Titlebook: Case-Based Reasoning Research and Development; 20th International C Belén Díaz Agudo,Ian Watson Conference proceedings 2012 Springer-Verlag

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钢笔尖

Adaptation in a CBR-Based Solver Portfolio for the Satisfiability Problem,ation, planning, scheduling, configuration and telecommunications. Recently, there has been growing interest in using portfolios of solvers for this problem. In this paper we present a case-based reasoning approach to SAT solving. A key challenge is the adaptation phase, which we focus on in some de

Decline

Metamorphosis

Learning and Reusing Goal-Specific Policies for Goal-Driven Autonomy,g strategy for action selection. For example, Q-learning is particularly slow to learn a policy to win complex strategy games. We propose GRL, the first GDA system capable of learning and reusing goal-specific policies. GRL is a case-based goal-driven autonomy (GDA) agent embedded in the RL cycle. G

Tincture

Custom Accessibility-Based CCBR Question Selection by Ongoing User Classification,ngth for retrieval. However, users may not always be able or willing to answer the most discriminative questions. This paper presents Accessibility Influenced Attribute Selection Plus (AIAS+), a method for customizing CCBR question selection to reflect the types of questions the user is likely to an

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Feature Weighting and Confidence Based Prediction for Case Based Reasoning Systems,conform to the similarity hypothesis decreases the alignment of the case base and often degrades the performance of a CBR system. It is therefore important to find out the suitability of each case for the application of CBR and associate a solution with a certain degree of confidence. Feature weight

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ACTIN

A Lazy Learning Approach to Explaining Case-Based Reasoning Solutions, The explanation rules discovered in our approach resemble the classification rules traditionally targeted by rule learning algorithms, and the learning process is adapted from one such algorithm (PRISM). The explanation rule learned for a CBR solution is required to cover both the target problem an

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https://doi.org/10.1007/978-1-4757-3419-5 different problem solving strategies, utilizing different kinds of knowledge, and becoming experts for specific areas of responsibility, computer based expert systems do not have the reputation to be successful at these tasks. Based on this, the potential of CBR succeeding as future expert systems is discussed.