Results 1  10
of
40
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 ..."
Abstract

Cited by 317 (4 self)
 Add to MetaCart
(Show Context)
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.
Preferencebased Constrained Optimization with CPnets
 Computational Intelligence
, 2001
"... Many AI tasks, such as product configuration, decision support, and the construction of autonomous agents, involve a process of constrained optimization, that is, optimization of behavior or choices subject to given constraints. In this paper we present an approach for constrained optimization based ..."
Abstract

Cited by 66 (11 self)
 Add to MetaCart
(Show Context)
Many AI tasks, such as product configuration, decision support, and the construction of autonomous agents, involve a process of constrained optimization, that is, optimization of behavior or choices subject to given constraints. In this paper we present an approach for constrained optimization based on a set of hard constraints and a preference ordering represented using a CPnetwork  a graphical model for representing qualitative preference information. This approach offers both pragmatic and computational advantages. First, it provides a convenient and intuitive tool for specifying the problem, and in particular, the decision maker's preferences. Second, it provides an algorithm for finding the most preferred feasible outcomes that has the following anytime property: the set of preferred feasible outcomes are enumerated without backtracking. In particular, the first feasible solution generated by this algorithm is optimal.
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 ..."
Abstract

Cited by 63 (6 self)
 Add to MetaCart
(Show Context)
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.
mCP Nets: Representing and Reasoning with Preferences of Multiple Agents
 In Proceedings of the Eighteenth National Conference on Artificial Intelligence, 239–246. Menlo Park, CA: Association for the Advancement of Artificial Intelligence
, 2004
"... We introduce mCP nets, an extension of the CP net formalism to model and handle the qualitative and conditional preferences of multiple agents. We give a number of different semantics for reasoning with mCP nets. The semantics are all based on the idea of individual agents voting. We describe how ..."
Abstract

Cited by 58 (13 self)
 Add to MetaCart
We introduce mCP nets, an extension of the CP net formalism to model and handle the qualitative and conditional preferences of multiple agents. We give a number of different semantics for reasoning with mCP nets. The semantics are all based on the idea of individual agents voting. We describe how to test optimality and preference ordering within a mCP net, and we give complexity results for such tasks. We also discuss whether the voting schemes fairly combine together the preferences of the individual agents.
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 ..."
Abstract

Cited by 51 (12 self)
 Add to MetaCart
(Show Context)
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 ..."
Abstract

Cited by 49 (10 self)
 Add to MetaCart
(Show Context)
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.
DecisionTheoretic Golog with Qualitative Preferences
 In KR
, 2006
"... Personalization is becoming increasingly important in agent programming, particularly as it relates to the Web. We propose to develop underspecified, taskspecific agent programs, and to automatically personalize them to the preferences of individual users. To this end, we propose a framework for ag ..."
Abstract

Cited by 24 (2 self)
 Add to MetaCart
(Show Context)
Personalization is becoming increasingly important in agent programming, particularly as it relates to the Web. We propose to develop underspecified, taskspecific agent programs, and to automatically personalize them to the preferences of individual users. To this end, we propose a framework for agent programming that integrates rich, nonMarkovian, qualitative user preferences expressed in a linear temporal logic with quantitative Markovian reward functions. We begin with DTGOLOG, a firstorder, decisiontheoretic agent programming language in the situation calculus. We present an algorithm that compiles qualitative preferences into GOLOG programs and prove it sound and complete with respect to the space of solutions. To integrate these preferences into DTGOLOG we introduce the notion of multiprogram synchronization and restate the semantics of the language as a transition semantics. We demonstrate the utility of this framework with an application to personalized travel planning over the Web. To the best of our knowledge this is the first work to combine qualitative and quantitative preferences for agent programming. Further, while the focus of this paper is on the integration of qualitative and quantitative preferences, a side effect of this work is realization of the simpler task of integrating qualitative preferences alone into agent programming as well as the generation of GOLOG programs from LTL formulae. 1
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 ..."
Abstract

Cited by 19 (5 self)
 Add to MetaCart
(Show Context)
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
Compact valuefunction representations for qualitative preferences
 In Twentieth Conference on Uncertainty in Artificial Intelligence
, 2004
"... We consider the challenge of preference elicitation in systems that help users discover the most desirable item(s) within a given database. Past work on preference elicitation focused on structured models that provide a factored representation of users ’ preferences. Such models require less informa ..."
Abstract

Cited by 13 (4 self)
 Add to MetaCart
(Show Context)
We consider the challenge of preference elicitation in systems that help users discover the most desirable item(s) within a given database. Past work on preference elicitation focused on structured models that provide a factored representation of users ’ preferences. Such models require less information to construct and support efficient reasoning algorithms. This paper makes two substantial contributions to this area: (1) Strong representation theorems for factored value functions. (2) A methodology that utilizes our representation results to address the problem of optimal item selection. 1
Hard and soft constraints for reasoning about qualitative conditional preferences
"... Abstract. Many real life optimization problems are defined in terms of both hard and soft constraints, and qualitative conditional preferences. However, there is as yet no single framework for combined reasoning about these three kinds of information. In this paper we study how to exploit classical ..."
Abstract

Cited by 12 (2 self)
 Add to MetaCart
(Show Context)
Abstract. Many real life optimization problems are defined in terms of both hard and soft constraints, and qualitative conditional preferences. However, there is as yet no single framework for combined reasoning about these three kinds of information. In this paper we study how to exploit classical and soft constraint solvers for handling qualitative preference statements such as those captured by the CPnets model. In particular, we show how hard constraints are sufficient to model the optimal outcomes of a possibly cyclic CPnet, and how soft constraints can faithfully approximate the semantics of acyclic conditional preference statements whilst improving the computational efficiency of reasoning about these statements. 1