Title

Learning Tactical Human Behavior Through Observation Of Human Performance

Keywords

Context-based reasoning; Genetic programming; Human behavioral modeling; Simulation

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. © 2006 IEEE.

Publication Date

2-1-2006

Publication Title

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volume

36

Issue

1

Number of Pages

128-140

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TSMCB.2005.855568

Socpus ID

31744449611 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/31744449611

This document is currently not available here.

Share

COinS