This article examines prospects for theories and methods of preferences, both in the specific sense of the preferences of the ideal rational agents considered in economics and decision theory and in the broader interplay between reasoning and rationality considered in philosophy, psychology, and artificial intelligence. Modern applications seek to employ preferences as means for specifying, designing, and controlling rational behaviors as well as descriptive means for understanding behaviors. We seek to understand the nature and representation of preferences by examining the roles, origins, meaning, structure, evolution, and application of preferences.

Abstract. We define logic programs with defaults and argumentation theories, a new framework that unifies most of the earlier proposals for defeasible reasoning in logic programming. We present a model-theoretic semantics and study its reducibility and well-behavior properties. We use the framework as an elegant and flexible foundation to extend and improve upon Generalized Courteous Logic Programs (GCLP) [19]—one of the popular forms of defeasible reasoning. The extensions include higherorder and object-oriented features of Hilog and F-Logic [7,21]. The improvements include much simpler, incremental reasoning algorithms and more intuitive behavior. The framework and its Courteous family instantiation were implemented as an extension to the FLORA-2 system.

In this work, we define a new framework in order to improve the knowledge representation power of Answer Set Programming paradigm. Our proposal is to use notions from possibility theory to extend the stable model semantics by taking into account a certainty level, expressed in terms of necessity measure, on each rule of a normal logic program. First of all, we introduce possibilistic definite logic programs and show how to compute the conclusions of such programs both in syntactic and semantic ways. The syntactic handling is done by help of a fix-point operator, the semantic part relies on a possibility distribution on all sets of atoms and we show that the two approaches are equivalent. In a second part, we define what is a possibilistic stable model for a normal logic program, with default negation. Again, we define a possibility distribution allowing to determine the stable models. 1

A crucial concern in the evaluation of evidence related to a major crime is the formulation of sufficient alternative plausible scenarios that can explain the available evidence. However, software aimed at assisting human crime investigators by automatically constructing crime scenarios from evidence is difficult to develop because of the almost infinite variation of plausible crime scenarios. This paper introduces a novel knowledge driven methodology for crime scenario construction and it presents a decision support system based on it. The approach works by storing the component events of the scenarios instead of entire scenarios and by providing an algorithm that can instantiate and compose these component events into useful scenarios. The scenario composition approach is highly adaptable to unanticipated cases because it allows component events to match the case under investigation in many different ways. Given a description of the available evidence, it generates a network of plausible scenarios that can then be analysed to devise effective evidence collection strategies. The applicability of the ideas presented here are demonstrated by means of a realistic example and prototype decision support software. 1

Abstract. The rapid expansion of the Internet and World Wide Web led to growing interest in data and information integration, which should be capable to deal with inconsistent and incomplete data. Answer Set solvers have been considered as a tool for data integration systems by different authors. We discuss why data integration can be an interesting model application of Answer Set programming, reviewing valuable features of non-monotonic logic programs in this respect, and emphasizing the role of the application for driving research. 1

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Recently, strong equivalence for Answer Set Programming has been studied intensively, and was shown to be beneficial for modular programming and automated optimization. In this paper we define the novel notion of strong equivalence for logic programs with preferences. Based on this definition we give, for several semantics for preference handling, necessary and sufficient conditions for programs to be strongly equivalent. These results provide a clear picture of the relationship of these semantics with respect to strong equivalence, which differs considerably from their relationship with respect to answer sets. Finally, based on these results, we present for the first time simplification methods for logic programs with preferences. 1

This paper describes a methodology based on Answer Set Programming (ASP) to work with incomplete geographic data. Source geographic data which describes a risk zone is translated to ASP description and it allows to solve query’s which can not be solved by a normal GIS. An evacuation plan can change when new situations are presented, for instance traffic, a zone in extreme danger or other natural modification of the zone to be evacuated. Since 1994, Risk Management Office in Mexico has declared around 30 km from the Popocatépetl Volcano crater a danger zone. This office defined several roads to evacuate the people when a Volcano event can be presented. Our approach allows to simulate and to give support to generate new evacuation plans. The results developed by the ASP approach can be translated to a visual format and can be incorporated to a GIS to develop other kind of analysis by using the geographic data.

A Konolige bridge between default logic and autoepistemic logic

Abstract. When configuring customizable software, it is useful to provide interactive tool-support that ensures that the configuration does not breach given constraints. But, when is a configuration complete and how can the tool help the user to complete it? We formalize this problem and relate it to concepts from non-monotonic reasoning well researched in Artificial Intelligence. The results are interesting for both practitioners and theoreticians. Practitioners will find a technique facilitating an interactive configuration process and experiments supporting feasibility of the approach. Theoreticians will find links between well-known formal concepts and a concrete practical application. 1