Learning collaborative team behavior from observation

    Authors

    C. L. Johnson;A. J. Gonzalez

    Comments

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    Abbreviated Journal Title

    Expert Syst. Appl.

    Keywords

    Multi-agent system; Machine learning; Learning by observation; Context-based; MULTIAGENT SYSTEMS; HUMAN-PERFORMANCE; FRAMEWORK; CONTEXT; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic; Operations Research & Management Science

    Abstract

    This paper describes an approach to creating a simulated team of agents through observation of another team performing a collaborative task. Simulated human teamwork can be used for a number of purposes, such as automated teammates for training purposes and realistic opponents in games as well as in military training simulation. Current simulated teamwork representations require that the team member behaviors be manually programmed into the agents, often requiring much time and effort. None of the currently documented techniques for multi-agent learning employ observational learning and a context-aware framework to automatically build agents that replicate the collaborative behaviors observed. Machine learning techniques for learning from observation and learning by demonstration have proven successful at observing the behavior of humans or other software agents and creating a behavior function for a single agent. This technique described here known as COLTS combines current research in teamwork simulation and learning from observation to effectively train a multi-agent system capable of displaying effective team behavior in limited domains. The paper describes the background and the related work by others as well as a detailed description of the learning method. A prototype built to evaluate the developed approach as well as the extensive experimentation conducted is also described. The results indicate success in the selected experiments. (C) 2013 Elsevier Ltd. All rights reserved.

    Journal Title

    Expert Systems with Applications

    Volume

    41

    Issue/Number

    5

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    2316

    Last Page

    2328

    WOS Identifier

    WOS:000330600800020

    ISSN

    0957-4174

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