Title

Discovery Of High-Level Behavior From Observation Of Human Performance In A Strategic Game

Keywords

Context-Based Reasoning (CxBR); Fuzzy ARTMAP (FAM); Learning from observation; Neural network; Poker; Template-based interpretation (TBI)

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. © 2008 IEEE.

Publication Date

6-1-2008

Publication Title

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

Volume

38

Issue

3

Number of Pages

855-874

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TSMCB.2008.922062

Socpus ID

44849115010 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/44849115010

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