Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilization
Machine Learning - Special issue on case-based reasoning
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Using Configuration Techniques for Adaptation
Case-Based Reasoning Technology, From Foundations to Applications
Techniques and Knowledge Used for Adaptation During Case-Based Problem Solving
IEA/AIE '98 Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial In telligence and Expert Systems: Tasks and Methods in Applied Artificial Intelligence
Relational Case-based Reasoning for Carcinogenic Activity Prediction
Artificial Intelligence Review
Extracting performers' behaviors to annotate cases in a CBR system for musical tempo transformations
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
The roles of adaptation in case-based design
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Dynamic composition of electronic institutions for teamwork
COIN'07 Proceedings of the 2007 international conference on Coordination, organizations, institutions, and norms in agent systems III
Derivational analogy: challenges and opportunities
EG-ICE'06 Proceedings of the 13th international conference on Intelligent Computing in Engineering and Architecture
Multi-agent case-based reasoning for cooperative reinforcement learners
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
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We present a form of case-based reuse conducive to the cooperation of multiple CBR agents in problem solving. First, we present a form of constructive adaptation for configuration tasks with compositional cases. We then introduce CoopCA, a multi-agent constructive adaptation technique for case reuse. The agents suggest possible components to be added to the ongoing configuration problem, allowing an open, distributed process where components used in cases of different agents are pooled together in a principled way. Moreover, the agents can use their case base to inform about a similarity-based likelihood that the suggested component will be adequate for the current problem. We illustrate CoopCA by applying it to the task of agent team formation.