Fast planning through planning graph analysis
Artificial Intelligence
Learning to Predict by the Methods of Temporal Differences
Machine Learning
MORALE. Mission ORiented Architectural Legacy Evolution
ICSM '97 Proceedings of the International Conference on Software Maintenance
Prodigy/Analogy: Analogical Reasoning in General Problem Solving
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Meta-Cases: Explaining Case-Based Reasoning
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Learning to Improve Case Adaption by Introspective Reasoning and CBR
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
A model-based approach to blame assignment: revising the reasoning steps of problem solvers
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Toward a Unified Catalog of Implemented Cognitive Architectures
Proceedings of the 2010 conference on Biologically Inspired Cognitive Architectures 2010: Proceedings of the First Annual Meeting of the BICA Society
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It is useful for an intelligent software agent to be able to adapt to new demands from an environment. Such adaptation can be viewed as a redesign problem; an agent has some original functionality but the environment demands an agent with a slightly different functionality, so the agent redesigns itself. It is possible to take a case-based approach to this redesign task. Furthermore, one class of agents which can be amenable to redesign of this sort is case-based reasoners. These facts suggest the notion of "meta-case-based reasoning," i.e., the application of case-based redesign techniques to the problem of adapting a case-based reasoning process. Of course, meta-case-based reasoning is a very broad topic. In this paper we focus on a more specific issue within meta-case-based reasoning: balancing the use of relatively efficient but knowledge intensive symbolic techniques with relatively flexible but computationally costly numerical techniques. In particular, we propose a mechanism whereby qualitative functional models are used to efficiently propose a set of design alternatives to specific elements within a meta-case and then reinforcement learning is used to select among these alternatives. We describe an experiment in which this mechanism is applied to a case-based disassembly agent. The results of this experiment show that the combination of model-based adaptation and reinforcement learning can address meta-case-based reasoning problems which are not effectively addressed by either approach in isolation.