Learning tactical human behavior through observation of human performance

    Authors

    H. K. G. Fernlund; A. J. Gonzalez; M. Georgiopoulos;R. F. DeMara

    Comments

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    Abbreviated Journal Title

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

    Keywords

    context-based reasoning; human behavioral modeling; genetic programming; simulation; KNOWLEDGE; Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics

    Abstract

    It is widely accepted that the difficulty and expense involved in acquiring the knowledge behind tactical behaviors has been one limiting factor in the development of simulated agents representing adversaries and teammates in military and game simulations. Several researchers have addressed this problem with varying degrees of success. The problem mostly lies in the fact that tactical knowledge is difficult to elicit and represent through interactive sessions between the model developer and the subject matter expert. This paper describes a novel approach that employs genetic programming in conjunction with context-based reasoning to evolve tactical agents based upon automatic observation of a human performing a mission on a simulator. In this paper, we describe the process used to carry out the learning. A prototype was built to demonstrate feasibility and it is described herein. The prototype was rigorously and extensively tested. The evolved agents exhibited good fidelity to the observed human performance, as well as the capacity to generalize from it.

    Journal Title

    Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics

    Volume

    36

    Issue/Number

    1

    Publication Date

    1-1-2006

    Document Type

    Article

    Language

    English

    First Page

    128

    Last Page

    140

    WOS Identifier

    WOS:000234882600011

    ISSN

    1083-4419

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