14992537
Machine Induction Without Revolutionary Changes in Hypothesis Size
This paper provides a beginning study of the effects on inductive inference of paradigm shifts whose absence is approximately modeled by various formal approaches to forbidding large changes in the size of programs conjectured. One approach, calledseverely parsimonious, requires all the programs conjectured on the way to success to be nearly (i.e., within a recursive function of) minimal size. It is shown that this very conservative constraint allows learning infinite classes of functions, butnotinfinite r.e. classes of functions. Another approach, callednon-revolutionary, requires all conjectures to be nearly the same size as one another. This quite conservative constraint is, nonetheless, shown to permit learning some infinite r.e. classes of functions. Allowing up to one extrabounded sizemind change towards a final program learned certainly does not appear revolutionary. However, somewhat surprisingly for scientific (inductive) inference, it is shown that there are classes learnablewiththe non-revolutionary constraint (respectively, with severe parsimony), up to (i+1) mind changes, and no anomalies, which classes cannotbe learned with no size constraint, an unbounded, finite number of anomalies in the final program, but with no more thanimind changes. Hence, in some cases, the possibility of one extra mind change is considerably more liberating than removal of very conservative size shift constraints. The proofs of these results are also combinatorially interesting.
122230605
On the intrinsic complexity of learning recursive functions
The intrinsic complexity of learning compares the difficulty of learning classes of objects by using some reducibility notion. For several types of learning recursive functions, both natural complete classes are exhibited and necessary and sufficient conditions for completeness are derived. Informally, a class is complete iff both its topological structure is highly complex while its algorithmic structure is easy. Some self-describing classes turn out to be complete. Furthermore, the structure of the intrinsic complexity is shown to be much richer than the structure of the mind change complexity, though in general, intrinsic complexity and mind change complexity can behave "orthogonally".
5851955
Vacillatory Learning of Nearly Minimal Size Grammars
In Gold's influential language learning paradigm a learning machine converges in the limit to one correct grammar. In an attempt to generalize Gold's paradigm, Case considered the question whether people might converge to vacillating between up to (some integer) n > 1 distinct, but equivalent, correct grammars. He showed that larger classes of languages can be algorithmically learned (in the limit) by converging to up to n + 1 rather than up to n correct grammars. He also argued that, for ''small'' n>1, it is plausible that people might sometimes converge to vacillating between up to n grammars. The insistence on small n was motivated by the consideration that, for ''large'' n, at least one of n grammars would be too large to fit in people's heads. Of course, even for Gold's n = 1 case, the single grammar converged to in the limit may be infeasibly large. An interesting complexity restriction to make, then, on the final grammar(s) converged to in the limit is that they all have small size. In this paper we study some of the trade-offs in learning power involved in making a well-defined version of this restriction. We show and exploit as a tool the desirable property that the learning power under our size-restricted criteria (for successful learning) is independent of the underlying acceptable programming systems. We characterize the power of our size-restricted criteria and use this characterization to prove that some classes of languages, which can be learned by converging in the limit to up to n + 1 nearly minimal size correct grammars, cannot be learned by converging to up to n unrestricted grammars even if these latter grammars are allowed to have a finite number of anomalies (i.e., mistakes) per grammar. We also show that there is no loss of learning power in demanding that the final grammars be nearly minimal size iff one is willing to tolerate an unbounded, finite number of anomalies in the final grammars and there is a constant bound on the number of different grammars converged to in the limit. Hence, if we allow an unbounded, finite number of anomalies in the final grammars and the number of different grammars converged to in the limit is unbounded but finite (or if there is a constant bound on the number of anomalies allowed in the final grammars), then there is a loss of learning power in requiring that the final grammars be nearly minimal size. These results do not always match what might be expected from the cases, previously examined by Freivalds, Kinber, and Chen, of learning nearly minimal size programs for functions.
39847140
Team Learning of Recursive Languages
A team of learning machines is a multiset of learning machines. A team is said to successfully learn a concept just in case each member of some nonempty subset, of predetermined size, of the team learns the concept. Team learning of languages turns out to be a suitable theoretical model for studying computational limits on multi-agent machine learning. Team learning of recursively enumerable languages has been extensively studied. However, it may be argued that from a practical point of view all languages of interest are recursive. 
 
This paper gives theoretical results about team learnability of recursive languages. These results are mainly about two issues: redundancy and aggregation. The issue of redundancy deals with the impact of increasing the size of a team and increasing the number of machines required to be successful. The issue of aggregation deals with conditions under which a team may be replaced by a single machine without any loss in learning ability. The learning scenarios considered are: (a) Identification in the limit of accepting grammars for recursive languages. (b) Identification in the limit of decision procedures for recursive languages. (c) Identification in the limit of accepting grammars for indexed families of recursive languages. (d) Identification in the limit of accepting grammars for indexed families with enumerable class of grammars for the family as the hypothesis space. Scenarios which can be modeled by team learning are also presented.
