Decision theory: an introduction to the mathematics of rationality
Decision theory: an introduction to the mathematics of rationality
Case-based reasoning
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
Case-Based Quality Management System Using Expectation Values
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
A new hybrid case-based architecture for medical diagnosis
Information Sciences—Informatics and Computer Science: An International Journal
Multi-agent system approach to context-aware coordinated web services under general market mechanism
Decision Support Systems
Loss and gain functions for CBR retrieval
Information Sciences: an International Journal
A Novel Framework for Case-Based Decision Analysis
Proceedings of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008
Multi-agent system approach to context-aware coordinated web services under general market mechanism
Decision Support Systems
Mammographic case base applied for supporting image diagnosis of breast lesion
Expert Systems with Applications: An International Journal
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In most realistic problem-solving activities, the problem solver faces two major issues: how to deal with unknown problem features, and how to make decisions in the presence of these unknowns. We've developed a methodology that lets case-based reasoning use decision-theoretic approaches to deal with these two issues. We view CBR as a technology for automated, intelligent problem solving; the goal of integrating CBR and decision theory is to improve the ability of CBR systems to solve problems in domains of incomplete information. (See the sidebars for more information on CBR and decision theory.) Our methodology views the retrieval of old cases in CBR as a decision problem, where each case from the case base provides an alternative solution and a prediction of the possible outcomes for the problem. When case-based problem solving encounters uncertainty, our methodology applies decision theory to evaluate each case in terms of the attributes that are significant for the problem, so that the most desirable case can be selected. We implemented our methodology in a case-based design assistant that helps chemists design pharmaceuticals. The system proposes chemicals for generating drugs and can evaluate the various design choices. Drug design is an appropriate application domain. The number of possible compounds that must be explored during the design phase is enormous. Also, the evaluation of design choices is extremely important, because it lets the chemist focus on a small subset of such compounds. Drug development is a very difficult design task where an intelligent assistant can greatly enhance the quality of the compounds generated and improve the chemist's productivity. The interactions of chemicals or their effect on the human body are often not known, and the specifications are necessarily incomplete. Finally, a compound might have multiple effects, both positive and negative. Its usefulness in a specific situation needs to be evaluated by weighing the drug's utility to the target population versus the predicted risks.