Introduction to artificial neural systems
Introduction to artificial neural systems
Foundations of fuzzy neural computations
Soft computing
Case-based reasoning
Artificial Intelligence Review - Special issue on lazy learning
Feature Weighting by Explaining Case-Based Planning Episodes
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Learning to Improve Case Adaption by Introspective Reasoning and CBR
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Using Introspective Learning to Improve Retrieval in CBR: A Case Study in Air Traffic Control
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Collaborative Maintenance - A Distributed, Interactive Case-Base Maintenance Strategy
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Learning Feature Weights from Case Order Feedback
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Defining Similarity Measures: Top-Down vs. Bottom-Up
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Increasing Precision of Credible Case-Based Inference
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Using evolution programs to learn local similarity measures
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Learning similarity measures: a formal view based on a generalized CBR model
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
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Recently more and more researchers have been supporting the view that learning is a goaldriven process. One of the key properties of a goal-driven learner is introspectiveness-the ability to notice the gaps in its knowledge and to reason about the information required to fill in those gaps. In this paper, we introduce a quantitative introspective learning paradigm into case-based reasoning (CBR). The result is an integrated problem-solving model which will learn introspectively feature weights in a case base in order to be responsive dynamically to its users. In contrast to the existing qualitative methods for introspective learning, our model has the advantage of being able to capture accurate learning information in the interactions with its users. A CBR system equipped with quantitative introspective learning ability can allow the feature weights to be captured automatically and to track its users' changing preferences continuously. In such a system, while the reasoning part is still case-based, the learning part is shouldered by a quantitative introspective learning model. Weight learning and evolution are accomplished in the background. The effectiveness of this integration will be demonstrated through a series of empirical experiments.