hydroxyapatite
发表于 2025-3-21 17:16:00
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CAJ
发表于 2025-3-21 20:38:49
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
发表于 2025-3-22 03:20:04
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
发表于 2025-3-22 07:47:07
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
placebo-effect
发表于 2025-3-22 10:18:38
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Dungeon
发表于 2025-3-22 16:09:34
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Dungeon
发表于 2025-3-22 17:10:28
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不可知论
发表于 2025-3-23 01:05:49
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
发表于 2025-3-23 03:16:55
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
发表于 2025-3-23 07:20:45
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.