On the Relation between the Context of a
Feature and the Domain Theory in Case-Based
Planning

Hctor Muoz-Avila & Frank Weberskirch &
Thomas Roth-Berghofer

Centre for Learning Systems and Applications (LSA)
University of Kaiserslautern, Dept. of Computer Science
P.O. Box 3049, D-67653 Kaiserslautern, Germany
E-mail: {munioz|weberski|roth}@informatik.uni-kl.de



Abstract. Determining the context of a feature (i.e., the factors affecting 
the ranking of a feature within a case) has been subject of several
studies in analysis tasks, particularly for classification but not in synthesis 
tasks like planning. In this paper we will address this problem and
explain how the domain theory plays a key role in determining the context 
of a feature. We provide a characterization of the domain theory and
show that in domains meeting this characterization, the context can be
simplified. We also use explanation-based learning techniques to determine 
the context in domains not meeting the characterization. Our work
relates for the first time CBR, machine learning and planning theory to
determine the context of a feature.
References

1.	D. W. Aha and R. L. Goldstone. Learning attribute relevance in context in
instance-based learning algorithms. In Proceedings of the Twelfth Annual Conference 
of the Cognitive Science Society, pages pp 141-148, Cambridge, IN: Lawrence
Eribaum, 1990.
2.	A. Barrett and D.S. Weld. Partial-order planning: Evaluating possible efficiency
gains. Artificial Intelligence, 67(1) :71112, 1994.
3.	L. Ihrig and S. Kambhampati. Derivational replay for partial-order planning. In
Proceedings of AAAI-94, pages 116125, 1994.
4.	L. Ihrig and S. Kambhampati. Design and implementation of a replay framework
based on a partial order planner. In D. Weld, editor, Proceedings of AAAI-96. IOS
Press, 1996.
5.	5. Kambliampati, L. Ihrig, and B. Srivastava. A candidate set based analysis
of subgoal interactions in conjunctive goal planning. In Proceedings of the 3rd
International Conference on AI Planning Systems (AIPS-96), pages 125133, 1996.
6.	5. Kambliampati, S. Katukam, and Y. Qu. Failure driven dynamic search control
for partial order planners: An explanation-based approach. Artificial Intelligence,
88(1-2):253315, 1996.
7.	D. McAllester and D. Rosenblitt. Systematic nonlinear planning. In Proceedings
of AAAI-91, pages 634639, 1991.
8.	5. Minton. Learning Search Control Knowledge: An Explanation-Based Approach.
Kluwer Academic Publishers, Boston, 1988.
9.	H. Muoz-Avila and J. Hllen. Retrieving relevant cases by using goal dependencies. 
In M. Veloso and A. Aamodt, editors, Proceedings of the 1st International
Conference on Case-Based Reasoning (ICCBR-95), number 1010 in Lecture Notes
in Artificial Intelligence. Springer, 1995.
10.	H. Muoz-Avila and 3. Hllen. Feature weighting by explaining case-based planning 
episodes. In Third European Workshop (EWCBR-96), number 1168 in Lecture
Notes in Artificial Intelligence. Springer, 1996.
11.	H. Muoz-Avila and F. Weberskirch. A specification of the domain of process
planning: Properties, problems and solutions. Technical Report LSA-96-10E, Centre 
for Learning Systems and Applications, University of Kaiserslautern, Germany,
1996.
12.	F. Ricci and P. Avesani. Learning a local similarity metric for case-based reasoning. 
In Case-Based Reasoning Research and Development, Proceedings of the 1st
International Conference (ICCBR-95), Sesimbra, Portugal, 1995. Springer Verlag.
13.	T. Roth-Berghofer. Explanation-based learning of control information of failures
in planning. Masters thesis (in german), University of Kaiserslautern, 1996.
14.	P. D. Turney. The identification of context-sensitive features: A formal definition
of context for concept learning. In Proceedings of the ECML-96 Workshop on
Learning in Context-Sensitive Domains, 1996.
15.	M. Veloso. Planning and learning by analogical reasoning. Number 886 in Lecture
Notes in Artificial Intelligence. Springer Verlag, 1994.
