Jokes and Belief Revision

The paper deals with a topic little studied in artificial intelligence: the understanding of humor. In this preliminary study, we try to identify the basic mechanism at work in quips and narrative jokes. It seems that in many cases a belief revision process is operating, leading to an unexpected conclusion, through the punchline of the jest. We propose a formal modeling of jokes based on belief revision. Namely the punchline, which triggers a revision, is both surprising and explains perfectly what was reported in the beginning of the joke. This also suggests a way of ranking jokes in terms of surprise and strength of explanation, using possibilistic logic.

AGGREGATION OF SYMBOLIC POSSIBILISTIC KNOWLEDGE BASES FROM THE POSTULATE POINT OF VIEW

Aggregation of knowledge bases in the propositional language was soon investigated and the requirements of aggregation processes of propositional knowledge bases basically are unified within the community of researchers and applicants. Aggregation of standard possibilistic knowledge bases where the weight of propositional formulas being numeric has also been investigated and applied in building the intelligent systems, in multi-criterion decision-making processes as well as in decision-making processes implemented by many people. Symbolic possibilistic logic (SPL for short) where the weight of the propositional formulas is symbols was proposed, and recently it was proven that SPL is soundness and completeness. In order to apply SPL in building intelligent systems as well as in decision-making processes, it is necessary to solve the problem of aggregation of symbolic possibilistic knowledge bases (SPK bases for short). This problem has not been researched so far.The purpose of this paper is to investigate aggregation processes of SPK bases from the postulate point of view in propositional language. These processes are implemented via impossibility distributions defined from SPK bases. Characteristics of merging operators, including hierarchical merging operators, of symbolic impossibility distributions (SIDs for short) from the postulate point of view will be shown in the paper.

AGGREGATION OF SYMBOLIC POSSIBILISTIC KNOWLEDGE BASES FROM THE POSTULATE POINT OF VIEW

Aggregation of knowledge bases in the propositional language was soon investigated and the requirements of aggregation processes of propositional knowledge bases basically are unified within the community of researchers and applicants. Aggregation of standard possibilistic knowledge bases where the weight of propositional formulas being numeric has also been investigated and applied in building the intelligent systems, in multi-criterion decision-making processes as well as in decision-making processes implemented by many people. Symbolic possibilistic logic (SPL for short) where the weight of the propositional formulas is symbols was proposed, and recently it was proven that SPL is soundness and completeness. In order to apply SPL in building intelligent systems as well as in decision-making processes, it is necessary to solve the problem of aggregation of symbolic possibilistic knowledge bases (SPK bases for short). This problem has not been researched so far.The purpose of this paper is to investigate aggregation processes of SPK bases from the postulate point of view in propositional language. These processes are implemented via impossibility distributions defined from SPK bases. Characteristics of merging operators, including hierarchical merging operators, of symbolic impossibility distributions (SIDs for short) from the postulate point of view will be shown in the paper.

Possibilistic ASP Base Revision by Certain Input

Belief base revision has been studied within the answer set programming framework. We go a step further by introducing uncertainty and studying belief base revision when beliefs are represented by possibilistic logic programs under possibilistic answer set semantics and revised by certain input. The paper proposes two approaches of rule-based revision operators and presents their semantic characterization in terms of possibilistic distribution. This semantic characterization allows for equivalently considering the evolution of syntactic logic programs and the evolution of their semantic content. It then studies the logical properties of the proposed operators and gives complexity results.