge (or) information.
Define Bayesian Learning.) predictions are made by using all the hypotheses.LITERALS 53. CHOOSE . Write down the derivative of the log likelihood with respect to each parameter. ii. NEW – LITERALS ii. Write down an expression for the likelihood of the data as a function of the parameter. iii. What are the 3 kinds of literals that can be added? i. What are the methods for maximum likelihood parameter learning? i. This model assumes that the attributes are conditionally independent of each other. given the data and makes predictions on that basis. Algorithms that can generate new predicates are called constructive induction algorithms. This is MAP. Define MAP. A very common approximation is to make predictions based on single most probable hypotheses. 52. Arithmetic comparisons 54. . Construct a new rule where LHS consists of the leaves of the proof tree and R. Construct a proof using the available background knowledge. 56. iv. Find the parameter values such that the derivatives are zero. Define constructive induction algorithm. Define sum of squared errors. Define MDL. In this model. 60. given the class. ii.e. 59.H. 51.50. (i. What is the basic EBL process step? i. Maximum A Posteriori. 55. Drop any conditions that are true. iii. What is Maximum – Likelihood hypotheses? ML – it is reasonable approach when there is no reason to prefer one hypotheses over another a prior. weighted by their probabilities rather than by using just single “best” hypotheses. The MDL (Maximum Description Length). The difference between the actual value yj and the predicated value ( θ1 xj + θ2 ) so E is the sum of squared errors. Define Naïve Bayes model. What are the two main subroutines used for generating literals? i. It calculates the probability of each hypotheses. 57. 58. Equality and inequality literals iii.S is the variabilized goal. Construct a generalized proof tree for the variabilized goal using the same inference steps as in the original proof. Literals using Predicate ii. is a learning method which attempts to minimize the size of the hypotheses and data encodings rather than work with probabilities. the “class” variable C is the root and the “attribute” variable XI are the leaves.
A link propagates the activation. What is single layer feed forward neural network? A network with all the inputs connected directly to the outputs is called a single layer neural network or a perceptron networks. 70. Each hidden unit act as a perceptron that represents a soft threshold functions in the input space. After that we refit the components to the data. What are the two functions in Neural network’s Activation functions? i.61. The agent’s policy is fixed and the task is to learn the utilities of states. Define Passive learning. E – Step ii. Give Activation function. Define Neural Networks. 62. M – Step 63. The agent must learn what to do. 64. Define Active Learning. Threshold function ii. What are the categories of neural network structures? i. . this could also involve learning a model of the environment. Output unit act as a soft threshold linear combination of several such functions. Define EM. n ai = g(ini) = g(Σ Wj. An agent must experience as much as possible of its environment in order to learn how to behave in it. What is multilayer feed forward neural networks? It consists of many hidden units. Sigmoid function 66. Acyclic (or) Feed – forward networks ii. Expectation Maximization: the idea of EM is to pretend that we know the parameters of the model and then to infer the probability that each data point belongs to each component. It consists of nodes or units connected by directed links. where each component is fitted to the entire data set with each point weighted by the probability. 68. What are the 2 steps in mixture model parameters? i. 69.i aj) j=0 65. Each link has a numeric weight which determines the strength and sign of the connection. Cyclic (or) Recurrent Networks 67.
Conditional planning is a way in which the incompleteness of information is incorporated in terms of adding a conditional step. Planning can be viewed as a type of problem solving in which the agent uses beliefs about actions and their consequences to search for a solution. The planner should be able to add new actions at any time. What are the ways in which incomplete and incorrect information’s can be handled in planning? They can be handled with the help of two planning agents namely. Define a complete plan. iv. Precondition. A consistent plan is one in which there are no contradictions in the ordering or binding constraints. 2. ii. A set of ordering constraints. 7. A complete plan is one in which every precondition of every step is achieved by some other step. 8. i. Define conditional planning. iii. Conditional planning agent. 9. ii. 3. The planner should be able to represent the states. . 4. Progressive planning. Situation space planning. UNIT – V 1. Partial order planning.What are the features of an ideal planner? i. Define planning. Fully instantiated planning. What are the components that are needed for representing an action? The components that are needed for representing an action are: Action description.71. which involves if – then rules. goals and actions. Define a solution. Define TD. The planner should be able to use Divide and Conquer method for solving very big problems. Effect. Replanning agent. 5. ii. 10. Define a consistent plan. 6. iii. Temporal Difference learning: The key of TD is to use the observed transitions to adjust the values of the observed states so that they agree with the constraint equations. What are the different types of planning? The different types of planning are as follows: i. What are the components that are needed for representing a plan? The components that are needed for representing a plan are: A set of plans steps. v. Regressive planning. A solution is defined as a plan that an agent can execute and that guarantees the achievement of goal.
do nothing” 16. Define Reification. b. 21. 14. State the no altering principle. The law states that. “When there is doubt about what to do. Similarity net is a representation in which nodes denotes models. The law states that. They are: i. 18. iii. Learning by training neural networks. “ You cannot learn anything unless you almost know it already”. “ When an object or situation known to be an example. The no altering principle. c. fails to match a general model. Give the classification of learning process. c. Learning by managing models. usually called as Data base mining: a. 12. State Martin’s law. What is reified link? The elevation of a link to the status of a describable node is a kind of reification. The wait and see principle. d. Require-link heuristics. The law states that. ii. The learning process can be classified as: Process which is based on coupling new information to previously acquired knowledge a.11. Forbid-link heuristics. What are the different types of induction heuristics? There are two different types of induction heuristics. 15. Process which is based on digging useful regularity out of data. The process of treating something abstract and difficult to talk about as though it were concrete and easy to talk about is called as reification. Learning by recording cases. 17. Similarity net is an approach for arranging models. Martin’s law. Define Similarity nets. When a link is so elevated then it is said to be a reified link. . Learning by building identification trees. Learning by correcting mistakes. What is Induction heuristics? Descriptions from positive and negative examples. Learning by analyzing differences. Learning by explaining experience. What are the principles that are followed by any learning procedure? i. State the wait and see principle. ii. b. 20. links connect similar models and links are tied to different descriptions. 19. create a special case exception model”. Define Communication. 13.
