Details
| Original language | English |
|---|---|
| Pages (from-to) | 347-365 |
| Number of pages | 19 |
| Journal | International Game Theory Review |
| Volume | 7 |
| Issue number | 3 |
| Publication status | Published - Sept 2005 |
Abstract
Today artificial neural networks are very useful to solve complex dynamic games of various types, i.e., to approximate optimal strategies with sufficient accuracy. Exemplarily four synthesis approaches for the solution of zero-sum, noncooperative dynamic games are outlined and discussed. Either value function, adjoint vector components or optimal strategies can be synthesized as functions of the state variables. In principle all approaches enable the solution of dynamic games. Nevertheless every approach has advantages and disadvantages which are discussed. The neural network training usually is very difficult and computationally very expensive. The coarse-grained parallelization FAUN 1.0-HPC-PVM of the advanced neurosimulator FAUN uses PVM subroutines and runs on heterogeneous and decentralized networks interconnecting general-purpose work-stations, PCs and also high-performance computers. Computing times of days, weeks or months can be cut down to hours. An enhanced cornered rat game - formulated and analyzed in 1993 - serves as an example. Optimal strategies for cat and rat are synthesized. For this purpose open-loop representations of optimal strategies on an equidistant grid in the state space are used. An important end game modification is presented.
Keywords
- Artificial neural networks, Cornered rat game, Dynamic games, Parallel computation, Synthesis of optimal strategies
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Business and International Management
- Computer Science(all)
- General Computer Science
- Decision Sciences(all)
- Statistics, Probability and Uncertainty
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In: International Game Theory Review, Vol. 7, No. 3, 09.2005, p. 347-365.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Coarse-grained parallelization of the advanced neurosimulator FAUN 1.0 with PVM and the enhanced cornered rat game revisited
AU - Von Mettenheim, Hans Jörg
AU - Breitner, Michael H.
N1 - Copyright: Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2005/9
Y1 - 2005/9
N2 - Today artificial neural networks are very useful to solve complex dynamic games of various types, i.e., to approximate optimal strategies with sufficient accuracy. Exemplarily four synthesis approaches for the solution of zero-sum, noncooperative dynamic games are outlined and discussed. Either value function, adjoint vector components or optimal strategies can be synthesized as functions of the state variables. In principle all approaches enable the solution of dynamic games. Nevertheless every approach has advantages and disadvantages which are discussed. The neural network training usually is very difficult and computationally very expensive. The coarse-grained parallelization FAUN 1.0-HPC-PVM of the advanced neurosimulator FAUN uses PVM subroutines and runs on heterogeneous and decentralized networks interconnecting general-purpose work-stations, PCs and also high-performance computers. Computing times of days, weeks or months can be cut down to hours. An enhanced cornered rat game - formulated and analyzed in 1993 - serves as an example. Optimal strategies for cat and rat are synthesized. For this purpose open-loop representations of optimal strategies on an equidistant grid in the state space are used. An important end game modification is presented.
AB - Today artificial neural networks are very useful to solve complex dynamic games of various types, i.e., to approximate optimal strategies with sufficient accuracy. Exemplarily four synthesis approaches for the solution of zero-sum, noncooperative dynamic games are outlined and discussed. Either value function, adjoint vector components or optimal strategies can be synthesized as functions of the state variables. In principle all approaches enable the solution of dynamic games. Nevertheless every approach has advantages and disadvantages which are discussed. The neural network training usually is very difficult and computationally very expensive. The coarse-grained parallelization FAUN 1.0-HPC-PVM of the advanced neurosimulator FAUN uses PVM subroutines and runs on heterogeneous and decentralized networks interconnecting general-purpose work-stations, PCs and also high-performance computers. Computing times of days, weeks or months can be cut down to hours. An enhanced cornered rat game - formulated and analyzed in 1993 - serves as an example. Optimal strategies for cat and rat are synthesized. For this purpose open-loop representations of optimal strategies on an equidistant grid in the state space are used. An important end game modification is presented.
KW - Artificial neural networks
KW - Cornered rat game
KW - Dynamic games
KW - Parallel computation
KW - Synthesis of optimal strategies
UR - http://www.scopus.com/inward/record.url?scp=25444531167&partnerID=8YFLogxK
U2 - 10.1142/S0219198905000569
DO - 10.1142/S0219198905000569
M3 - Article
AN - SCOPUS:25444531167
VL - 7
SP - 347
EP - 365
JO - International Game Theory Review
JF - International Game Theory Review
SN - 0219-1989
IS - 3
ER -