Experimental study of an evaluation function for cases imperfectly explained

Bento, Carlos; Exposto, José; Francisco, Víctor; Costa, Ernesto

doi:10.1007/3-540-60364-6_25

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Figure 8adepicts this situation; a goal event (1) is prevented by the disablement of one of its preconditions (2), the precondition having been blocked by some earlier event(3) causing a conflicting state(4). This blocked-precondition interaction can occur when the speed of a coil-car is increased (during adaptation), causing a power availability problem that results in the coil-car running out of power (power is a precondition of movement).…”

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confidence: 99%

Adaptation-guided retrieval: questioning the similarity assumption in reasoning

Smyth

Keane

1998

Artificial Intelligence

135

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One of the major assumptions in Artificial Intelligence is that similar experiences can guide future reasoning, problem solving and learning; what we will call, the similarity assumption. The similarity assumption is used in problem solving and reasoning systems when target problems are dealt with by resorting to a previous situation with common conceptual features. In this article, we question this assumption in the context of case-based reasoning (CBR). In CBR, the similarity assumption plays a central role when new problems are solved, by retrieving similar cases and adapting their solutions. The success of any CBR system is contingent on the retrieval of a case that can be successfully reused to solve the target problem. We show that it is unwarranted to assume that the most similar case is also the most appropriate from a reuse perspective. We argue that similarity must be augmented by deeper, adaptation knowledge about whether a case can be easily modified to fit a target problem. We implement this idea in a new technique, called adaptation-guided retrieval (AGR), which provides a direct link between retrieval similarity and adaptation needs. This technique uses specially formulated adaptation knowledge, which, during retrieval, facilitates the computation of a precise measure of a case's adaptation requirements. In closing, we assess the broader implications of AGR and argue that it is just one of a growing number of methods that seek to overcome the limitations of the traditional, similarity assumption in an effort to deliver more sophisticated and scaleable reasoning systems.

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confidence: 99%

Adaptation-guided retrieval: questioning the similarity assumption in reasoning

Smyth

Keane

1998

Artificial Intelligence

135

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“…The most representative paper for this cluster is the invited talk from EWCBR'06 (Rissland 2006), titled “The Fun Begins with Retrieval: Explanation and CBR.” The temporal distribution of papers in this cluster is bimodal with a number of papers appearing in the early days of the conference series in 1993 and 1994 and another concentration of activity in more recent years. The early papers (for example, Aamodt [1993], Bento et al [1994]) report work on knowledge‐intensive explanation while some of the more recent papers (Cunningham, Doyle, and Loughrey 2003; McSherry 2004) represent a knowledge‐light approach.…”

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confidence: 99%

An Analysis of Current Trends in CBR Research Using Multiview Clustering

et al. 2010

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The European Conference on Case-Based Reasoning (CBR) in 2008 marked 15 years of international and European CBR conferences where almost seven hundred research papers were published. In this report we review the research themes covered in these papers and identify the topics that are active at the moment. The main mechanism for this analysis is a clustering of the research papers based on both co-citation links and text similarity. It is interesting to note that the core set of papers has attracted citations from almost three thousand papers outside the conference collection so it is clear that the CBR conferences are a sub-part of a much larger whole. It is remarkable that the research themes revealed by this analysis do not map directly to the sub-topics of CBR that might appear in a textbook. Instead they reflect the applications-oriented focus of CBR research, and cover the promising application areas and research challenges that are faced.

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Survey on structure-based case retrieval

Gebhardt

1997

The Knowledge Engineering Review

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The main components of case-based reasoning are case retrieval and case reuse. While case retrieval mostly uses attribute comparisons, many other possibilities exist. The case similarity concepts described in the literature that are based on more elaborate structural properties are classified here into five groups: restricted geometric relationships; graphs; semantic nets; model-based similarities; hierarchically structured similarities. Some general topics conclude this survey on structure-based case retrieval methods and systems.

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Experimental study of an evaluation function for cases imperfectly explained

Cited by 3 publications

References 2 publications

Adaptation-guided retrieval: questioning the similarity assumption in reasoning

Adaptation-guided retrieval: questioning the similarity assumption in reasoning

An Analysis of Current Trends in CBR Research Using Multiview Clustering

Survey on structure-based case retrieval

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