10759876
A Polynomial Time Learner for a Subclass of Regular Patterns
Presented is an algorithm (for learning a subclass of erasing regular pattern languages) which can be made to run with arbitrarily high probability of success on extended regular languages generated by patterns of the form x0 1x1::: mxm for unknown m but known c , from number of examples polynomial in m (and exponential in c ), where x0; : : : ; xm are variables and where 1; :::; m are each strings of terminals of length c . This assumes that the algorithm randomly draws samples with natural and plausible assumptions on the distribution. With the aim of nding a better algorithm, we also explore computer simulations of a heuristic.
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.
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.
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.
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.
7115385
Integrated simulation and gaming architecture for incident management training
The simulation-based training systems that are available or under development today for incident management are typically focused on macro level sequence of events. A few systems targeted at individual responders are under development using a gaming environment. Separate uses of such systems provide disparate experiences to decision makers and individual responders. There is a need to provide common training experiences to these groups for better effectiveness. This paper presents a novel approach integrating gaming and simulation systems for training of decision makers and responders on the same scenarios preparing them to work together as a team. An integrated systems architecture is proposed for this purpose. Major modules in gaming and simulation subsystems are defined and interaction mechanisms established. Research and standards issues for implementation of the proposed architecture are discussed.
7194511
A Concept Prototype for Integrated Gaming and Simulation for Incident Management
This paper describes a prototype that has been developed to demonstrate the concept of integrated gaming and simulation for incident management. An architecture for the purpose was developed and presented at the last conference. A hypothetical emergency incident scenario has been developed for demonstrating the applicability of integrated simulation and gaming. A number of simulation and gaming modules have been utilized to model the major aspects of the hypothetical scenario. The modules demonstrate the value of utilizing simulation for incident management applications. They can be used to highlight the value of simulation and gaming for training applications in particular. Two of the simulation modules have been integrated using a modified implementation of the high level architecture to give an idea of the advantages. Technical issues in integration are identified
14572238
A simulation architecture for manufacturing interoperability testing
Manufacturing systems are often costly to develop and operate. Simulation technology has been demonstrated to be an effective tool for improving the efficiency of manufacturing system design, operation, and maintenance. But manufacturing simulations are usually developed to address a narrow set of industrial issues, e.g., the purchase of new equipment or the modification of a manufacturing process. Once the analysis is complete a particular simulation model may not be used again. If simulations could be made more modular and easily integrated, they could have tremendous value as tools for manufacturing interoperability testing. This paper presents a modular reference architecture to facilitate the integration of manufacturing simulation systems with other support and testing applications. Opportunities for testing are also discussed that will be enabled by the implementation of the architecture.
14852497
Towards standards for integrated gaming and simulation for incident management
Simulation and gaming can support decision making through all phases of incident management including prevention, preparedness, response, recovery and mitigation. A number of gaming and simulation tools have been developed for the purpose but they generally utilize proprietary or unique data interfaces and their own view of partitioning the application and solution space. This creates a large obstacle for wide use, and in particular, prevents the use of these tools in an integrated manner to address the application space. This paper explores the groundwork needed to build standards for integrated gaming and simulation tools for incident management. 
 
An architecture has been proposed to identify the required groups of simulation and gaming modules for incident management and define their scope in the solution space. A conceptual model is proposed for the data required for such simulations. Available data exchange standards are mapped to the conceptual data model. A concept prototype has been developed based on the architecture to demonstrate the value of integrated modeling and simulation and the architecture itself. A number of simulation and gaming modules have been utilized to model the major aspects of a hypothetical scenario. The exercise is used to identify the issues due to lack of data exchange standards.