15 found
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  1.  12
    Ontology Reasoning with Deep Neural Networks.Patrick Hohenecker & Thomas Lukasiewicz - manuscript
    The ability to conduct logical reasoning is a fundamental aspect of intelligent behavior, and thus an important problem along the way to human-level artificial intelligence. Traditionally, symbolic methods from the field of knowledge representation and reasoning have been used to equip agents with capabilities that resemble human reasoning qualities. More recently, however, there has been an increasing interest in applying alternative approaches based on machine learning rather than logic-based formalisms to tackle this kind of tasks. Here, we make use of (...)
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  2.  52
    Probabilistic Logic Under Coherence, Model-Theoretic Probabilistic Logic, and Default Reasoning in System P.Veronica Biazzo, Angelo Gilio, Thomas Lukasiewicz & Giuseppe Sanfilippo - 2002 - Journal of Applied Non-Classical Logics 12 (2):189-213.
    We study probabilistic logic under the viewpoint of the coherence principle of de Finetti. In detail, we explore how probabilistic reasoning under coherence is related to model- theoretic probabilistic reasoning and to default reasoning in System . In particular, we show that the notions of g-coherence and of g-coherent entailment can be expressed by combining notions in model-theoretic probabilistic logic with concepts from default reasoning. Moreover, we show that probabilistic reasoning under coherence is a generalization of default reasoning in System (...)
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  3.  4
    Ontology Reasoning with Deep Neural Networks.Patrick Hohenecker & Thomas Lukasiewicz - 2018
    The ability to conduct logical reasoning is a fundamental aspect of intelligent behavior, and thus an important problem along the way to human-level artificial intelligence. Traditionally, symbolic methods from the field of knowledge representation and reasoning have been used to equip agents with capabilities that resemble human reasoning qualities. More recently, however, there has been an increasing interest in applying alternative approaches based on machine learning rather than logic-based formalisms to tackle this kind of tasks. Here, we make use of (...)
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  4.  65
    Nonmonotonic Probabilistic Reasoning Under Variable-Strength Inheritance with Overriding.Thomas Lukasiewicz - 2005 - Synthese 146 (1-2):153 - 169.
    We present new probabilistic generalizations of Pearl’s entailment in System Z and Lehmann’s lexicographic entailment, called Zλ- and lexλ-entailment, which are parameterized through a value λ ∈ [0,1] that describes the strength of the inheritance of purely probabilistic knowledge. In the special cases of λ = 0 and λ = 1, the notions of Zλ- and lexλ-entailment coincide with probabilistic generalizations of Pearl’s entailment in System Z and Lehmann’s lexicographic entailment that have been recently introduced by the author. We show (...)
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  5. Causes and Explanations in the Structural-Model Approach: Tractable Cases.Thomas Eiter & Thomas Lukasiewicz - 2006 - Artificial Intelligence 170 (6-7):542-580.
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  6. Combining Answer Set Programming with Description Logics for the Semantic Web.Thomas Eiter, Giovambattista Ianni, Thomas Lukasiewicz, Roman Schindlauer & Hans Tompits - 2008 - Artificial Intelligence 172 (12-13):1495-1539.
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  7. Complexity Results for Structure-Based Causality.Thomas Eiter & Thomas Lukasiewicz - 2002 - Artificial Intelligence 142 (1):53-89.
  8. Complexity Results for Explanations in the Structural-Model Approach.Thomas Eiter & Thomas Lukasiewicz - 2004 - Artificial Intelligence 154 (1-2):145-198.
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  9. Default Reasoning From Conditional Knowledge Bases: Complexity and Tractable Cases.Thomas Eiter & Thomas Lukasiewicz - 2000 - Artificial Intelligence 124 (2):169-241.
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  10. Combining Probabilistic Logic Programming with the Power of Maximum Entropy.Gabriele Kern-Isberner & Thomas Lukasiewicz - 2004 - Artificial Intelligence 157 (1-2):139-202.
  11. Ki-2001 Workshop: Uncertainty in Artificial Intellligence. Informatik-Berichte (8/2001).Gabriele Kern-Isberner, Thomas Lukasiewicz & Emil Weydert (eds.) - 2001
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  12. Ki-2001 Workshop: Uncertainty in Artificial Intellligence.Gabriele Kern-Isberner, Thomas Lukasiewicz & Emil Weydert (eds.) - 2001
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  13.  1
    Complexity Results for Preference Aggregation Over (M)CP-Nets: Pareto and Majority Voting.Thomas Lukasiewicz & Enrico Malizia - 2019 - Artificial Intelligence 272:101-142.
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  14. Expressive Probabilistic Description Logics.Thomas Lukasiewicz - 2008 - Artificial Intelligence 172 (6-7):852-883.
  15. Weak Nonmonotonic Probabilistic Logics.Thomas Lukasiewicz - 2005 - Artificial Intelligence 168 (1-2):119-161.