Game theory describes the conditions for the strategies of rational agents to form an equilibrium. However, game theory can fail from the prescriptive viewpoint and can serve only as a heuristic recommendation for agents. There exists a plethora of game theoretic solution concepts, however, their effectiveness has never been compared; hence, there is no guideline for selecting correct algorithm for a given domain. Therefore, we compare the effectiveness of solution-concept strategies and strategies computed by Counterfactual regret minimization (CFR) and Monte-Carlo tree search in practice. Our results show that (1) CFR strategies are typically the best, and (2) the effectiveness of the refinements of NE depends on the utility structure of the game.

Strategy effectiveness of game-theoretical solution concepts in extensive-form general-sum games

GATTI, NICOLA
2015

Abstract

Game theory describes the conditions for the strategies of rational agents to form an equilibrium. However, game theory can fail from the prescriptive viewpoint and can serve only as a heuristic recommendation for agents. There exists a plethora of game theoretic solution concepts, however, their effectiveness has never been compared; hence, there is no guideline for selecting correct algorithm for a given domain. Therefore, we compare the effectiveness of solution-concept strategies and strategies computed by Counterfactual regret minimization (CFR) and Monte-Carlo tree search in practice. Our results show that (1) CFR strategies are typically the best, and (2) the effectiveness of the refinements of NE depends on the utility structure of the game.
Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
9781450337717
9781450337717
Game theory; General-sum games; Solution concepts; Artificial Intelligence; Software; Control and Systems Engineering
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1005030
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