Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Case-based reasoning
Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
Parallel Models of Associative Memory
Parallel Models of Associative Memory
Learning to Refine Indexing by Introspective Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
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
Maintaining Unstructured Case Base
ICCBR '97 Proceedings of the Second 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
Refining Conversational Case Libraries
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Protos: a unified approach to concept representation, classification, and learning
Protos: a unified approach to concept representation, classification, and learning
An Open Framework for Smart and Personalized Distance Learning
ICWL '02 Proceedings of the First International Conference on Advances in Web-Based Learning
The Application of Case Based Reasoning on Q&A System
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Using Reinforcement Learning for Similarity Assessment in Case-Based Systems
IEEE Intelligent Systems
Metacognition in computation: a selected research review
Artificial Intelligence
Integrating Case-Based Reasoning and Meta-Learning for a Self-Improving Intelligent Tutoring System
International Journal of Artificial Intelligence in Education
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Integrated introspective case-based reasoning for intelligent tutoring systems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Field review: Metacognition in computation: A selected research review
Artificial Intelligence
Case mining from large databases
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Classifying criminal charges in chinese for web-based legal services
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Engineering Applications of Artificial Intelligence
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A key issue in case-based reasoning is how to maintain the domain knowledge in the face of a changing environment. During the case retrieval process in case-based reasoning, feature-value pairs are used to compute the ranking scores of the cases in a case base, and different feature-value pairs may have different importance measures, represented as weight values, in this computation. How to maintain a set of appropriate feature weights so that they can be used to solve future problems effectively and efficiently will be a key factor in determining the success of case-based reasoning applications.Our focus in this paper is on the dynamic maintenance of feature weights in a case base. We address a particular problem related to the feature-weight maintenance issue. In current practice, the feature weights are assigned and revised manually, not only making them highly informal and inaccurate, but also involving intensive labor. We would like to introduce a semi-automatic introspective learning method to partially address this issue. Our approach is to construct a network architecture on the case base that supports introspective learning. Weight learning and weight-evolution are accomplished in the background through the integration of a learning network into case-based reasoning, in which, while the reasoning part is still case based, the learning part is shouldered by a layered network. The computation in the network follows well-known neural network algorithms with well known properties. We demonstrate the effectiveness of our approach through experiments.