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Browse All Reviews > Computing Methodologies (I) > Simulation And Modeling (I.6) > Simulation Theory (I.6.1) > Model Classification (I.6.1...)
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1-10 of 13
Reviews about "Model Classification (I.6.1...)":
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Model selection in reinforcement learning
Farahmand A., Szepesvári C. Machine Learning 85(3): 299-332, 2011. Type: Article
This paper considers the problem of finding an optimal action-value function, and choosing the action to perform, in the context of batch reinforcement learning. The learning problem is to identify the best action to take in the context of a...
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May 3 2012 |
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Properties of hybrid systems--a computer science perspective
Stauner T. Formal Methods in System Design 24(3): 223-259, 2004. Type: Article
In this paper, properties of mixed discrete and continuous systems are clarified for computer scientists, to help them to have a good grasp of corresponding control theory concepts. This authoritatively well-written paper is based on the...
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Dec 13 2004 |
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Further towards a taxonomy of agent-based simulation models in environmental management
Hare M., Deadman P. Mathematics and Computers in Simulation 64(1): 25-40, 2004. Type: Article, Reviews: (2 of 2)
Six requirements for the classification of simulation studies of environmental management that are implemented using agent technology are elaborated on in this paper. The requirements are: coupling social and environmental models, micro-level...
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May 19 2004 |
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Further towards a taxonomy of agent-based simulation models in environmental management
Hare M., Deadman P. Mathematics and Computers in Simulation 64(1): 25-40, 2004. Type: Article, Reviews: (1 of 2)
The authors provide an overview of agent-based simulation (ABS) for environmental modelers, with the purpose of linking user requirements to currently available techniques. Although the paper contributes positively to the classification of a...
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Apr 23 2004 |
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Parallelism in sequential multiprocessor simulation models: a case study
Sellami H., Yalamanchili S. ACM Transactions on Modeling and Computer Simulation 5(2): 101-128, 1995. Type: Article
Discrete event simulation is a complex computer application that uses a great deal of computer resources. It is therefore reasonable to attempt to apply parallel computing to this process. The authors propose a technique to automatically convert...
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Oct 1 1996 |
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Specification of Fault-Tolerant System Issues by predicate/Transition Nets and Regular Expressions-Approach and Case Study
Belli F., Grosspietsch K. IEEE Transactions on Software Engineering 17(6): 513-526, 1991. Type: Article
A method to systematically integrate fault tolerance properties into the design of complex software systems is presented. This integration is accomplished by means of a formal system specification that uses a combination of a predicate/transition ...
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Nov 1 1992 |
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Using Flat Concurrent Prolog in System Modeling
Dotan Y., Arazi B. IEEE Transactions on Software Engineering 17(6): 493-512, 1991. Type: Article
The evolution of a system with parallel components can be described graphically in terms of a Petri net representation. This representation lends itself naturally to a logic program description. The aim of this paper is to develop the use of Flat ...
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Oct 1 1992 |
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Control strategies for two-player games
Abramson B. ACM Computing Surveys 21(2): 137-161, 1989. Type: Article
Abramson considers computer games and the various control strategies, proposed and implemented, for playing them. He introduces the major approaches for describing and analyzing game search trees, including the minimax, alpha-beta pruning,...
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Jul 1 1990 |
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Dynamic models and discrete event simulation
Delaney W., Vaccari E., Marcel Dekker, Inc., New York, NY, 1989.Type: Book (9789780824776541)
This textbook contains ten chapters: “Systems and Models,” “Deterministic Models,” “Stochastic Models,” “Identification of Stochastic Systems,” “Modeling of Complex Systems,” “The...
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Nov 1 1989 |
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Fuzzy least squares
Diamond P. Information Sciences: an International Journal 46(3): 141-157, 1988. Type: Article
This paper gives an algorithm for least squares fitting of fuzzy data to linear models of the following forms: Y = a + b X a , b  R Y = E + b X b R , E...
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May 1 1989 |
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