Search
2000 Volume 15
Article Contents
RESEARCH ARTICLE   Open Access    

Game theoretic and decision theoretic agents

More Information
  • In the last few years, increasing numbers of members of the agent community have been adopting techniques from game theory and decision theory. Broadly speaking, decision theory (Raiffa, 1968) is a means of analysing which of a series of options should be taken when it is uncertain exactly what the result of taking the option will be. Decision theory concentrates on identifying the “best” decision option, where the notion of “best” is allowed to have a number of different meanings, of which the most common is that which maximises the expected utility of the decision maker. Game theory (Binmore, 1992) can be considered as a variant of decision theory in which the outcome of taking a particular decision is dependent upon the actions of another, frequently an opponent which is trying to maximise its own benefit at the cost of the decision maker. Alternatively, game theory can be considered a mechansim for analysing games between two players in which each gets to choose a move from some limited set of options and, depending on what both have chosen, each receives a payout. Since the payout one player receives depends upon the move made by the other then, to maximise its payout, each player needs to take into account the likely move taken by its opponent. From this perspective, decision theory can be considered to be the study of games played against nature, an opponent which does not look to gain the best payout, but rather acts randomly.
  • 加载中
  • Cite this article

    SIMON PARSONS, MICHAEL WOOLDRIDGE. 2000. Game theoretic and decision theoretic agents. The Knowledge Engineering Review. 15:16 doi: 10.1017/S0269888900001016
    SIMON PARSONS, MICHAEL WOOLDRIDGE. 2000. Game theoretic and decision theoretic agents. The Knowledge Engineering Review. 15:16 doi: 10.1017/S0269888900001016

Article Metrics

Article views(10) PDF downloads(135)

Other Articles By Authors

RESEARCH ARTICLE   Open Access    

Game theoretic and decision theoretic agents

The Knowledge Engineering Review  15 Article number: 10.1017/S0269888900001016  (2000)  |  Cite this article

Abstract: In the last few years, increasing numbers of members of the agent community have been adopting techniques from game theory and decision theory. Broadly speaking, decision theory (Raiffa, 1968) is a means of analysing which of a series of options should be taken when it is uncertain exactly what the result of taking the option will be. Decision theory concentrates on identifying the “best” decision option, where the notion of “best” is allowed to have a number of different meanings, of which the most common is that which maximises the expected utility of the decision maker. Game theory (Binmore, 1992) can be considered as a variant of decision theory in which the outcome of taking a particular decision is dependent upon the actions of another, frequently an opponent which is trying to maximise its own benefit at the cost of the decision maker. Alternatively, game theory can be considered a mechansim for analysing games between two players in which each gets to choose a move from some limited set of options and, depending on what both have chosen, each receives a payout. Since the payout one player receives depends upon the move made by the other then, to maximise its payout, each player needs to take into account the likely move taken by its opponent. From this perspective, decision theory can be considered to be the study of games played against nature, an opponent which does not look to gain the best payout, but rather acts randomly.

    • © 2000 Cambridge University Press
  • About this article
    Cite this article
    SIMON PARSONS, MICHAEL WOOLDRIDGE. 2000. Game theoretic and decision theoretic agents. The Knowledge Engineering Review. 15:16 doi: 10.1017/S0269888900001016
    SIMON PARSONS, MICHAEL WOOLDRIDGE. 2000. Game theoretic and decision theoretic agents. The Knowledge Engineering Review. 15:16 doi: 10.1017/S0269888900001016
  • Catalog

      /

      DownLoad:  Full-Size Img  PowerPoint
      Return
      Return