Abstract
Making a decision, an agent must consider how his outcome can be influenced by possible actions of other agents. A’b est defense model’ for games involving uncertainty assumes usually that the opponents know everything about the actual situation and the player’s plans for certain. In this paper it’s argued that the assumption results in algorithms that are too cautious to be good in many game settings. Instead, a ’reasonably good defense’ model is proposed: the player should look for a best strategy against all the potential actions of the opponents, still assuming that any opponent plays his best according to his actual knowledge. The defense model is formalized for the case of two-player zero-sum (adversary) games. Also, algorithms for decision-making against ’reasonably good defense’ are proposed.The argument and the ideas are supported by the results of experiments with random zero-sum two-player games on binary trees.KeywordsMonte CarloIncomplete InformationMultiagent SystemGood DefenseGame TreeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.