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66
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
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.
Planning with goal preferences and constraints
, 2004
"... In classical planning, the planner is given a concrete goal; it returns a plan for it or a failure message. In the latter case, the user can either quit or modify the goal. For many applications, it is more convenient to let the user provide a more elaborate specification consisting of constraints ..."
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Cited by 52 (4 self)
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In classical planning, the planner is given a concrete goal; it returns a plan for it or a failure message. In the latter case, the user can either quit or modify the goal. For many applications, it is more convenient to let the user provide a more elaborate specification consisting of constraints and preferences over possible goal states. Then, let the system discover a plan for the most desirable among the feasible goal states. To materialize such an approach we require a formalism for specifying preferences and constraints over goals and an algorithm for solving the resulting constrained optimization problem. In this work we motivate the need for planning with preferences and constraints, suggest a rich, yet intuitive formalism for representing goal preferences in the context of a deterministic action model, discuss some of its properties, propose an efficient algorithm for planning with preferences and constraints based on this formalism, and provide extensive experimental analysis in an interesting new domain of configuration planning.
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.
Winner determination in sequential majority voting
 In Proceedings of the ECAI2006 Multidisciplinary Workshop on Advances in Preference Handling
, 2007
"... Preferences can be aggregated using a voting rule. Each agent gives their preference orderings over a set of candidates, and a voting rule is used to compute the winner. We consider voting rules which perform a sequence of pairwise comparisons between two candidates, where the result of each is comp ..."
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Cited by 45 (14 self)
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Preferences can be aggregated using a voting rule. Each agent gives their preference orderings over a set of candidates, and a voting rule is used to compute the winner. We consider voting rules which perform a sequence of pairwise comparisons between two candidates, where the result of each is computed by a majority vote. The winner thus depends on the chosen sequence of comparisons, which can be represented by a binary tree. There are candidates that will win in some trees (called possible winners) or in all trees (called Condorcet winners). While it is easy to find the possible and Condorcet winners, we prove that it is difficult if we insist that the tree is balanced. This restriction is therefore enough to make voting difficult for the chair to manipulate. We also consider the situation where we lack complete informations about preferences, and determine the computational complexity of computing possible and Condorcet winners in this extended case. 1
Planning with preferences using logic programming
, 2006
"... We present a declarative language,PP, for the highlevel specification of preferences between possible solutions (or trajectories) of a planning problem. This novel language allows users to elegantly express nontrivial, multidimensional preferences and priorities over such preferences. The semanti ..."
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Cited by 31 (3 self)
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We present a declarative language,PP, for the highlevel specification of preferences between possible solutions (or trajectories) of a planning problem. This novel language allows users to elegantly express nontrivial, multidimensional preferences and priorities over such preferences. The semantics ofPP allows the identification of most preferred trajectories for a given goal. We also provide an answer set programming implementation of planning problems with PP preferences.
Planning as satisfiability with preferences
 In AAAI Conference on Artificial Intelligence
, 2007
"... Planning as Satisfiability is one of the most wellknown and effective technique for classical planning: SATPLAN has been the winning system in the deterministic track for optimal planners in the 4th International Planning Competition (IPC) and a cowinner in the 5th IPC. In this paper we extend the ..."
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Cited by 28 (9 self)
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Planning as Satisfiability is one of the most wellknown and effective technique for classical planning: SATPLAN has been the winning system in the deterministic track for optimal planners in the 4th International Planning Competition (IPC) and a cowinner in the 5th IPC. In this paper we extend the Planning as Satisfiability approach in order to handle preferences and SATPLAN in order to solve problems with simple preferences. The resulting system, SATPLAN(P) is competitive with SGPLAN, the winning system in the category “simple preferences ” at the last IPC. Further, we show that SATPLAN(P) performances are (almost) always comparable to those of SATPLAN when solving the same problems without preferences: in other words, introducing simple preferences in SATPLAN does not affect its performances. This latter result is due both to the particular mechanism we use in order to incorporate preferences in SATPLAN and to the relative low number of soft goals (each corresponding to a simple preference) usually present in planning problems. Indeed, if we consider the issue of determining minimal plans (corresponding to problems with thousands of preferences) the performances of SATPLAN(P) are comparable to those of SATPLAN in many cases, but can be significantly worse when the number of preferences is very high compared to the total number of variables in the problem. Our analysis is conducted considering both qualitative and quantitative preferences, different reductions from quantitative to qualitative ones, and most of the propositional planning domains from the IPCs and that SATPLAN can handle.
Preference Handling  An Introductory Tutorial
"... We present a tutorial introduction to the area of preference handling – one of the core issues in the design of any system that automates or supports decision making. The main goal of this tutorial is to provide a framework, or perspective, within which current work on preference handling – represen ..."
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Cited by 23 (0 self)
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We present a tutorial introduction to the area of preference handling – one of the core issues in the design of any system that automates or supports decision making. The main goal of this tutorial is to provide a framework, or perspective, within which current work on preference handling – representation, reasoning, and elicitation – can be understood. Our intention is not to provide a technical description of the diverse methods used, but rather, to provide a general perspective on the problem and its varied solutions and to highlight central ideas and techniques.
Consistency and Constrained Optimisation for Conditional Preferences
"... TCPnets are an extension of CPnets which allow the expression of conditional relative importance of pairs of variables. In this paper it is shown that a simple logic of conditional preferences can be used to express TCPnet orders, as well as being able to represent much stronger statements of imp ..."
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Cited by 21 (7 self)
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TCPnets are an extension of CPnets which allow the expression of conditional relative importance of pairs of variables. In this paper it is shown that a simple logic of conditional preferences can be used to express TCPnet orders, as well as being able to represent much stronger statements of importance than TCPnets allow. The paper derives various sufficient conditions for a subset of the logical language to be consistent, and develops methods for finding a total order on outcomes which is consistent with the set of conditional preferences. This leads also to an approach to the problem of constrained optimisation.
Compact preference representation for boolean games
 In Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI
, 2006
"... Abstract. Boolean games, introduced by [15, 14], allow for expressing compactly twoplayers zerosum static games with binary preferences: an agent’s strategy consists of a truth assignment of the propositional variables she controls, and a player’s preferences is expressed by a plain propositional ..."
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Cited by 16 (4 self)
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Abstract. Boolean games, introduced by [15, 14], allow for expressing compactly twoplayers zerosum static games with binary preferences: an agent’s strategy consists of a truth assignment of the propositional variables she controls, and a player’s preferences is expressed by a plain propositional formula. These restrictions (twoplayers, zerosum, binary preferences) strongly limit the expressivity of the framework. While the first two can be easily encompassed by defining the agents ’ preferences as an arbitrary nuple of propositional formulas, relaxing the last one needs Boolean games to be coupled with a propositional language for compact preference representation. In this paper, we consider generalized Boolean games where players ’ preferences are expressed within two of these languages: prioritized goals and propositionalized CPnets. 1