Titlebook: Case-Based Learning; Janet L. Kolodner Book 1993 Springer Science+Business Media New York 1993 Action.C programming language.case-based re

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书目名称Case-Based Learning影响因子(影响力)




书目名称Case-Based Learning影响因子(影响力)学科排名




书目名称Case-Based Learning网络公开度




书目名称Case-Based Learning网络公开度学科排名




书目名称Case-Based Learning被引频次




书目名称Case-Based Learning被引频次学科排名




书目名称Case-Based Learning年度引用




书目名称Case-Based Learning年度引用学科排名




书目名称Case-Based Learning读者反馈




书目名称Case-Based Learning读者反馈学科排名

CAJ

Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilization, in some detail, and extensive results of the first full implementation are presented. These results show up to a large performance improvement in a simple transportation domain for structurally similar problems, and smaller improvements when less strict similarity metrics are used for problems that

撤退

Opportunism and Learning,lishes this by treating the behavior of intelligent agents as an ongoing attempt to discover, create, and maintain the stability that is necessary for the production of actions that satisfy our goals.

附录

https://doi.org/10.1007/978-1-4615-5021-1owledge in response to experiences in such situations. The theory has been implemented in a case-based story understanding program that can (a) learn a new case in situations where no case already exists, (b) learn how to index the case in memory, and (c) incrementally refine its understanding of th

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Dungeon

Dungeon

不可知论

ngside the cases it used for reasoning, to be usedanother time..The key to this reasoning method, then, is remembering. Rememberinghas two parts: integrating cases or experiences into memory when theyhappen and recalling them in appropriate situations later on. Thecase-based reasoning community calls this rel978-1-4613-6418-4978-1-4615-3228-6

一再遛

Book 1993t solutions to problems, to point outpotential problems with a solution being computed, to interpret a newsituation and make predictions about what might happen, or to createarguments justifying some conclusion. A case-based reasoner solves newproblems by remembering old situations and adapting thei

Barrister

Integrating Feature Extraction and Memory Search,make an abstract thematic feature valuable to a case-based reasoner, and recasts the problem of case retrieval into a framework under which a system can explicitly and dynamically reason about the cost of acquiring features relative to their information value.