Title :
Exploiting similarity and experience in decision making
Author :
Hüllermeier, Eyke
Author_Institution :
Dept. of Comput. Sci., Dortmund Univ., Germany
fDate :
6/24/1905 12:00:00 AM
Abstract :
The idea of case-based decision making has recently been proposed as an alternative to the expected utility theory. 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. In this paper, we consider case-based decision making within the context of instance-based learning, which is a special type of machine learning method. From this consideration we suggest alternative case-based decision principles. These principles are motivated from a computational point of view and characterized axiomatically. Moreover, the possibility of applying case-based decision making in approximate reasoning is briefly discussed
Keywords :
case-based reasoning; decision theory; learning (artificial intelligence); pattern classification; approximate reasoning; axiomatic characterization; case-based decision making; case-based reasoning; decision theory; instance-based learning; machine learning; nearest neighbor classification; Artificial intelligence; Computer science; Decision making; Decision theory; Learning systems; Machine learning; Nearest neighbor searches; Psychology; Uncertainty; Utility theory;
Conference_Titel :
Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7280-8
DOI :
10.1109/FUZZ.2002.1005083