Hierarchical HMM-based failure isolation for cognitive robots

Dogan Altan, Sanem Sariel-Talay

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Robots execute their planned actions in the physical world to accomplish their goals. However, since the real world is partially observable and dynamic, failures may occur during the execution of their actions. These failures should be detected immediately, and the underlying reasons of these failures should be isolated to ensure robustness. In this paper, we propose a probabilistic and temporal model-based failure isolation method that maintains Hierarchical Hidden Markov Models (HHMMs) in order to represent and reason about different failure types. The underlying reason of a failure can be isolated efficiently by multi-hypothesis tracking.

Original languageEnglish
Title of host publicationICAART 2014 - Proceedings of the 6th International Conference on Agents and Artificial Intelligence
PublisherSciTePress
Pages299-304
Number of pages6
ISBN (Print)9789897580161
Publication statusPublished - 2014
Event6th International Conference on Agents and Artificial Intelligence, ICAART 2014 - Angers, Loire Valley, France
Duration: 6 Mar 20148 Mar 2014

Publication series

NameICAART 2014 - Proceedings of the 6th International Conference on Agents and Artificial Intelligence
Volume2

Conference

Conference6th International Conference on Agents and Artificial Intelligence, ICAART 2014
Country/TerritoryFrance
CityAngers, Loire Valley
Period6/03/148/03/14

Keywords

  • Cognitive robots
  • Hierarchical hidden markov models
  • Model-based diagnosis
  • Probabilistic failure isolation
  • Uncertain reasoning

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