Title

A Dynamic Bayesian Network Framework For Learning From Observation

Abstract

Learning from Observation (a.k.a. learning from demonstration) studies how computers can learn to perform complex tasks by observing and thereafter imitating the performance of an expert. Most work on learning from observation assumes that the behavior to be learned can be expressed as a state-to-action mapping. However most behaviors of interest in real applications of learning from observation require remembering past states. We propose a Dynamic Bayesian Network approach to learning from observation that addresses such problem by assuming the existence of non-observable states. © 2013 Springer-Verlag.

Publication Date

10-10-2013

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

8109 LNAI

Number of Pages

373-382

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-642-40643-0_38

Socpus ID

84885055497 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84885055497

This document is currently not available here.

Share

COinS