Abstract
The paper deals with noncooperative games in which each player has some number of criteria measuring his payoff. A decision support system is considered as a computer-based tool that allows the players to make an analysis of the conflict situation, taking into account their preferences. The analysis can be done using an interactive, learning procedure utilizing methods of multicriteria optimization. An algorithm supporting analysis of payoffs in the multicriteria game and derivation of the best response strategies satisfying preferences of the players is proposed. The reference point approach with application of the respected achievement function is used in the interactive procedure in which payoffs of players are calculated closely to their preferences. The algorithm utilizes new theoretical results of the theory of noncooperative games. The results presented in the form of theorems include parametric characterization of the multicriteria gains representing preferences of the players and show relations among equilibria in the multicriteria games and the respective classical games.
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KruĊ, L. (2018). On Computer-Based Support in Noncooperative Multicriteria Games. In: Atanassov, K., et al. Uncertainty and Imprecision in Decision Making and Decision Support: Cross-Fertilization, New Models and Applications. IWIFSGN 2016. Advances in Intelligent Systems and Computing, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-319-65545-1_12
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DOI: https://doi.org/10.1007/978-3-319-65545-1_12
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