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Auflistung Künstliche Intelligenz 31(1) - März 2017 nach Erscheinungsdatum
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- ZeitschriftenartikelReasoning about Imprecise Beliefs in Multi-Agent Systems with PDT Logic(KI - Künstliche Intelligenz: Vol. 31, No. 1, 2017) Martiny, Karsten; Möller, RalfWe present Probabilistic Doxastic Temporal (PDT) Logic, a formalism to represent and reason about probabilistic beliefs and their finite temporal evolution in multi-agent systems. This formalism enables the quantification of agents’ beliefs through probability intervals and incorporates an explicit notion of time. In this work, we give an overview of recent contributions on PDT Logic. After describing the syntax and semantics of this formalism, we show that two alternative representation forms are available to model problems in PDT Logic. Furthermore, we outline how abductive reasoning can be performed in PDT Logic and how this formalism can be extended to infinite time frames.
- ZeitschriftenartikelAnalyzing a Bipolar Decision Structure Through Qualitative Decision Theory(KI - Künstliche Intelligenz: Vol. 31, No. 1, 2017) Saint-Cyr, Florence Dupin; Guillaume, RomainIn this paper we study the link between a bipolar decision structure called bipolar leveled framework (BLF) and the qualitative decision theory based on possibility theory. A BLF defines the set of possible decision principles that may be used in order to evaluate the admissibility of a given candidate. A decision principle is a rule that relates some observations about the candidate to a given goal that the selection of this candidate may achieve or miss. The decision principles are ordered according to the importance of the goal they support. Oppositions to decision principles are also described in the BLF under the form of observations that contradict the realization of the decision principles. In order to show that this rich and visual framework is well founded we show how the notions defined in the BLF can be translated in terms of qualitative decision theory.
- ZeitschriftenartikelDecidability and Complexity of Fuzzy Description Logics(KI - Künstliche Intelligenz: Vol. 31, No. 1, 2017) Baader, Franz; Borgwardt, Stefan; Peñaloza, RafaelFuzzy description logics (FDLs) have been introduced to represent concepts for which membership cannot be determined in a precise way, i.e., where instead of providing a strict border between being a member and not being a member, it is more appropriate to model a gradual change from membership to non-membership. First approaches for reasoning in FDLs where based either on a reduction to reasoning in classical description logics (DLs) or on adaptations of reasoning approaches for DLs to the fuzzy case. However, it turned out that these approaches in general do not work if expressive terminological axioms, called general concept inclusions (GCIs), are available in the FDL. The goal of this project was a comprehensive study of the border between decidability and undecidability for FDLs with GCIs, as well as determining the exact complexity of the decidable logics. As a result, we have provided an almost complete classification of the decidability and complexity of FDLs with GCIs.
- ZeitschriftenartikelMany Facets of Reasoning Under Uncertainty, Inconsistency, Vagueness, and Preferences: A Brief Survey(KI - Künstliche Intelligenz: Vol. 31, No. 1, 2017) Kern-Isberner, Gabriele; Lukasiewicz, ThomasIn this paper, we give an introduction to reasoning under uncertainty, inconsistency, vagueness, and preferences in artificial intelligence (AI), including some historic notes and a brief survey to previous approaches.
- ZeitschriftenartikelBDI Logics for BDI Architectures: Old Problems, New Perspectives(KI - Künstliche Intelligenz: Vol. 31, No. 1, 2017) Herzig, Andreas; Lorini, Emiliano; Perrussel, Laurent; Xiao, ZhanhaoThe mental attitudes of belief, desire, and intention play a central role in the design and implementation of autonomous agents. In 1987, Bratman proposed their integration into a belief–desire–intention (BDI) theory that was seminal in AI. Since then numerous approaches were built on the BDI paradigm, both practical (BDI architectures and BDI agents) and formal (BDI logics). The logical approaches that were most influential are due to Cohen and Levesque and to Rao and Georgeff. However, three fundamental problems remain up to now. First, the practical and the formal approaches evolved separately and neither fertilised the other. Second, only few formal approaches addressed some important issues such as the revision of intentions or the fundamentally paraconsistent nature of desires, and it seems fair to say that there is currently no consensical, comprehensive logical account of intentions. Finally, only few publications study the interaction between intention and other concepts that are naturally connected to intention, such as actions, planning, and the revision of beliefs and intentions. Our paper summarizes the state of the art, discusses the main open problems, and sketches how they can be addressed. We argue in particular that research on intention should be better connected to fields such as reasoning about actions, automated planning, and belief revision and update.
- ZeitschriftenartikelEditorial(KI - Künstliche Intelligenz: Vol. 31, No. 1, 2017) Igel, Christian
- ZeitschriftenartikelA Practical Comparison of Qualitative Inferences with Preferred Ranking Models(KI - Künstliche Intelligenz: Vol. 31, No. 1, 2017) Beierle, Christoph; Eichhorn, Christian; Kutsch, StevenWhen reasoning qualitatively from a conditional knowledge base, two established approaches are system Z and p-entailment. The latter infers skeptically over all ranking models of the knowledge base, while system Z uses the unique pareto-minimal ranking model for the inference relations. Between these two extremes of using all or just one ranking model, the approach of c-representations generates a subset of all ranking models with certain constraints. Recent work shows that skeptical inference over all c-representations of a knowledge base includes and extends p-entailment. In this paper, we follow the idea of using preferred models of the knowledge base instead of the set of all models as a base for the inference relation. We employ different minimality constraints for c-representations and demonstrate inference relations from sets of preferred c-representations with respect to these constraints. We present a practical tool for automatic c-inference that is based on a high-level, declarative constraint-logic programming approach. Using our implementation, we illustrate that different minimality constraints lead to inference relations that differ mutually as well as from system Z and p-entailment.
- ZeitschriftenartikelQuo vadis, AI?(KI - Künstliche Intelligenz: Vol. 31, No. 1, 2017) Visser, Ubbo
- ZeitschriftenartikelSpecial Issue on Challenges for Reasoning under Uncertainty, Inconsistency, Vagueness, and Preferences(KI - Künstliche Intelligenz: Vol. 31, No. 1, 2017) Kern-Isberner, Gabriele; Lukasiewicz, Thomas
- ZeitschriftenartikelNews(KI - Künstliche Intelligenz: Vol. 31, No. 1, 2017)