Case-Based Reasoning Research and Development: 22nd by Luc Lamontagne, Enric Plaza

By Luc Lamontagne, Enric Plaza

This booklet constitutes the refereed lawsuits of the twenty first overseas convention on Case-Based Reasoning learn and improvement (ICCBR 2014) held in Cork, eire, in September 2014. The 35 revised complete papers offered have been conscientiously reviewed and chosen from forty nine submissions. The shows hide a variety of CBR issues of curiosity either to researchers and practitioners together with case retrieval and variation, similarity review, case base upkeep, wisdom administration, recommender platforms, multiagent structures, textual CBR, and purposes to healthcare and laptop games.

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Extra info for Case-Based Reasoning Research and Development: 22nd International Conference, ICCBR 2014, Cork, Ireland, September 29, 2014 - October 1, 2014. Proceedings

Sample text

More formally, this means that we assume a set of theoretically conceivable solutions Y∗ that contains the space of practically reachable solutions Y as a subset. , those preferences will be deemed relevant that pertain to similar problems. An important question that needs to be answered in this connection is the following: Given a set of observed preferences on solutions, considered representative for a problem x0 , what is the underlying preference structure ∗ x0 or, equivalently, what is most likely the ideal solution y for x0 ?

O. 5 Results and Analysis H1: Figure 5 displays plots for Coverage and Energy Consumption for the test problems. Using a one-tailed t-test, we found that R consistently recorded significantly lower Efficiency (not shown for space reasons) than the other algorithms, which supports H1. For Coverage, S performed significantly better on 6 scenarios, O 9 times, CBRN 4 times, and CBRA 6 times. For Energy Consumption, these algorithms significantly outperformed R for all scenarios (except problem 4 for CBRN and O).

An element from Ynn is selected uniformly at random). Although this is a very simple strategy, it is suitable to isolate the effect of guiding the search behavior on the basis of preference information. , we defined the neighborhood of a solution y as N (y) = Y \ {y}. As can be seen from the results in Figure 3, our preference-based CBR approach shows a clear trend toward improvement from episode to episode, as opposed to the random variant of the search algorithm. More importantly, however, similarity adaptation is clearly beneficial: Making use of the preference information gathered in the first episodes, Pref-CBR Search succeeds in learning the ground truth similarity measure, which in turn leads to better search performance and solution quality.

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