Abstract
In this paper we present an algorithm called DerUCP, which can be regarded as a general model for plan adaptation using Derivational Analogy. Using DerUCP, we show that previous results on the complexity of plan adaptation do not apply to Derivational Analogy. We also show that Derivational Analogy can potentially produce exponential reductions in the size of the search space generated by a planning system.
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References
Veloso, M., Carbonell, J.: Derivational analogy in PRODIGY: Automating case acquisition, Storage and Utilization. Machine Learning (1993) 249–278
Ihrig, L., Kambhampati, S.: Plan-space vs. State-space planning in reuse and replay. Technical report, Arizona State University (1996)
Muñoz-Avila, H.: Case-Base Maintenance by Integrating Case Index Revision and Case Retention Policies in a Derivational Replay Framework. Computational Intelligence 17 (2001)
Nebel, B., Koehler, J.: Plan reuse versus plan generation: a theoretical and empirical analysis. Artificial Intelligence 76 (1995) 427–454
Kambhampati, S., Srivastava, B.: Unifying Classical Planning Approaches. Technical report, Arizona State University (1996)
Fikes, R., Hart, P., Nilsson, N.: Learning and executing generalized robot plans. Artificial Intelligence 3 (1972) 251–288
Carbonell, J.G.: Derivational analogy: A theory of reconstructive problem solving and expertise acquisition. Machine Learning (1986)
Bhansali, S., IIarandi, M.T.: When (not) to Use Derivational Analogy: Lessons Learned Using APU. In Aha, D., ed.: Proceeding of AAAI-94 Workshop: Case-based Reasoning. (1994)
Blumenthal, B., Porter, B.: Analysis and Empirical Studies of Derivational Analogy. Artificial Intelligence 67 (1994) 287–328
Finn, D., Slattery, S., Cunningham, P.: Modelling of Engineering Thermal Problems-An implementation using CBR with Derivational Analogy. In: Proceedings of EWCBR’93, Springer-Verlag (1993)
Ihrig, L., Kambhampati, S.: Derivation Replay for Partial-Order Planning. AAAI-1994 (1994)
Muñoz-Avila, H., Weberskirch, F.: Planning for Manufacturing Workpieces by Storing, Indexing and Replaying Planning Decisions. Proc. 3rd Int. Conference on AI Planning Systems (AIPS-96) (1996)
Muñoz-Avila, H., Paulokat, J., Wess, S.: Controlling Nonlinear Hierarchical Planning by Case Replay. In: Proceedings the 2nd European Workshop on Case-Based Reasoning (EWCBR-94). (1994) 195–203
Bylander, T.: The Computational Complexity of Propositional STRIPS Planning. Artificial Intelligence 69 (1994) 165–204
Kambhampati, S.: Exploiting causal structure to control retrieval and refitting during plan reuse. Computational Intelligence 10 (1994) 213–244
Hanks, S., Weld, D.S.: A Domain-Independent Algorithm for Plan Adaptation. Journal of Artificial Intelligence Research 2 (1995) 319–360
Korf, R.: Planning as Search: A Quantitative Approach. Artificial Intelligence 33 (1987) 65–88
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© 2002 Springer-Verlag Berlin Heidelberg
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Au, TC., Muñoz-Avila, H., Nau, D.S. (2002). On the Complexity of Plan Adaptation by Derivational Analogy in a Universal Classical Planning Framework. In: Craw, S., Preece, A. (eds) Advances in Case-Based Reasoning. ECCBR 2002. Lecture Notes in Computer Science(), vol 2416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46119-1_3
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DOI: https://doi.org/10.1007/3-540-46119-1_3
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