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96
Issues in multiagent resource allocation
 INFORMATICA
, 2006
"... The allocation of resources within a system of autonomous agents, that not only have preferences over alternative allocations of resources but also actively participate in computing an allocation, is an exciting area of research at the interface of Computer Science and Economics. This paper is a sur ..."
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Cited by 104 (17 self)
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The allocation of resources within a system of autonomous agents, that not only have preferences over alternative allocations of resources but also actively participate in computing an allocation, is an exciting area of research at the interface of Computer Science and Economics. This paper is a survey of some of the most salient issues in Multiagent Resource Allocation. In particular, we review various languages to represent the preferences of agents over alternative allocations of resources as well as different measures of social welfare to assess the overall quality of an allocation. We also discuss pertinent issues regarding allocation procedures and present important complexity results. Our presentation of theoretical issues is complemented by a discussion of software packages for the simulation of agentbased market places. We also introduce four major application areas for Multiagent Resource Allocation, namely industrial procurement, sharing of satellite resources, manufacturing control, and grid computing.
Voting procedures with incomplete preferences
 in Proc. IJCAI05 Multidisciplinary Workshop on Advances in Preference Handling
, 2005
"... We extend the application of a voting procedure (usually defined on complete preference relations over candidates) when the voters ’ preferences consist of partial orders. We define possible (resp. necessary) winners for a given partial preference profile R with respect to a given voting procedure a ..."
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Cited by 95 (11 self)
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We extend the application of a voting procedure (usually defined on complete preference relations over candidates) when the voters ’ preferences consist of partial orders. We define possible (resp. necessary) winners for a given partial preference profile R with respect to a given voting procedure as the candidates being the winners in some (resp. all) of the complete extensions of R. We show that, although the computation of possible and necessary winners may be hard in general case, it is polynomial for the family of positional scoring procedures. We show that the possible and necessary Condorcet winners for a partial preference profile can be computed in polynomial time as well. Lastly, we point out connections to vote manipulation and elicitation. 1
A short introduction to computational social choice
 Proc. 33rd Conference on Current Trends in Theory and Practice of Computer Science
, 2007
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On graphical modeling of preference and importance
, 2006
"... In recent years, CPnets have emerged as a useful tool for supporting preference elicitation, reasoning, and representation. CPnets capture and support reasoning with qualitative conditional preference statements, statements that are relatively natural for users to express. In this paper, we extend ..."
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Cited by 63 (6 self)
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In recent years, CPnets have emerged as a useful tool for supporting preference elicitation, reasoning, and representation. CPnets capture and support reasoning with qualitative conditional preference statements, statements that are relatively natural for users to express. In this paper, we extend the CPnets formalism to handle another class of very natural qualitative statements one often uses in expressing preferences in daily life – statements of relative importance of attributes. The resulting formalism, TCPnets, maintains the spirit of CPnets, in that it remains focused on using only simple and natural preference statements, uses the ceteris paribus semantics, and utilizes a graphical representation of this information to reason about its consistency and to perform, possibly constrained, optimization using it. The extra expressiveness it provides allows us to better model tradeoffs users would like to make, more faithfully representing their preferences. 1.
Extending CPnets with stronger conditional preference statements
 In Proceedings of AAAI04
, 2004
"... A logic of conditional preferences is defined, with a language which allows the compact representation of certain kinds of conditional preference statements, a semantics and a proof theory. CPnets can be expressed in this language, and the semantics and proof theory generalise those of CPnets. Des ..."
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Cited by 51 (12 self)
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A logic of conditional preferences is defined, with a language which allows the compact representation of certain kinds of conditional preference statements, a semantics and a proof theory. CPnets can be expressed in this language, and the semantics and proof theory generalise those of CPnets. Despite being substantially more expressive, the formalism maintains important properties of CPnets; there are simple sufficient conditions for consistency, and, under these conditions, optimal outcomes can be efficiently generated. It is also then easy to find a total order on outcomes which extends the conditional preference order, and an approach to constrained optimisation can be used which generalises a natural approach for CPnets. Some results regarding the expressive power of CPnets are also given.
