809142
The Intrinsic Complexity of Language Identification
A new investigation of the complexity of language identification is undertaken using the notion of reduction from recursion theory and complexity theory. The approach, referred to as the intrinsic complexity of language identification, employs notions of ‘weak’ and ‘strong’ reduction between learnable classes of languages. The intrinsic complexity of several classes is considered and the results agree with the intuitive difficulty of learning these classes. Several complete classes are shown for both the reductions and it is also established that the weak and strong reductions are distinct. An interesting result is that the self referential class of Wiehagen in which the minimal element of every language is a grammar for the language and the class of pattern languages introduced by Angluin are equivalent in the strong sense. This study has been influenced by a similar treatment of function identification by Freivalds, Kinber, and Smith.
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.
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.
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".
112103084
Using simulation generators for modeling flexible manufacturing systems
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.
17397415
Analyzing the supply chain for a large logistics operation using simulation
This paper presents a case study of using simulation for analyzing the impact of proposed changes in the supply chain processes for a large logistics operation. The major changes explored include business process changes, and use of new supply chain software. The results of the analysis indicate that the changes in forecast accuracy provide much larger savings compared to process automation changes. A number of insights are drawn from the results of the analyses.
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.
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.