Title

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

Keywords

Experiential learning; FALCONET; haptics; machine learning; multimodal; observational learning; PIGEON

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 learnfirst 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. © 2010 IEEE.

Publication Date

6-1-2011

Publication Title

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

Volume

41

Issue

3

Number of Pages

792-804

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TSMCB.2010.2091955

Socpus ID

79957503210 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/79957503210

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