Conference proceeding
A Dynamic Bayesian Network Framework for Learning from Observation
ADVANCES IN ARTIFICIAL INTELLIGENCE, CAEPIA 2013, v 8109, pp 373-382
01 Jan 2013
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.
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Details
- Title
- A Dynamic Bayesian Network Framework for Learning from Observation
- Creators
- Santiago Ontanon - Drexel UniversityJose Luis Montana - University of CantabriaAvelino J. Gonzalez - University of Central Florida
- Contributors
- C Bielza (Editor)A Salmeron (Editor)A AlonsoBetanzos (Editor)J I Hidalgo (Editor)L Martinez (Editor)A Troncoso (Editor)E Corchado (Editor)J M Corchado (Editor)
- Publication Details
- ADVANCES IN ARTIFICIAL INTELLIGENCE, CAEPIA 2013, v 8109, pp 373-382
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature
- Number of pages
- 10
- Grant note
- TIN2011-27479-C04-04 / spanish grant; Spanish Government
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000340401800038
- Scopus ID
- 2-s2.0-84885055497
- Other Identifier
- 991019170393704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods