Semantics for possibilistic disjunctive programs

We define a possibilistic disjunctive logic programming approach for modeling uncertain, incomplete and inconsistent information. This approach introduces the use of possibilistic disjunctive clauses which are able to capture incomplete information and incomplete states of a knowledge base at the sa...

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Detalles Bibliográficos
Autores: Nieves, Juan Carlos, Cortés García, Claudio Ulises|||0000-0003-0192-3096, Osorio Galindo, Mauricio
Tipo de recurso: informe técnico
Fecha de publicación:2008
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/86911
Acceso en línea:https://hdl.handle.net/2117/86911
Access Level:acceso abierto
Palabra clave:Answer set programming
Uncertain information
Possibilistic reasoning
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Descripción
Sumario:We define a possibilistic disjunctive logic programming approach for modeling uncertain, incomplete and inconsistent information. This approach introduces the use of possibilistic disjunctive clauses which are able to capture incomplete information and incomplete states of a knowledge base at the same time. This approach is computable and moreover allows encoding uncertain information by using either numerical values or relative likelihoods. In order to define the semantics of the possibilistic disjunctive programs, three approaches are defined: 1.- The first is strictly close to the proof theory of possibilistic logic and answer set models; 2.- The second is based on partial evaluation, a fix-point operator and answer set models; and 3.- The last is also based on the proof theory of possibilistic logic and pstable semantics. In order to manage inconsistent possibilistic logic programs, a preference criterion between inconsistent possibilistic models is defined; in addition, the approach of cuts for restoring consistency of an inconsistent possibilistic knowledge base is adopted. The approach is illustrated by a medical scenario.