18622476
Learning in the Presence of Partial Explanations
Abstract The effect of a partial explanation as additional information in the learning process is investigated. A scientist performs experiments to gather experimental data about some phenomenon, and then tries to construct an explanation (or theory) for the phenomenon. A plausible model for the practice of science is an inductive inference machine (scientist) learning a program (explanation) from a graph (set of experiments) of a recursive function (phenomenon). It is argued that this model of science is not an adequate one, as scientists, in addition to performing experiments, make use of some approximate partial explanation based on the “state of the art” knowledge about that phenomenon. An attempt has been made to model this partial explanation as additional information in the scientific process. It is shown that the inference capability of machines is improved in the presence of such a partial explanation. The quality of this additional information is modeled using certain “density” notions. It is shown that additional information about a “better” quality partial explanation enhances the inference capability of learning machines as scientists more than a “not so good” partial explanation. Similar enhancements to inference of approximations, a more sophisticated model of science, are demonstrated. Inadequacies in Gold's paradigm of language learning are investigated. It is argued that Gold's model fails to incorporate certain additional information that children get from their environment. Children are sometimes told about some grammatical rule that enumerates elements of the language. It is argued that these rules are a kind of additional information. They enable children to see in advance elements that are yet to appear in their environments. Also, children are being given some information about what is not in the language. Sometimes, they are rebuked for making incorrect utterances, or are told of a rule that enumerates certain non-elements of the language. An attempt has been made to extend Gold's model to incorporate both the above types of additional information. It is shown that either type of additional information enhances the learning capability of formal language learning devices.
30890449
Prudence in Vacillatory Language Identification (Extended Abstract)
The present paper settles an open question about ‘prudent’ vacillatory identification of grammars from positive data only.
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".
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.
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.
39763724
Branch and bound on the network model
Abstract Karp and Zhang developed a general randomized parallel algorithm for solving branch and bound problems. They showed that with high probability their algorithm attained optimal speedup within a constant factor (for p⩽ n/( log n) c , where p is the number of processors, n is the “size” of the problem, and c is a constant). Ranade later simplified the analysis and obtained a better processor bound. Karp and Zhang's algorithm works on models of computation where communication cost is constant. The present paper considers the Branch and Bound problem on networks where the communication cost is high. Suppose sending a message in a p processor network takes G = O ( log p) time and node expansion (defined below) takes unit time (other operations being free). Then a simple randomized algorithm is presented which is, asymptotically, nearly optimal for p = O (2 log c n ) , where c is any constant and n is the number of nodes in the input tree with cost no greater than the cost of the optimal leaf in the tree.
11952430
David vs. Goliath: An Analysis of Asymmetric Mixed-Strategy Games and Experimental Evidence
Mixed strategies are widely used to model strategic situations in diverse fields such as economics, marketing, political science, and biology. However, some of the implications of asymmetric mixed-strategy solutions are counterintuitive. We develop a stylized model of patent race to examine some of these implications. In our model two firms compete to develop a product and obtain a patent. However, one firm values the patent more because of its market advantages, such as brand reputation and distribution network. Contrary to some intuition, we find that the firm that values the patent less is likely to invest more aggressively in developing the product and will also win the patent more often. We argue that the reason for these counterintuitive results is inherent in the very concept of mixed strategy solution. In a laboratory test, we examine whether subjects' behavior conforms to the equilibrium predictions. We find that the aggregate behavior of our subjects is consistent with the game-theoretic predictions. With the help of the experience-weighted attraction (EWA) learning model proposed by Camerer and Ho (1999), we show that adaptive learning can account for the investment behavior of our subjects.We find that the EWA learning model tracks the investment decisions of our subjects well, whether we hold out trials or an entire group of subjects.
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.
32884753
Expert Simulation For On-line Scheduling
In recent years, the automotive industry has realized the importance of speed of new products to market and has mounted efforts for improving it. The Expert System Scheduler (ESS) facilitates these efforts by enabling manufacturing plants to generate viable schedules under increasing constraints and demands for flexibility. The scheduler takes advantage of the Computer Integrated Manufacturing (CIM) environment by utilizing the real-time information from the factory for responsive scheduling. The Expert System Scheduler uses heuristics developed by an experiences factory scheduler. It uses simulation concepts and these heuristics to generate schedules. Forward and "backward" simulation are used at different stages of the schedule generation process. The system is used to control parts flow on the factory floor at one automated facility. This highly automated facility is a testbed for implementation of CIM concepts. The scheduler runs on a Texas Instruments (TI) Explorer II computer using software developed inhouse utilizing IntelliCorp's Knowledge Engineering Environment (KEE) shell and the LISP language. The scheduling computer is networked to the factory control computer, which actually controls the plant floor. The TI Explorer II acquires current plant floor information from the factory control system, generates a new schedule and sends it back within a short time. The configuration allows fast response to changes in requirements and plant floor conditions.
16256467
Implementations of dispatch rules in parallel manufacturing simulation
Most features in commercial simulation packages are often omitted in parallel simulation benchmarks, because they neither affect the overall correctness of the simulation protocol nor the benchmark's performance. In our work on parallel simulation of a wafer fabrication plant, we however find several features which complicate the implementation of the simulation protocol and affect the program performance. One such feature is the dispatch rules which a machine set uses to decide the priority of the waiting wafer lots. In a sequential simulation, the dispatch rule can be implemented in a straightforward fashion, because the whole system state is at the same simulation time, and the rule simply reads the state variables (of any machine, resources, etc.) In a parallel simulation, the dispatch rule computation may be complicated by the fact that different portions of the simulated system can be at different simulation times. This paper describes our study of the implementation of dispatch rules in parallel simulation. This is actually an instance of the little-studied problem of providing shared-state information in parallel simulation. We briefly survey previous related work. We then outline two different approaches for a dispatch rule to access the shared-state information and compare them in terms of their ease of implementation.
15127807
Pricing of Information Products on Online Servers: Issues, Models, and Analysis
Online information servers that provide access to diverse databases where users can search for, browse through, and download the information they need have been rapidly increasing in number in the past few years. Online vendors have traditionally charged users for information on the based on the length of the time they were connected to the databases. With hardware and software advances, many online servers have recently started changing their pricing strategies to search-based and/or subscription-fee pricing. This paper examines the various issues involved in pricing these information products, and presents an economic approach to analyze conditions under which the various pricing schemes may prove optimal for the online servers. Our results show that the variation in consumer expertise and valuation of information affects the choice of a pricing strategy by the server. We present general conditions under which subscription-fee pricing is optimal even when consumer demand is inelastic. We also find that, given the cost structures characterizing the market, undifferentiated online servers can compete and coexist in the market each making positive profits. We show that in a competitive setting an increase in costs of online servers can sometimes benefit them by enabling them to differentiate themselves. Our results offer insights into the trends in pricing strategies and may provide an explanation as to why many servers may persist with connect-time strategies.