14 found
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  1.  13
    Multilanguage hierarchical logics, or: How we can do without modal logics.Fausto Giunchiglia & Luciano Serafini - 1994 - Artificial Intelligence 65 (1):29-70.
  2.  12
    Logic Tensor Networks.Samy Badreddine, Artur D'Avila Garcez, Luciano Serafini & Michael Spranger - 2022 - Artificial Intelligence 303 (C):103649.
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  3.  10
    Distributed First Order Logic.Chiara Ghidini & Luciano Serafini - 2017 - Artificial Intelligence 253 (C):1-39.
  4.  12
    Comparing formal theories of context in AI.Luciano Serafini & Paolo Bouquet - 2004 - Artificial Intelligence 155 (1-2):41-67.
  5. Verifying Space and Time Requirements for Resource-Bounded Agents.Natasha Alechina, Piergiorgio Bertoli, Chiara Ghidini, Mark Jago, Brian Logan & Luciano Serafini - 2007 - In A. Lomuscio & S. Edelkamp (eds.), Model Checking and Artificial Intelligence. Springer.
    The effective reasoning capability of an agent can be defined as its capability to infer, within a given space and time bound, facts that are logical consequences of its knowledge base. In this paper we show how to determine the effective reasoning capability of an agent with limited memory by encoding the agent as a transition system and automatically verifying whether a state where the agent believes a certain conclusion is reachable from the start state. We present experimental results using (...)
     
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  6.  3
    Enhancing context knowledge repositories with justifiable exceptions.Loris Bozzato, Thomas Eiter & Luciano Serafini - 2018 - Artificial Intelligence 257 (C):72-126.
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  7. Minimality and Non-determinism in Multi-context Systems.Floris Roelofsen & Luciano Serafini - 2005 - In B. Kokinov A. Dey (ed.), Modeling and Using Context. Springer. pp. 424--435.
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  8. in Modeling and Using Context. Proceedings of the Second International and Interdisciplinary Conference.Paolo Bouquet, Patrick Brezillon, Francesca Castellani & Luciano Serafini (eds.) - 1999 - Springer.
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  9.  8
    Perspectives on Contexts.Paolo Bouquet, Luciano Serafini & Richmond H. Thomason (eds.) - 2008 - Center for the Study of Language and Inf.
    Most human thinking is thoroughly informed by context but, until recently, theories of reasoning have concentrated on abstract rules and generalities that make no reference to this crucial factor. _Perspectives on Contexts_ brings together essays from leading cognitive scientists to forge a vigorous interdisciplinary understanding of the contextual phenomenon. Applicable to human and machine cognition in philosophy, artificial intelligence, and psychology, this volume is essential to the current renaissance in thinking about context.
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  10. Perspectives on Contexts, CSLI Lecture Notes No. 180.Paolo Bouquet, Luciano Serafini & Richmond H. Thomason (eds.) - 2008 - Center for the Study of Language and Information Publications.
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  11.  20
    Two formalizations of context: a comparison.Paolo Bouquet & Luciano Serafini - 2001 - In P. Bouquet V. Akman (ed.), Modeling and Using Context. Springer. pp. 87--101.
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  12.  25
    On relating heterogeneous elements from different ontologies.Chiara Ghidini, Luciano Serafini & Sergio Tessaris - 2007 - In D. C. Richardson B. Kokinov (ed.), Modeling and Using Context. Springer. pp. 234--247.
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  13.  10
    Corpus-based terminological evaluation of ontologies.Marco Rospocher, Sara Tonelli, Luciano Serafini & Emanuele Pianta - 2012 - Applied ontology 7 (4):429-448.
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  14.  36
    ML systems: A proof theory for contexts. [REVIEW]Luciano Serafini & Fausto Giunchiglia - 2002 - Journal of Logic, Language and Information 11 (4):471-518.
    In the last decade the concept of context has been extensivelyexploited in many research areas, e.g., distributed artificialintelligence, multi agent systems, distributed databases, informationintegration, cognitive science, and epistemology. Three alternative approaches to the formalization of the notion ofcontext have been proposed: Giunchiglia and Serafini's Multi LanguageSystems (ML systems), McCarthy's modal logics of contexts, andGabbay's Labelled Deductive Systems.Previous papers have argued in favor of ML systems with respect to theother approaches. Our aim in this paper is to support these arguments froma (...)
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