Janet L. Kolodner
Springer Science & Business Media, Apr 30, 1993 - Computers - 171 pages
Case-based reasoning means reasoning based on remembering previous experiences. A reasoner using old experiences (cases) might use those cases to suggest solutions to problems, to point out potential problems with a solution being computed, to interpret a new situation and make predictions about what might happen, or to create arguments justifying some conclusion. A case-based reasoner solves new problems by remembering old situations and adapting their solutions. It interprets new situations by remembering old similar situations and comparing and contrasting the new one to old ones to see where it fits best. Case-based reasoning combines reasoning with learning. It spans the whole reasoning cycle. A situation is experienced. Old situations are used to understand it. Old situations are used to solve a problem (if there is one to be solved). Then the new situation is inserted into memory alongside the cases it used for reasoning, to be used another time.
The key to this reasoning method, then, is remembering. Remembering has two parts: integrating cases or experiences into memory when they happen and recalling them in appropriate situations later on. The case-based reasoning community calls this related set of issues the indexing problem. In broad terms, it means finding in memory the experience closest to a new situation. In narrower terms, it can be described as a two-part problem:
What people are saying - Write a review
We haven't found any reviews in the usual places.
Other editions - View all
abstract XP action actor agent algorithm analogical anomaly ANON applied approach AQUA AQUA's Artificial Intelligence base blackmail boxl Carbonell Carnegie Mellon University Case-based planning case-based reasoning causal Cognitive Science Society complex Computer Science concentrations conjuncts content theory cost current situation decision derivational analogy described detectors discriminate domain domain theory elaborated example execution execution-time experience EXPLAINS node explanation pattern explanation-based learning explanatory f-values failure feature extraction figure function Hammond heuristic search hypothesis incremental indices inference inference rules initial inrooB instantiated knowledge structures Kolodner labeling terms locB Machine Learning memory Morgan Kaufmann NoLiMiT novel obj2 operator opportunities optimal path performance planner preconditions predictive problem solver question refined relevant replay represent retrieval reuse S-COTTAGE Schank Seven-Eleven similarity metric solution solving specific steps story subgoaling suicide bombing suspended goals task theory tion treatment train TRUCKER trucks understanding Veloso XP-ASSERTED-NODES