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Answer set programming for computing decisions under uncertainty
Confalonieri, Roberto; Prade, Henri
Universitat Politècnica de Catalunya. KEMLG - Grup d´Enginyeria del Coneixement i Aprenentatge Automàtic
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.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Declarative programming
Programació declarativa
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Springer
         

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