INTEGRATING CASE BASED REASONING AND EXPLANATION BASED LEARNING IN AN APPRENTICE AGENT

Lei Wang, Tetsuo Sawaragi, Yajie Tian, Yukio Horiguchi

2010

Abstract

The problem in applications of case based reasoning (CBR) is its utility problem, that is, the cost of retrieving the most appropriate case from the case library for a new given problem and the cost of adapting the retrieved case for solving the new given problem. This paper proposes an approach to solve the utility problem of CBR by integrating CBR and explanation based learning (EBL) from a perspective that emphasizes the function of learning in CBR. In this paper, CBR and EBL are integrated in an apprentice agent, and the application of this apprentice agent in the robotic assembly domain is given as an example.

References

  1. Aamodt, A. and Plaza, E., 1994. Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Communications, Vol. 7: 1, pp. 39-59.
  2. Armengol, E., Ontanon, S. and Plaza, E., 2004. Explaining similarity in CBR. ECCBR 2004 Workshop Proceedings.
  3. Armengol, E., 2007. Usages of generalization in casebased reasoning. Lecture Notes In Artificial Intelligence, Vol. 4626. Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development.
  4. DeJong, G. and Mooney, R., 1986. Explanation-based learning: an alternative view. Machine Learning, Vol.1, pp.145-176.
  5. DeJong, G., 2006. Toward robust real-world inference: a new perspective on explanation-based learning. ECML 2006.
  6. Iglezakis, I., Reinartz, T. and Roth-Berghofer, T., 2004. Maintenance memories: beyond concepts and techniques for case base maintenance. In Proceedings of the Seventh European Conference on Case-Based Reasoning. Berlin: Springer, pp. 227-241.
  7. Mantaras, R., et al., 2006. Retrieval, reuse, revision and retention in case-based reasoning. The Knowledge Engineering Review, Vol.20:3, pp. 215-240.
  8. Mitchell, T., Keller, R. and Kedar-Cabelli, S., 1986. Explanation-based generalization: a unifying view. Machine Learning, Vol.1, pp.47-80.
  9. Wang, L., Tian, Y. and Sawaragi, T., 2008. Explanationbased manipulator learning: acquisition of assembling technique through observation. Proceedings of the 17th World Congress of IFAC, pp.2412-2417.
  10. Wilson, D. and Leake, D., 2001. Maintaining cased-based reasoners: dimensions and directions. Computational Intelligence, Vol.17:2, pp.196-213.
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Paper Citation


in Harvard Style

Wang L., Sawaragi T., Tian Y. and Horiguchi Y. (2010). INTEGRATING CASE BASED REASONING AND EXPLANATION BASED LEARNING IN AN APPRENTICE AGENT . In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-674-021-4, pages 667-670. DOI: 10.5220/0002763306670670


in Bibtex Style

@conference{icaart10,
author={Lei Wang and Tetsuo Sawaragi and Yajie Tian and Yukio Horiguchi},
title={INTEGRATING CASE BASED REASONING AND EXPLANATION BASED LEARNING IN AN APPRENTICE AGENT},
booktitle={Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2010},
pages={667-670},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002763306670670},
isbn={978-989-674-021-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - INTEGRATING CASE BASED REASONING AND EXPLANATION BASED LEARNING IN AN APPRENTICE AGENT
SN - 978-989-674-021-4
AU - Wang L.
AU - Sawaragi T.
AU - Tian Y.
AU - Horiguchi Y.
PY - 2010
SP - 667
EP - 670
DO - 10.5220/0002763306670670