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CPnets: A Tool for Representing and Reasoning with Conditional Ceteris Paribus Preference Statements
 JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
, 2004
"... Information about user preferences plays a key role in automated decision making. In many domains it is desirable to assess such preferences in a qualitative rather than quantitative way. In this paper, we propose a qualitative graphical representation of preferences that reflects conditional dep ..."
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Cited by 222 (4 self)
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Information about user preferences plays a key role in automated decision making. In many domains it is desirable to assess such preferences in a qualitative rather than quantitative way. In this paper, we propose a qualitative graphical representation of preferences that reflects conditional dependence and independence of preference statements under a ceteris paribus (all else being equal) interpretation. Such a representation is often compact and arguably quite natural in many circumstances. We provide a formal semantics for this model, and describe how the structure of the network can be exploited in several inference tasks, such as determining whether one outcome dominates (is preferred to) another, ordering a set outcomes according to the preference relation, and constructing the best outcome subject to available evidence.
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 68 (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.
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 48 (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
, 2005
"... 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 a ..."
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Cited by 43 (3 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.
Efficient utility functions for ceteris paribus preferences
 In Proceedings of the Eighteenth National Conference on Artificial Intelligence
, 2002
"... Although ceteris paribus preference statements concisely represent one natural class of preferences over outcomes or goals, many applications of such preferences require numeric utility function representations to achieve computational efficiency. We provide algorithms, complete for finite universes ..."
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Cited by 41 (3 self)
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Although ceteris paribus preference statements concisely represent one natural class of preferences over outcomes or goals, many applications of such preferences require numeric utility function representations to achieve computational efficiency. We provide algorithms, complete for finite universes of binary features, for converting a set of qualitative ceteris paribus preferences into quantitative utility functions.
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 36 (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 31 (8 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.
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 18 (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.
Constraintbased preferential optimization
 in Proceedings of AAAI05
, 2005
"... We first show that the optimal and undominated outcomes of an unconstrained (and possibly cyclic) CPnet are the solutions of a set of hard constraints. We then propose a new algorithm for finding the optimal outcomes of a constrained CPnet which makes use of hard constraint solving. Unlike previou ..."
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Cited by 17 (5 self)
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We first show that the optimal and undominated outcomes of an unconstrained (and possibly cyclic) CPnet are the solutions of a set of hard constraints. We then propose a new algorithm for finding the optimal outcomes of a constrained CPnet which makes use of hard constraint solving. Unlike previous algorithms, this new algorithm works even with cyclic CPnets. In addition, the algorithm is not tied to CPnets, but can work with any preference formalism which produces a preorder over the outcomes. We also propose an approximation method which weakens the preference ordering induced by the CPnet, returning a larger set of outcomes, but provides a significant computational advantage. Finally, we describe a weighted constraint approach that allows to find good solutions even when optimals do not exist. 1