400188
Finite Identification of Functions by Teams with Success Ratio 1\over2 and Above
Consider a scenario in which an algorithmic machine, M, is being fed the graph of a computable function ƒ. M is said to finitely identify ƒ just in case, after inspecting a finite portion of the graph of ƒ, it emits its first conjecture, which is a program for ƒ, and it never abandons this conjecture thereafter. A team of machines is a multiset of such machines. A team is said to be successful just in case each member of some nonempty subset, of predetermined size, of the team is successful, The ratio of the number of machines required to be successful to the size of the team is referred to as the success ratio of the team. The present paper investigates the finite identification of computable functions by teams of learning machines. The results presented complete the picture for teams with success ratio 12 and greater. It is shown that at success ratio 12, introducing redundancy in the team can result in increased learning power. In particular, it is established that larger collections of functions can be learned by employing teams of 4 machines and requiring at least 2 to be successful than by employing teams of 2 machines and requiring at least 1 to be successful. Surprisingly, it is also shown that introducing further redundancy at success ratio 12 does not yield any extra learning power. In particular, it is shown that the collections of functions that can be finitely identified by a team of 2m machines requiring at least m to be successful is the same as: 
• 
the collections of functions that can be finitely identified by a team of 4 machines requiring at least 2 to be successful, if is even, and 
 
• 
the collections of functions that can be identified by a team of 2 machines requiring at least 1 to be successful, if is odd. 
 
 
These latter results require development of sophisticated simulation techniques.
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.
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.
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.
44990266
Topology Independent Models for Parallel Computation
In a continuous or cyclic steam foam drive, recovery of an acidic reservoir oil (1) is improved by injecting steam accompanied by (a) enough dissolved alkaline monovalent salt to ion-exchange multivalent cations from the reservoir rocks and precipitate those ions in compounds which are insoluble in an alkaline aqueous liquid as well as forming soaps of the reservoir oil acids and (b) surfactants for foaming the steam and increasing the salt tolerance of an aqueous surfactant system containing the soaps of the reservoir oil acids, and (2) is further improved by using trona or an equivalent mixture of alkali metal carbonates and bicarbonates as the alkaline monovalent salt.
34370031
Simulation for emergency response: a framework for modeling and simulation for emergency response
A number of modeling and simulation tools have been developed and more are being developed for emergency response applications. The available simulation tools are meant mostly for standalone use. Addressing an emergency incident requires addressing multiple interdependent aspects of the situation. The simulation tools addressing different aspects of an emergency situation need to be integrated to provide the whole picture to planners, trainers, and responders. A framework is required to ensure that modeling and simulation tools can be systematically integrated together to address the overall response. This paper proposes a framework for integration of modeling, simulation, and visualization tools for emergency response. The development and implementation of the proposed framework will significantly improve the nation's capability in the emergency response area.
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.
17470610
Load balancing for conservative simulation on shared memory multiprocessor systems
Load balancing is a crucial factor in achieving good performance for parallel discrete event simulations. We present a load balancing scheme that combines both static partitioning and dynamic load balancing. The static partitioning scheme maps simulation objects to logical processes before simulation starts while the dynamic load balancing scheme attempts to balance the load during runtime. The static scheme involves two steps. First, the simulation objects that contribute to small lookahead are merged together by using a merging algorithm. Then a partitioning algorithm is applied. The merging is needed to ensure a consistent performance for our dynamic scheme. Our dynamic scheme is tailor-made for an asynchronous simulation protocol that does not rely on null messages. The performance study on a supply chain simulation shows that the partitioning algorithm and dynamic load balancing are important in achieving good performance.
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