Over generation iii. 22. Synthesis iv.) with the words and search for a tree with root S. Parsing is the process of finding a parse tree for a given input string. A formal language is defined as a set of strings of terminal symbols. 31. Generating Good English sentences ii. What are the algorithms to have efficient parsing? i. Define Formal Language. 23. Define Bottom up parsing. Language enables us to communicate most of what we have observed about the environment. Intention ii. We start from the leaf nodes (i. The process of extracting the meaning of an utterance an expression in some representation language. 27. Define Language. Top down parsing ii. Perception v. 29. Define Parsing. What are the processes in communication? i. 32. Define Semantic Interpretation. Define Top down parsing. 24. It starts with root node S and search for a tree that has the words as it leaves. Incorporation 25. Syntactic Interpretation ii. Pragmatic Interpretation 26. Generation iii. Analysis vi Disambiguation vii. Under generation 28. It is called as words. Left to right parsing algorithm ii. Semantic Interpretation iii. Chart Parsing algorithm. Left corner parsing . i.e. It is also known as syntactic analysis. What are the processes of Relative clause. iii. What are the parts in analyze? i. Communication is the international exchange of information brought about by the production and perception of signs drawn from a shared system of conventional signs. What are the types of parsing? i. Bottom up parsing 30.
39.33. Define Segmentation? The process of finding the words boundaries in a text with no spaces. 37. 44. E 2 eliminates VP by mentioning which phrases can allow which verbs which are known as sub categorization. A discourage is any string of language usually one that is more than one sentence long. Define Disambiguation. What are the characteristics of IR? i. Add one smoothing. What are the types of smoothing? i. 45. 40. Linear Interpolation Smoothing 43. The structure of coherent discourse. 35. 41. Information retrieval ii. Machine Translation 42. Syntactic Ambiguity iii. Define Information Retrieval (IR). Define DCG. The process of adding the existing rules of a grammar instead of introducing new rules. 34. The method of rewriting the existing rules in the grammar by the method of augmentation is called as DCG (Define Clause Grammar). It is called Augmentation. What are the tasks in probabilistic language model? i. . The sentence that does not provide exact meaning are called ambiguous sentence. Define Ambiguity. A document collection. Information Extraction iii. Define Augmentation. What are the types of Ambiguity? i. 36. ii. IR is the task of finding documents that are relevant to user’s need for information. Semantic Ambiguity 38. Define Discourse understanding. Lexical Ambiguity ii. What are the two sub problems in discourse understanding? i. The speaker’s aim is to communicate some words in utterance and hearer work is to get back the meaning of the world from the knowledge of situation. Reference Resolution ii. Define Sub categorization.
What are the types of information extraction systems? i. Precision 47. Complex phrases v. Case folding ii. It is a process of creating database entries by skimming a text and looking for occurrences of a particular class of object. Lexicon ii. What are the types of machine translation? i. What are the stages in Relational based systems? i. What are the methods to do IR Refinements? i. Basic groups iv. Complex word handling iii. Literacy translation . What are the two data structures that make IR systems efficient? i. 52. What are the types of evaluation of IR systems? i. What are the types of clustering technique? i. Relevance feedback ii. Document classification iii. Document clustering 49. Recognize synonyms iv. A result set iv. Restricted source translation iii. K-means clustering 50. Rough translation ii. Meta data 48. iii. Agglomerative clustering ii. Define Information Extraction. tokenization ii. Stemming iii. A query posed in a query language. 46. What are the 3 mechanism to achieve performance improvement? i. ii. Recall ii. Relational based system 53.Inverted Index 51. Pre edited translation iv. Attribute based system ii. Merges structures 54. Spelling correction v. A presentation of the result set.
Estimate the initial word choice model vi. Estimate the French language model iii. . How to use the parameters for machine translation? i. Align sentences iv. Estimate the initial offset model vii. Improve all the estimates. Estimate the initial fertility model v.55. Segment into sentences ii.
Principles of Compiler Design -A.v. Aho . J.D.ullman; Pearson Education.

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Set type to int, and value to v.
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Set type to MapType, and value to v.
Set type to ListType, and value to v.
Check for equality with another Element.
Check for inequality with anything we can check equality with.
Check for equality with a int.
Check for equality with a double.
Check for equality with a pointer.
Check for equality with a const char *.
Check for equality with a std::string.
Check for equality with a MapType.
Check for equality with a ListType.
Check whether the current type is nothing.
Check whether the current type is int.
Check whether the current type is double.
Check whether the current type is pointer.
Check whether the current type is numeric.
Check whether the current type is std::string.
Check whether the current type is MapType.
Check whether the current type is ListType.
Retrieve the current value as a int.
Retrieve the current value as a double.
Retrieve the current value as a pointer.
Retrieve the current value as a number.
Retrieve the current value as a const std::string reference.
Retrieve the current value as a non-const std::string reference.
Retrieve the current value as a const MapType reference.
Retrieve the current value as a non-const MapType reference.
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Retrieve the current value as a non-const ListType reference.
Changed the name to Element as Object is a stupid name for a class.

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