Title

Building High-Performing Human-Like Tactical Agents Through Observation and Experience

Authors

Authors

G. Stein;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

Experiential learning; FALCONET; haptics; machine learning; multimodal; observational learning; PIGEON; NEURAL NETWORKS; Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics

Abstract

This paper describes a two-phase approach for automating the agent-building process when the agent is to perform tactical tasks. The research is inspired by how humans learn-first by observation of a teacher's performance and then by practicing the performance themselves. The objectives of this approach are to produce a high-performing agent that 1) approaches or exceeds the proficiency of a human and 2) does so in a human-like manner. We accomplish these objectives by combining observational learning with experiential learning. These processes are executed sequentially, with the former creating a competent but somewhat limited human-like model from scratch, and the latter improving its performance without significantly eroding its human-like qualities. The process is described in detail, and test results confirming our hypothesis are described.

Journal Title

Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics

Volume

41

Issue/Number

3

Publication Date

1-1-2011

Document Type

Article

Language

English

First Page

792

Last Page

804

WOS Identifier

WOS:000290734400016

ISSN

1083-4419

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