On Automated Discovery of Models Using Genetic Programming: Bargaining in a Three-Agent Coalitions Game

The creation of mathematical, as well as qualitative (or rule-based), models is difficult, time-consuming, and expensive. Recent developments in evolutionary computation hold out the prospect that, for many problems of practical import, machine learning techniques can be used to discover useful models automatically. The prospects are particularly bright, we believe, for such automated discoveries in the context of game theory. This paper reports on a series of successful experiments in which we used a genetic programming regime to discover high-quality negotiation policies. The game-theoretic context in which we conducted these experiments-- a three-player coalitions game with sidepayments--is considerably more complex and subtle than any reported in the previous literature on machine learning applied to game theory.

[1]  Garett O. Dworman,et al.  Games computers play: simulating characteristic function game playing agents with classifier systems , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[2]  李幼升,et al.  Ph , 1989 .

[3]  J. Nash.  NON-COOPERATIVE GAMES , 1951, Classics in Game Theory.

[4]  Teck-Hua Ho,et al.  Finite automata play repeated prisoner's dilemma with information processing costs , 1996 .

[5]  David M. Kreps,et al.  Rational cooperation in the finitely repeated prisoners' dilemma , 1982 .

[6]  John C. Harsanyi,et al.  Общая теория выбора равновесия в играх / A General Theory of Equilibrium Selection in Games , 1989 .

[7]  R. Selten.  A Noncooperative Model of Characteristic-Function Bargaining , 1988 .

[8]  Vidroha Debroy,et al.  Genetic Programming , 1998, Lecture Notes in Computer Science.

[9]  J. H. Miller,et al.  AUCTIONS WITH ADAPTIVE ARTIFICIALLY INTELLIGENT AGENTS , 1990 .

[10]  Richard Roll,et al.  Strategies for Pairwise Competition in Markets and Organizations , 1981 .

[11]  R. Selten.  Reexamination of the perfectness concept for equilibrium points in extensive games , 1975, Classics in Game Theory.

[12]  D. E. Goldberg,et al.  Optimization and Machine Learning , 2022 .

[13]  Amnon Rapoport,et al.  Test of The Bargaining Set and Kernel Models in Three-Person Games , 1990 .

[14]  Daniel L. Stein,et al.  Lectures In The Sciences Of Complexity , 1989 .

[15]  D. E. Matthews.  Evolution and the Theory of Games , 1977 .

[16]  James D. Laing.  A Noncooperative Model of Bargaining in Simple Spatial Games , 1991 .

[17]  Ellen R. McGrattan,et al.  Money as a medium of exchange in an economy with artificially intelligent agents , 1990 .

[18]  Robert J. Aumann,et al.  Essays in game theory and mathematical economics in honor of Oskar Morgenstern , 1981 .

[19]  W. Hamilton,et al.  The evolution of cooperation. , 1984, Science.

[20]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[21]  John H. Holland,et al.  Induction: Processes of Inference, Learning, and Discovery , 1987, IEEE Expert.

[22]  Colin Camerer.  Behavioral Game Theory , 1990 .

[23]  Robert W. Blanning,et al.  Decision support and knowledge-based systems , 1989 .

[24]  John R. Koza,et al.  Genetic Programming II , 1992 .

[25]  J. K. Kinnear,et al.  Advances in Genetic Programming , 1994 .

[26]  A. Rapoport.  Game theory as a theory of conflict resolution , 1974 .

[27]  J. Kagan,et al.  Rational choice in an uncertain world , 1988 .

[28]  Garett O. Dworman,et al.  On automated discovery of models using genetic programming in game-theoretic contexts , 1995, Proceedings of the Twenty-Eighth Annual Hawaii International Conference on System Sciences.

[29]  John R. Koza,et al.  Genetic programming (videotape): the movie , 1992 .

[30]  Reinhard Selten.  Models Of Strategic Rationality , 1988 .

[31]  James D. Laing,et al.  Prominence, Competition, Learning, and the Generation of Offers in Computer-Aided Experimental Spatial Games , 1991 .

[32]  A. Rubinstein,et al.  The Structure of Nash Equilibrium in Repeated Games with Finite Automata (Now published in Econometrica, 56 (1988), pp.1259-1282.) , 1986 .

[33]  Peter J. Angeline,et al.  Genetic programming and emergent intelligence , 1994 .

[34]  M. Shubik.  Game theory in the social sciences: Concepts and solutions , 1982 .

[35]  Lawrence Davis,et al.  Genetic Algorithms and Simulated Annealing , 1987 .

[36]  John R. Koza,et al.  Genetic programming II (videotape): the next generation , 1994 .

[37]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[38]  John H. Miller,et al.  The coevolution of automata in the repeated Prisoner's Dilemma , 1996 .

[39]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[40]  Robert J. Aumann,et al.  Perspectives on Bounded Rationality , 1992, TARK.

[41]  Elizabeth C. Hirschman,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.