The mechanisms of analogical learning
Similarity and analogical reasoning
A validation-structure-based theory of plan modification and reuse
Artificial Intelligence
Learning by analogical reasoning in general problem-solving
Learning by analogical reasoning in general problem-solving
Massively parallel matching of knowledge structures
Massively parallel artificial intelligence
Protein Sequencing Experiment Planning Using Analogy
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
Lazy Incremental Learning of Control Knowledge for EfficientlyObtaining Quality Plans
Artificial Intelligence Review - Special issue on lazy learning
A Survey on Case-Based Planning
Artificial Intelligence Review
Back-End Technology for High-Performance Knowledge-Representation Systems
IEEE Intelligent Systems
Case-Based Reasoning in Course Timetabling: An Attribute Graph Approach
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Extensionally defining principles and cases in ethics: an AI model
Artificial Intelligence - Special issue on AI and law
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The Caper case-based planner uses the massive parallelism of the Connection Machine to quickly retrieve cases and plans from a large, unindexed memory. The system can retrieve cases and plans based on any feature of the target problem, including abstractions of target features. By controlling which features are part of the retrieval probe and their level of abstraction, a wide range of queries can be issued. The more specific the query, the closer the retrieved cases are to the current problem. Unlike serial planners, Caper can afford to retrieve several plans to achieve different parts of the target problem, and then merge them into a composite plan that solves most of the target goals with less adaptation. We are testing Caper's case-retrieval components in two domains: car assembly and transportation logistics.