Instance-based prediction of real-valued attributes
Computational Intelligence
Instance-Based Learning Algorithms
Machine Learning
Case-Based Approximate Reasoning (Theory and Decision Library B)
Case-Based Approximate Reasoning (Theory and Decision Library B)
Label Ranking in Case-Based Reasoning
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Generic preferences over subsets of structured objects
Journal of Artificial Intelligence Research
Tournament selection: stable fitness pressure in XCS
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Preferences in AI: An overview
Artificial Intelligence
Towards case-based adaptation of workflows
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Preference-based CBR: general ideas and basic principles
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Building on recent research on preference handling in artificial intelligence and related fields, our general goal is to develop a coherent and universally applicable methodological framework for CBR on the basis of formal concepts and methods for knowledge representation and reasoning with preferences. A preference-based approach to CBR appears to be appealing for several reasons, notably because case-based experiences naturally lend themselves to representations in terms of preference relations, even when not dealing with preference information in a literal sense. Moreover, the flexibility and expressiveness of a preference-based formalism well accommodate the uncertain and approximate nature of case-based problem solving. In this paper, we make a first step toward a preference-based formalization of CBR. Apart from providing a general outline of the framework as a whole, we specifically address the step of case-based inference. The latter consists of inferring preferences for candidate solutions in the context of a new problem, given such preferences in similar situations. Our case-based approach to predicting preference models is concretely realized for a scenario in which solutions are represented in the form of subsets of a reference set. First experimental results are presented to demonstrate the effectiveness of this approach.