A Defense Model for Games with Incomplete Information

W.J. Jamroga

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    2 Citations (Scopus)

    Abstract

    Making a decision, an agent must consider how his outcome can be influenced by possible actions of other agents. A 'best 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.
    Original languageUndefined
    Title of host publicationKI 2001: Advances in Artificial Intelligence - Joint German/Austrian Conference on AI
    EditorsFranz Baader, Gerhard Brewka, Thomas Eiter
    Place of PublicationLondon
    PublisherSpringer
    Pages260-273
    Number of pages14
    ISBN (Print)3-540-42612-4
    DOIs
    Publication statusPublished - 2001

    Publication series

    NameLecture notes in artificial intelligence
    PublisherSpringer Verlag
    Number2174
    Volume2174
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Keywords

    • METIS-205753
    • HMI-IA: Intelligent Agents
    • IR-63340
    • EWI-6594

    Cite this

    Jamroga, W. J. (2001). A Defense Model for Games with Incomplete Information. In F. Baader, G. Brewka, & T. Eiter (Eds.), KI 2001: Advances in Artificial Intelligence - Joint German/Austrian Conference on AI (pp. 260-273). (Lecture notes in artificial intelligence; Vol. 2174, No. 2174). London: Springer. https://doi.org/10.1007/3-540-45422-5_19