Discovery of high-level behavior from observation of human performance in a strategic game

    Authors

    B. S. Stensrud;A. J. Gonzalez

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    IEEE Trans. Syst. Man Cybern. Part B-Cybern.

    Keywords

    Context-Based Reasoning (CxBR); fuzzy ARTMAP (FAM); learning from; observation; neural network; poker; template-based interpretation (TBI); CHESS; KNOWLEDGE; BRIDGE; Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics

    Abstract

    This paper explores the issues faced in creating a system that can learn tactical human behavior merely by observing a human perform the behavior in a simulation. More specifically, this paper describes a technique based on fuzzy ARTMAP (FAM) neural networks to discover the criteria that cause a transition between contexts during a strategic game simulation. The approach depends on existing context templates that can identify the high-level action of the human, given a description of the situation along with his action. The learning task then becomes the identification and representation of the context sequence executed by the human. In this paper, we present the FAM/Template-based Interpretation Learning Engine (FAMTILE). This system seeks to achieve this learning task by constructing rules that govern the context transitions made by the human. To evaluate FAMTILE, six test scenarios were developed to achieve three distinct evaluation goals: 1) to assess the learning capabilities of FAM; 2) to evaluate the ability of FAMTILE to correctly predict human and context selections, given an observation; and 3) more fundamentally, to create a model of the human's behavior that can perform the high-level task at a comparable level of proficiency.

    Journal Title

    Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics

    Volume

    38

    Issue/Number

    3

    Publication Date

    1-1-2008

    Document Type

    Article

    Language

    English

    First Page

    855

    Last Page

    874

    WOS Identifier

    WOS:000258763600022

    ISSN

    1083-4419

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