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  1. Ontology Merging as Social Choice.Daniele Porello & Ulle Endriss - 2014 - Journal of Logic and Computation 24 (6):1229--1249.
    The problem of merging several ontologies has important applications in the Semantic Web, medical ontology engineering and other domains where information from several distinct sources needs to be integrated in a coherent manner.We propose to view ontology merging as a problem of social choice, i.e. as a problem of aggregating the input of a set of individuals into an adequate collective decision. That is, we propose to view ontology merging as ontology aggregation. As a first step in this direction, we (...)
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  2. Complexity of Judgment Aggregation.Ulle Endriss, Umberto Grandi & Daniele Porello - 2012 - Journal of Artificial Intelligence Research 45:481--514.
    We analyse the computational complexity of three problems in judgment aggregation: (1) computing a collective judgment from a profile of individual judgments (the winner determination problem); (2) deciding whether a given agent can influence the outcome of a judgment aggregation procedure in her favour by reporting insincere judgments (the strategic manipulation problem); and (3) deciding whether a given judgment aggregation scenario is guaranteed to result in a logically consistent outcome, independently from what the judgments supplied by the individuals are (the (...)
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    First-Order Logic Formalisation of Impossibility Theorems in Preference Aggregation.Umberto Grandi & Ulle Endriss - 2013 - Journal of Philosophical Logic 42 (4):595-618.
    In preference aggregation a set of individuals express preferences over a set of alternatives, and these preferences have to be aggregated into a collective preference. When preferences are represented as orders, aggregation procedures are called social welfare functions. Classical results in social choice theory state that it is impossible to aggregate the preferences of a set of individuals under different natural sets of axiomatic conditions. We define a first-order language for social welfare functions and we give a complete axiomatisation for (...)
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    Modelling Multilateral Negotiation in Linear Logic.Daniele Porello & Ulle Endriss - 2010 - In {ECAI} 2010 - 19th European Conference on Artificial Intelligence, Lisbon, Portugal, August 16-20, 2010, Proceedings. pp. 381--386.
    We show how to embed a framework for multilateral negotiation, in which a group of agents implement a sequence of deals concerning the exchange of a number of resources, into linear logic. In this model, multisets of goods, allocations of resources, preferences of agents, and deals are all modelled as formulas of linear logic. Whether or not a proposed deal is rational, given the preferences of the agents concerned, reduces to a question of provability, as does the question of whether (...)
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  5.  47
    Modelling Combinatorial Auctions in Linear Logic.Daniele Porello & Ulle Endriss - 2010 - In Principles of Knowledge Representation and Reasoning: Proceedings of the Twelfth International Conference, {KR} 2010, Toronto, Ontario, Canada, May 9-13, 2010.
    We show that linear logic can serve as an expressive framework in which to model a rich variety of combinatorial auction mechanisms. Due to its resource-sensitive nature, linear logic can easily represent bids in combinatorial auctions in which goods may be sold in multiple units, and we show how it naturally generalises several bidding languages familiar from the literature. Moreover, the winner determination problem, i.e., the problem of computing an allocation of goods to bidders producing a certain amount of revenue (...)
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  6.  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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    Abstract Models for Dialogue Protocols.Raquel Fernández & Ulle Endriss - 2007 - Journal of Logic, Language and Information 16 (2):121-140.
    We examine a variety of dialogue protocols, taking inspiration from two fields: natural language dialogue modelling and multiagent systems. In communicative interaction, one can identify different features that may increase the complexity of the dialogue structure. This motivates a hierarchy of abstract models for protocols that takes as a starting point protocols based on deterministic finite automata. From there, we proceed by looking at particular examples that justify either an enrichment or a restriction of the initial model.
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    Aggregating Dependency Graphs Into Voting Agendas in Multi-Issue Elections.Stephane Airiau, Ulle Endriss, Umberto Grandi, Daniele Porello & Joel Uckelman - 2011 - In {IJCAI} 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011. pp. 18--23.
    Many collective decision making problems have a combinatorial structure: the agents involved must decide on multiple issues and their preferences over one issue may depend on the choices adopted for some of the others. Voting is an attractive method for making collective decisions, but conducting a multi-issue election is challenging. On the one hand, requiring agents to vote by expressing their preferences over all combinations of issues is computationally infeasible; on the other, decomposing the problem into several elections on smaller (...)
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  9. Distributed Fair Allocation of Indivisible Goods.Yann Chevaleyre, Ulle Endriss & Nicolas Maudet - 2017 - Artificial Intelligence 242:1-22.
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    Preservation of Semantic Properties in Collective Argumentation: The Case of Aggregating Abstract Argumentation Frameworks.Weiwei Chen & Ulle Endriss - 2019 - Artificial Intelligence 269:27-48.
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    Computational Social Choice, 3–5 September.Ulle Endriss - 2008 - The Reasoner 2 (10):9-10.
  12. Graph Aggregation.Ulle Endriss & Umberto Grandi - 2017 - Artificial Intelligence 245:86-114.
  13. Sincerity and Manipulation Under Approval Voting.Ulle Endriss - 2013 - Theory and Decision 74 (3):335-355.
    Under approval voting, each voter can nominate as many candidates as she wishes and the election winners are those candidates that are nominated most often. A voter is said to have voted sincerely if she prefers all those candidates she nominated to all other candidates. As there can be a set of winning candidates rather than just a single winner, a voter’s incentives to vote sincerely will depend on what assumptions we are willing to make regarding the principles by which (...)
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  14. Lifting Integrity Constraints in Binary Aggregation.Umberto Grandi & Ulle Endriss - 2013 - Artificial Intelligence 199:45-66.
  15. Computational Social Choice 2006.Jerome Lang & Ulle Endriss (eds.) - 2006 - University of Amsterdam.
  16. Compactly Representing Utility Functions Using Weighted Goals and the Max Aggregator.Joel Uckelman & Ulle Endriss - 2010 - Artificial Intelligence 174 (15):1222-1246.
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  17. Incentive Engineering for Boolean Games.Michael Wooldridge, Ulle Endriss, Sarit Kraus & Jérôme Lang - 2013 - Artificial Intelligence 195:418-439.