The Computational Complexity of Dominance and Consistency in CPNets
"... We investigate the computational complexity of testing dominance and consistency in CPnets. Previously, the complexity of dominance has been determined for restricted classes in which the dependency graph of the CPnet is acyclic. However, there are preferences of interest that define cyclic depend ..."
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Cited by 49 (10 self)
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We investigate the computational complexity of testing dominance and consistency in CPnets. Previously, the complexity of dominance has been determined for restricted classes in which the dependency graph of the CPnet is acyclic. However, there are preferences of interest that define cyclic dependency graphs; these are modeled with general CPnets. In our main results, we show here that both dominance and consistency for general CPnets are PSPACEcomplete. We then consider the concept of strong dominance, dominance equivalence and dominance incomparability, and several notions of optimality, and identify the complexity of the corresponding decision problems. The reductions used in the proofs are from STRIPS planning, and thus reinforce the earlier established connections between both areas.
Efficiency and envyfreeness in fair division of indivisible goods: Logical representation and complexity
 In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI2005
, 2005
"... and complexity ..."
Incompleteness and Incomparability in Preference Aggregation
 In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007
, 2007
"... We consider how to combine the preferences of multiple agents despite the presence of incompleteness and incomparability in their preference orderings. An agent’s preference ordering may be incomplete because, for example, there is an ongoing preference elicitation process. It may also contain incom ..."
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Cited by 43 (16 self)
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We consider how to combine the preferences of multiple agents despite the presence of incompleteness and incomparability in their preference orderings. An agent’s preference ordering may be incomplete because, for example, there is an ongoing preference elicitation process. It may also contain incomparability as this is useful, for example, in multicriteria scenarios. We focus on the problem of computing the possible and necessary winners, that is, those outcomes which can be or always are the most preferred for the agents. Possible and necessary winners are useful in many scenarios including preference elicitation. First we show that computing the sets of possible and necessary winners is in general a difficult problem as is providing a good approximation of such sets. Then we identify general properties of the preference aggregation function which are sufficient for such sets to be computed in polynomial time. Finally, we show how possible and necessary winners can be used to focus preference elicitation. 1
Reasoning about soft constraints and conditional preferences: Complexity results and approximation techniques
 In Proceedings of IJCAI2003
, 2003
"... Many real life optimization problems contain both hard and soft constraints, as well as qualitative conditional preferences. However, there is no single formalism to specify all three kinds of information. We therefore propose a framework, based on both CPnets and soft constraints, that handles bot ..."
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Cited by 40 (16 self)
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Many real life optimization problems contain both hard and soft constraints, as well as qualitative conditional preferences. However, there is no single formalism to specify all three kinds of information. We therefore propose a framework, based on both CPnets and soft constraints, that handles both hard and soft constraints as well as conditional preferences efficiently and uniformly. We study the complexity of testing the consistency of preference statements, and show how soft constraints can faithfully approximate the semantics of conditional preference statements whilst improving the computational complexity. 1
Expressive power of weighted propositional formulas for cardinal preference modeling
 In Proceedings of KR’07
, 2007
"... As proposed in various places, a set of propositional formulas, each associated with a numerical weight, can be used to model the preferences of an agent in combinatorial domains. If the range of possible choices can be represented by the set of possible assignments of propositional symbols to truth ..."
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Cited by 36 (7 self)
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As proposed in various places, a set of propositional formulas, each associated with a numerical weight, can be used to model the preferences of an agent in combinatorial domains. If the range of possible choices can be represented by the set of possible assignments of propositional symbols to truth values, then the utility of an assignment is given by the sum of the weights of the formulas it satisfies. Our aim in this paper is twofold: (1) to establish correspondences between certain types of weighted formulas and wellknown classes of utility functions (such as monotonic, concave or kadditive functions); and (2) to obtain results on the comparative succinctness of different types of weighted formulas for representing the same class of utility functions.