Complete classes of strategies for the classical iterated prisoner’s dilemma

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1447, Issue , 1998, Pages 33-41

  Beaufils, Bruno ^a   Delahaye, Jean Paul ^a   Mathieu, Philippe ^a  

^a UNIV LILLE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

EVOLUTION OF COOPERATION; GENETIC APPROACH; OBJECTIVE METHODS;

GENETIC PROGRAMMING;

EID: 84947706252     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0040757     Document Type: Conference Paper

References (8)

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3. M. M. Flood. Some experimental games. Technical report, RAND Corporation.
4. K. Lindgren. Evolutionary phenomena in simple dynamics. In C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen, editors, Artificial Life II: Proceedings of the Second Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems, volume 10 of Santa Fe Institute Studies in the Sciences of Complexity, pages 295-312, Reading, MA, USA, 1992. Addisson-Wesley.
5. W. Poundstone. Prisoner’s Dilemma : John von Neumann, Game Theory, and the Puzzle of the Bomb. Oxford University Press, Oxford, UK, 1993.
6. A. Salhi, H. Glaser, D. D. Roure, and J. Putney. The prisoner’s dilemma revisited. Technical Report DSSE-TR-96-2, University of Southhampton, Department of Electronics and Computer Science, Declarative Systems and Software Engineering Group, Southampton, UK, March 1996.
7. T. W. Sandholm and R. H. Crites. Multiagent reinforcement learning in the iterated prisoner’s dilemma. BioSystems, 37(1,2): 147-166, 1996.
8. J. von Neumann and O. Morgenstern. Theory of Games and Economics Behavior. Princeton University Press, Princeton, NJ, USA, 1944.