Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Label Ranking in Case-Based Reasoning
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Learning Similarity Functions from Qualitative Feedback
ECCBR '08 Proceedings of the 9th 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
A Novel Framework for Case-Based Decision Analysis
Proceedings of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008
CBR for Advice Giving in a Data-Intensive Environment
Proceedings of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008
Toward a probabilistic formalization of case-based inference
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Using evolution programs to learn local similarity measures
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Completeness criteria for retrieval in recommender systems
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Decision diagrams: fast and flexible support for case retrieval and recommendation
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Combining case-based and similarity-based product recommendation
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
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This paper proposes a novel approach to case-based decision analysis supported by case-based reasoning (CBR). The strength of CBR is utilized for building a situation dependent decision model without complete domain knowledge. This is achieved by deriving states probabilities and general utility estimates from the case library and the subset of cases retrieved in a situation described in query. In particular, the derivation of state probabilities is realized through an information fusion process which comprises evidence (case) combination using the Dempster-Shafer theory and Bayesian probabilistic reasoning. Subsequently decision theory is applied to the decision model learnt from previous cases to identify the most promising, secured, and rational choices. In such a way we take advantage of both the strength of CBR to learn without domain knowledge and the ability of decision theory to analyze under uncertainty. We have also studied the issue of imprecise representations of utility in individual cases and explained how fuzzy decision analysis can be conducted when case specific utilities are assigned with fuzzy data.