Advertisement

An evolutionary model of multi-agent systems

  • Bengt Carlsson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1087)

Abstract

A multi-agent system can be viewed as an evolutionary system where each individual (agent) acts on a basis of population dynamics and stability under the criterion of Darwinian fitness. This “survival of the fittest” or “best for individual (or gene or agent)” argument can be used to find evolutionary stable strategies within the multiagent system. The aim of this paper is to introduce some of these evolutionary ideas to the multi-agent society.

The differences between a product maximising mechanism (PMM) and an evolutionary stable strategy (ESS) are discussed. In a PMM the utility for an agent, in a mixed joint plan, is the positive difference between the maximum expected cost that the agent is willing to pay in order to achieve his goal, and his expected part of the outcome. In an ESS the fitness of the utility function does not have to be positive because zero fitness is a state which can be both improved and weakened. Both PMM and ESS are, according to game theory, individual rational and pareto optimal but they address different kinds of problems. If we know the maximum expected costs that each agent is willing to pay to achieve his goal, it is possible to use a PMM. If we instead know the agent's zero fitness, which is assumed to be the same for all agents, it is possible to use an ESS.

As an example, a solution to the telephone call competition among two agents with an overbid-underbid strategy (or hawk-dove strategy in the terminology used by evolutionary biologists) is demonstrated by using an ESS. This solution is compared with one in which the best bid wins and is paid the second price.

In an extended asymmetric game between two competing agents, one mixed and one pure evolutionary stable strategy are found. It is proposed that both these strategies will bring the competing agents near the “real” value and accordingly can be alternatives to the best bid wins, get second price idea.

Keywords

Multi-Agent System (MAS) Evolutionary Stable Strategy (ESS) Product Maximising Mechanisms (PMM) Distributed Artificial Intelligence (DAI) Game Theory Agent Interaction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. R. Axelrod, The evolution of co-operation, New York: Basic Books, 1984.Google Scholar
  2. R. Axelrod and W.D. Hamilton, “The evolution of co-operation”, Science vol. 211, 1981.Google Scholar
  3. R. Dawkins, The Selfish Gene, Oxford University Press, 1976.Google Scholar
  4. R. Dawkins, The Extended Phenotype, W. H. Freeman and Company, 1982.Google Scholar
  5. D. Fudenberg and J. Tirole, Game Theory, MIT Press, 1991.Google Scholar
  6. M. Genesereth and M. Ginsberg and J. Rosenschein “Co-operation without communication”, in Bond and Gasser Readings in Distributed Artificial Intelligence, Morgan Kaufmann, 1988.Google Scholar
  7. R. D. Luce and H. Raiffa, Games and decisions, Dover Publications Inc, 1957.Google Scholar
  8. J. Maynard Smith and G.R. Price, “The logic of animal conflict”, Nature vol. 246, 1973.Google Scholar
  9. J. Maynard Smith, Evolution and the theory of games, Cambridge University Press, 1982.Google Scholar
  10. J. Maynard Smith, Evolutionary Genetics, Oxford University Press, 1989.Google Scholar
  11. J. F. Nash, “The bargaining problem”, Econometrica 28, 1950.Google Scholar
  12. J. F. Nash, “Two-person cooperative games”, Econometrica 21, 1953.Google Scholar
  13. J. Rosenschein and M. Genesereth, “Deals among rational agents”, in Readings in Distributed Artificial Intelligence, Morgan Kaufmann, 1988.Google Scholar
  14. J. Rosenschein and G. Zlotkin, “Designing conventions for automated negotiation”, AI Magazine, 1994a. Google Scholar
  15. J. Rosenschein and G. Zlotkin, Rules of Encounter, MIT Press, 1994b.Google Scholar
  16. E. O. Wilson, Sociobiology-The abridged edition, Belknap Press 1980.Google Scholar
  17. K Wärneryd, Language, “Evolution, and the Theory of Games”, in Casti and Karlqvist Co-operation & Conflict in General Evolutionary Processes, Wiley-Interscience, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Bengt Carlsson
    • 1
    • 2
  1. 1.Department of Computer Science and Business AdministrationUniversity of Karlskrona/RonnebyRonnebySweden
  2. 2.Lund University Cognitive ScienceLundSweden

Personalised recommendations