145211
Computational Limits on Team Identification of Languages
A team of learning machines is a multiset of learning machines. A team is said to successfully identify a concept just in case each member of some nonempty subset, of predetermined size, of the team identifies the concept. Team identification of programs for computable functions from their graphs has been investigated by Smith. Pitt showed that this notion is essentially equivalent to function identification by a single probabilistic machine. The present paper introduces, motivates, and studies the more difficult subject of team identification of grammars for languages from positive data. It is shown that an analog of Pitt's result about equivalence of team function identification and probabilistic function identification does not hold for language identification, and the results in the present paper reveal a very complex structure for team language identification. It is also shown that for certain cases probabilistic language identification is strictly more powerful than team language identification. Proofs of many results in the present paper involve very sophisticated diagonalization arguments. Two very general tools are presented that yield proofs of new results from simple arithmetic manipulation of the parameters of known ones.
644359
Anomalous Learning Helps Succinctness
Abstract It is shown that allowing a bounded number of anomalies (mistakes) in the final programs learned by an algorithmic procedure can considerably “succinctify” those final programs. Naturally, only those contexts are investigated in which the presence of anomalies is not actually required for successful inference (learning). The contexts considered are certain infinite subclasses of the class of characteristic functions of finite sets. For each finite set D , these subclasses have a finite set containing D . This latter prevents the anomalies from wiping out all the information in the sets featured in these subclasses and shows the context to be fairly robust. Some of the results in the present paper are shown to be provably more constructive than others. The results of this paper can also be interpreted as facts about succinctness of coding finite sets, which facts have interesting consequences for learnability of decision procedures for finite sets.
9214465
Performance prediction tools for parallel discrete-event simulation
We have developed a set of performance prediction tools which help to estimate the achievable speedups from parallelizing a sequential simulation. The tools focus on two important factors in the actual speedup of a parallel simulation program: the simulation protocol used; and the inherent parallelism in the simulation model. The first two tools are a performance/parallelism analyzer for a conservative, asynchronous simulation protocol, and a similar analyzer for a conservative, synchronous (super-step) protocol. Each analyzer allows us to study how the speedup of a model changes with increasing number of processors, when a specific protocol is used. The third tool-a critical path analyzer-gives on ideal upper bound to the model's speedup. This paper gives an overview of the prediction tools, and reports the predictions from applying the tools to a discrete-event wafer fabrication simulation model. The predictions are close to speedups from actual parallel implementations. These tools help us to set realistic expectations of the speedup from a parallel simulation program, and to focus our work on issues which are more likely to yield performance improvement.
5203881
Components of an Incident Management Simulation and Gaming Framework and Related Developments
The nation's emergency responders need to work in a coordinated, well-planned manner to best mitigate the impact of an emergency incident. They need to be trained and ready to act in view of the increased security threat. The training has been traditionally provided using live eXercises at great eXpense. Simulation and gaming systems could provide a wider range of training at a much lower cost. Integrated gaming and simulation systems should be used for training decision makers and responders on the same scenarios, preparing them to work together as a team. A large number of simulation and gaming tools are needed to address the huge number of scenarios that can be of interest for incident management. An efficient approach will be to develop a library of interoperable component models that can be assembled together in different combinations to represent a range of scenarios. Standard specifications of the component models are needed to stimulate the development. The specifications should allow for alternate competing implementations. This paper presents a conceptual architecture that partitions the incident management simulation and gaming solution space into standard components. Recent literature is surveyed to identify related models and/or simulators where available for each defined component. The suggested components and the survey lay some preliminary groundwork in developing a holistic model of the incident response domain.
6148667
Supply chain management tradeoffs analysis
Supply chain management involves understanding complex interactions between many factors and using the understanding to achieve balance between conflicting objectives. Simulation is a very useful technique to evaluate the impact of changes in factors such as inventory control and business process parameters. This paper describes a simulation based study for analyzing the tradeoffs among service level, inventory and lead times for a large logistics supply chain. The study highlights the use of simulation in understanding seemingly non-intuitive results and guiding the effort for performance improvement.
195865607
Experiences With Backward Simulation Based Approach For Lot Release Planning
Bottleneck based scheduling approach suggests planning the release of lots based on the capacity of the bottleneck machines in the manufacturing process. Many scheduling systems determine the start of the first operation of the lot based on backward scheduling from its operation on the bottleneck machine. An approach was developed for determining the lot release times based on backward simulation. While conceptually appealing and successful with simple problems, the approach did not lead to expected improvements in a highly complex semiconductor manufacturing scenario. This paper describes the approach, its implementation and the limitations realized in the complex scenario.
1408878
Distributed simulation for interoperability testing along the supply chain
The need for interoperability of information systems among supply chain partners has been recognized. A number of standards have been or are being developed to ensure interoperability of applications used along the supply chain. An associated need for interoperability testing has emerged. There is a need to evaluate compliance of applications to standards across specific platforms. The standards themselves need to be evaluated for a comprehensive coverage of the application scope (validation testing). This paper reports on a distributed simulation based approach for supply chain interoperability testing. Simulations are used to represent real life organizations to serve as sources and consumers of dynamic data. The data can be encapsulated per the standard under consideration and exchanged with other organizations directly or through selected applications for testing. Error free performance of the simulated systems over time will provide confidence in the interoperability of applications and in the standards themselves.