Handbook of logic in artificial intelligence and logic programming (vol. 3)
Using possibilistic logic for modeling qualitative decision: ATMS-based algorithms
Fundamenta Informaticae - Special issue on soft computing
Qualitative decision theory: from savage's axioms to nonmonotonic reasoning
Journal of the ACM (JACM)
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
Possibilistic uncertainty handling for answer set programming
Annals of Mathematics and Artificial Intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Possibility theory as a basis for qualitative decision theory
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Handling exceptions in logic programming without negation as failure
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Possibilistic semantics for logic programs with ordered disjunction
FoIKS'10 Proceedings of the 6th international conference on Foundations of Information and Knowledge Systems
Handling exceptions in logic programming without negation as failure
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Nested Preferences in Answer Set Programming
Fundamenta Informaticae - Latin American Workshop on Logic Languages, Algorithms and New Methods of Reasoning (LANMR)
Dealing with explicit preferences and uncertainty in answer set programming
Annals of Mathematics and Artificial Intelligence
Using possibilistic logic for modeling qualitative decision: Answer Set Programming algorithms
International Journal of Approximate Reasoning
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Possibility theory offers a qualitative framework for modeling decision under uncertainty. In this setting, pessimistic and optimistic decision criteria have been formally justified. The computation by means of possibilistic logic inference of optimal decisions according to such criteria has been proposed. This paper presents an Answer Set Programming (ASP)-based methodology for modeling decision problems and computing optimal decisions in the sense of the possibilistic criteria. This is achieved by applying both a classic and a possibilistic ASP-based methodology in order to handle both a knowledge base pervaded with uncertainty and a prioritized preference base.