Planning and Learning by Analogical Reasoning
@inproceedings{Veloso1994PlanningAL, title={Planning and Learning by Analogical Reasoning}, author={Manuela M. Veloso}, booktitle={Lecture Notes in Computer Science}, year={1994} }
Overview.- The problem solver.- Generation of problem solving cases.- Case storage: Automated indexing.- Efficient case retrieval.- Analogical replay.- Empirical results.- Related work.- Conclusion.
370 Citations
Implementation of an analogical reasoning system on a parallel recirculating computer architecture
- Computer Science, Biology2010 IEEE Aerospace Conference
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An approach is described for implementing the SAGE™ analogical reasoning system on a parallel recirculating computer architecture to increase the efficiency of planning, infer missing structure in test cases, and classify cases across domains.
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This paper presents preliminary ideas of our work for automated learning of Hierarchical Goal Networks in nondeterministic domains. We are currently implementing the ideas expressed in this paper.
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This paper defines a “simplification problem”, then describes the approach to solving design simplification problems by analogical reasoning, and draws conclusions and proposes directions for future research.
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The paper describes some peculiarities of proof planning and discusses some possible cross-fertilization of planning and proof planning.
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This paper presents a definition of simplification and some examples, and describes a prototype system that has been built to support the preliminary investigations of this problem.
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This work formalizes the notion that goal formulation and goal change are themselves major parts of the problem-solving process and includes in the model not just planning and plan execution but also interpretation of the environment as plans are executed and exogenous events occur.
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We briefly examine case-based planning starting with the seminal work of Hammond. Derivational analogy represents an important shift of technical emphasis that helped mature the techniques. The…
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It is shown that the case-based planning approach can be an effective alternative to plan generation when similar reuse candidates can be chosen.