A Dynamic-Bayesian Network framework for modeling and evaluating learning from observation

    Authors

    S. Ontanon; J. L. Montana;A. J. Gonzalez

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Expert Syst. Appl.

    Keywords

    Learning from observation; Dynamic Bayesian Networks; HUMAN-BEHAVIOR; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic; Operations Research & Management Science

    Abstract

    Learning from observation (LfO), also known as learning from demonstration, studies how computers can learn to perform complex tasks by observing and thereafter imitating the performance of a human actor. Although there has been a significant amount of research in this area, there is no agreement on a unified terminology or evaluation procedure. In this paper, we present a theoretical framework based on Dynamic-Bayesian Networks (DBNs) for the quantitative modeling and evaluation of LfO tasks. Additionally, we provide evidence showing that: (1) the information captured through the observation of agent behaviors occurs as the realization of a stochastic process (and often not just as a sample of a state-to-action map); (2) learning can be simplified by introducing dynamic Bayesian models with hidden states for which the learning and model evaluation tasks can be reduced to minimization and estimation of some stochastic similarity measures such as crossed entropy. (C) 2014 Elsevier Ltd. All rights reserved.

    Journal Title

    Expert Systems with Applications

    Volume

    41

    Issue/Number

    11

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    5212

    Last Page

    5226

    WOS Identifier

    WOS:000336191800019

    ISSN

    0957-4174

    Share

    COinS