25 found
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  1. Introspective Forgetting.Hans van Ditmarsch, Andreas Herzig, Jérôme Lang & Pierre Marquis - 2009 - Synthese 169 (2):405-423.
    We model the forgetting of propositional variables in a modal logical context where agents become ignorant and are aware of each others’ or their own resulting ignorance. The resulting logic is sound and complete. It can be compared to variable-forgetting as abstraction from information, wherein agents become unaware of certain variables: by employing elementary results for bisimulation, it follows that beliefs not involving the forgotten atom(s) remain true.
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  2.  14
    Introspective Forgetting.Hans Ditmarsch, Andreas Herzig, Jérôme Lang & Pierre Marquis - 2009 - Synthese 169 (2):405-423.
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  3.  48
    From Knowledge-Based Programs to Graded Belief-Based Programs, Part I: On-Line Reasoning.Noël Laverny & Jérôme Lang - 2005 - Synthese 147 (2):277-321.
    Knowledge-based programs (KBPs) are a powerful notion for expressing action policies in which branching conditions refer to implicit knowledge and call for a deliberation task at execution time. However, branching conditions in KBPs cannot refer to possibly erroneous beliefs or to graded belief, such as “if my belief that φ holds is high then do some action α else perform some sensing action β”.
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  4.  46
    Editorial Introduction to the Special Issue LORI Guangzhou.Hans van Ditmarsch & Jérôme Lang - 2013 - Synthese 190 (1):1-4.
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  5.  7
    Representing Utility Functions Via Weighted Goals.Joel Uckelman, Yann Chevaleyre, Ulle Endriss & Jérôme Lang - 2009 - Mathematical Logic Quarterly 55 (4):341-361.
    We analyze the expressivity, succinctness, and complexity of a family of languages based on weighted propositional formulas for the representation of utility functions. The central idea underlying this form of preference modeling is to associate numerical weights with goals specified in terms of propositional formulas, and to compute the utility value of an alternative as the sum of the weights of the goals it satisfies. We define a large number of representation languages based on this idea, each characterized by a (...)
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  6.  21
    Foreword.Giacomo Bonanno, Andreas Herzig, Wiebe van der Hoek & Jérôme Lang - 2011 - Journal of Applied Non-Classical Logics 21 (3-4):263-264.
  7.  11
    Special Issue on Formal Models of Belief Change in Rational Agents.Giacomo Bonanno, James Delgrande, Jérôme Lang & Hans Rott - 2009 - Journal of Applied Logic 7 (4):363.
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  8.  68
    Effectivity Functions and Efficient Coalitions in Boolean Games.Elise Bonzon, Marie-Christine Lagasquie-Schiex & Jérôme Lang - 2012 - Synthese 187 (S1):73-103.
    Boolean games are a logical setting for representing strategic games in a succinct way, taking advantage of the expressive power and conciseness of propositional logic. A Boolean game consists of a set of players, each of which controls a set of propositional variables and has a specific goal expressed by a propositional formula. We show here that Boolean games are a very simple setting, yet sophisticated enough, for analysing the formation of coalitions. Due to the fact that players have dichotomous (...)
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  9. Lecture Notes on Artificial Intelligence 5422, Logic, Language, and Computation 7th International Tbilisi Symposium on Logic, Language, and Computation. [REVIEW]Peter Bosch, David Gabelaia & Jérôme Lang (eds.) - 2009 - Springer.
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  10. From Ordering-Based Nonmonotonic Reasoning to Conditional Logics.Luis Fariñas del Cerro, Andreas Herzig & Jérôme Lang - 1994 - Artificial Intelligence 66 (2):375-393.
  11.  16
    Guest Editors’ Introduction.James Delgrande & Jérôme Lang - 2015 - Journal of Philosophical Logic 44 (2):111-115.
    This special issue presents a selection of papers in Knowledge Representation in Artificial Intelligence , intended to illustrate the depth and breadth of current research in the area. It comes just over 25 years since a similar special issue of the Journal of Philosophical Logic appeared on the topic Philosophical Logic and Artificial Intelligence [15]. This latter special issue covered work addressing the use of logic, in one form or another, for representing and reasoning with knowledge. The papers of the (...)
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  12. Belief Extrapolation.Florence Dupin de Saint-Cyr & Jérôme Lang - 2011 - Artificial Intelligence 175 (2):760-790.
  13. Conditional Independence in Propositional Logic.Jérôme Lang, Paolo Liberatore & Pierre Marquis - 2002 - Artificial Intelligence 141 (1-2):79-121.
  14. Computational Social Choice 2006.Jerome Lang & Ulle Endriss (eds.) - 2006 - University of Amsterdam.
  15. Multi-Attribute Proportional Representation.Jérôme Lang & Piotr Skowron - 2018 - Artificial Intelligence 263:74-106.
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  16. On Propositional Definability.Jérôme Lang & Pierre Marquis - 2008 - Artificial Intelligence 172 (8-9):991-1017.
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  17. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18).Jérôme Lang (ed.) - 2018
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  18.  24
    Removing Inconsistencies in Assumption-Based Theories Through Knowledge-Gathering Actions.Jérôme Lang & Pierre Marquis - 2001 - Studia Logica 67 (2):179-214.
    In this paper, the problem of purifying an assumption-based theory KB, i.e., identifying the right extension of KB using knowledge-gathering actions (tests), is addressed. Assumptions are just normal defaults without prerequisite. Each assumption represents all the information conveyed by an agent, and every agent is associated with a (possibly empty) set of tests. Through the execution of tests, the epistemic status of assumptions can change from "plausible" to "certainly true", "certainly false" or "irrelevant", and the KB must be revised so (...)
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  19. Reasoning Under Inconsistency: A Forgetting-Based Approach.Jérôme Lang & Pierre Marquis - 2010 - Artificial Intelligence 174 (12-13):799-823.
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  20.  5
    Semantic Evaluation in Possibilistic Logic Application to Min-Max Discrete Optimisation Problems.Jérôme Lang - 1991 - In B. Bouchon-Meunier, R. R. Yager & L. A. Zadeh (eds.), Uncertainty in Knowledge Bases. Springer. pp. 260--268.
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  21. Semantic Evaluation in Possibilistic Logic.Jérôme Lang - 1991 - In B. Bouchon-Meunier, R. R. Yager & L. A. Zadeh (eds.), Uncertainty in Knowledge Bases. Springer. pp. 90--260.
     
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  22. Voting on Multi-Issue Domains with Conditionally Lexicographic Preferences.Jérôme Lang, Jérôme Mengin & Lirong Xia - 2018 - Artificial Intelligence 265:18-44.
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  23. Finding a Collective Set of Items: From Proportional Multirepresentation to Group Recommendation.Piotr Skowron, Piotr Faliszewski & Jérôme Lang - 2016 - Artificial Intelligence 241:191-216.
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  24. Incentive Engineering for Boolean Games.Michael Wooldridge, Ulle Endriss, Sarit Kraus & Jérôme Lang - 2013 - Artificial Intelligence 195:418-439.
  25.  2
    Knowledge-Based Programs as Succinct Policies for Partially Observable Domains.Bruno Zanuttini, Jérôme Lang, Abdallah Saffidine & François Schwarzentruber - 2020 - Artificial Intelligence 288:103365.
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