Abstract
The idea of case-based decision making has recently been proposed as an alternative to expected utility theory. It combines concepts and principles from both decision theory and case-based reasoning. Loosely speaking, a case-based decision maker learns by storing already experienced decision problems, along with a rating of the results. Whenever a new problem needs to be solved, possible actions are assessed on the basis of experience from similar situations in which these actions have already been applied. We formalize case-based decision making within the framework of fuzzy sets and possibility theory. The basic idea underlying this approach is to give preference to acts which have always led to good results for problems which are similar to the current one. We also propose two extensions of the basic model. Firstly, we deal separately with situations where an agent has made very few, if any, observations. Obviously, such situations are difficult to handle for a case-based approach. Secondly, we propose a reasonable relaxation of the original decision principle, namely to look for acts which have yielded good results, not necessarily for all, but at least for most cases in the past.
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Dubois, D., Hüllermeier, E., Prade, H. (2003). Possibilistic case-based decisions. In: Lesage, C., Cottrell, M. (eds) Connectionist Approaches in Economics and Management Sciences. Advances in Computational Management Science, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3722-6_2
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DOI: https://doi.org/10.1007/978-1-4757-3722-6_2
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