Quantitative results concerning the utility of explanation-based learning☆
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Cited by (193)
CPCES: A planning framework to solve conformant planning problems through a counterexample guided refinement
2020, Artificial IntelligenceDynamic maintenance case base using knowledge discovery techniques for case based reasoning systems
2020, Theoretical Computer ScienceCitation Excerpt :Other researchers have been interested in solving the problem of CBR system's efficiency which can be defined as the average time for solving a problem [9]. We start this Section by talking about the traditional CBM methods such as random deletion policy [10], ironically policy and utility deletion [11]. The drawback of these methods is that the competence of the reduced case base is not always guaranteed to be preserved.
Explanation-based learning with analogy for impasse resolution
2016, Expert Systems with ApplicationsCase-based maintenance: Structuring and incrementing the case base
2015, Knowledge-Based SystemsMemory and forgetting: An improved dynamic maintenance method for case-based reasoning
2014, Information SciencesCitation Excerpt :Therefore, in order to improve the overall performance of the system, the establishment of a suitable case base maintenance method is urgently needed which can control the growth rate of case base while maintaining a high accuracy of problem solving. Many case base maintenance methods have been proposed, and these can be divided into efficiency-oriented methods [3,18] and performance-oriented [11,22–24] methods. For the former, a simple policy is random deletion.
Evaluating Case-Base Maintenance algorithms
2014, Knowledge-Based SystemsCitation Excerpt :Although the learning ability of CBR is an important advantage, this characteristic is not free of drawbacks. For example, the addition of new cases to the case-base could degrade a system’s performance [21,38,51] because case-bases with many cases need a long time to retrieve cases similar to an input query. Some reasons for increase in retrieval time are the scalability of the data structures that represent the case-base, and the case descriptions for both problem and solution.
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Revised version of the paper that won the Artificial Intelligence Journal Best Paper Award at AAAI-88, St. Paul, MN.
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Current address: Sterling Federal Systems, Artificial Intelligence Branch, NASA Ames Research Center, Mail Stop 244-17, Moffett Field, CA 94035, USA.