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135
Preference Elicitation in Combinatorial Auctions (Extended Abstract)
 IN PROCEEDINGS OF THE ACM CONFERENCE ON ELECTRONIC COMMERCE (ACMEC
, 2001
"... Combinatorial auctions (CAs) where bidders can bid on bundles of items can be very desirable market mechanisms when the items sold exhibit complementarity and/or substitutability, so the bidder's valuations for bundles are not additive. However, in a basic CA, the bidders may need to bid on e ..."
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Cited by 118 (30 self)
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Combinatorial auctions (CAs) where bidders can bid on bundles of items can be very desirable market mechanisms when the items sold exhibit complementarity and/or substitutability, so the bidder's valuations for bundles are not additive. However, in a basic CA, the bidders may need to bid on exponentially many bundles, leading to di#culties in determining those valuations, undesirable information revelation, and unnecessary communication. In this paper we present a design of an auctioneer agent that uses topological structure inherent in the problem to reduce the amount of information that it needs from the bidders. An analysis tool is presented as well as data structures for storing and optimally assimilating the information received from the bidders. Using this information, the agent then narrows down the set of desirable (welfaremaximizing or Paretoe#cient) allocations, and decides which questions to ask next. Several algorithms are presented that ask the bidders for value, order, and rank information. A method is presented for making the elicitor incentive compatible.
Exploiting Structure to Efficiently Solve Large Scale Partially Observable Markov Decision Processes
, 2005
"... Partially observable Markov decision processes (POMDPs) provide a natural and principled framework to model a wide range of sequential decision making problems under uncertainty. To date, the use of POMDPs in realworld problems has been limited by the poor scalability of existing solution algorithm ..."
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Cited by 91 (6 self)
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Partially observable Markov decision processes (POMDPs) provide a natural and principled framework to model a wide range of sequential decision making problems under uncertainty. To date, the use of POMDPs in realworld problems has been limited by the poor scalability of existing solution algorithms, which can only solve problems with up to ten thousand states. In fact, the complexity of finding an optimal policy for a finitehorizon discrete POMDP is PSPACEcomplete. In practice, two important sources of intractability plague most solution algorithms: large policy spaces and large state spaces. On the other hand,
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 ..."
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Cited by 67 (12 self)
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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.
PointBased Value Iteration for Continuous POMDPs
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... We propose a novel approach to optimize Partially Observable Markov Decisions Processes (POMDPs) defined on continuous spaces. To date, most algorithms for modelbased POMDPs are restricted to discrete states, actions, and observations, but many realworld problems such as, for instance, robot na ..."
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Cited by 65 (4 self)
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We propose a novel approach to optimize Partially Observable Markov Decisions Processes (POMDPs) defined on continuous spaces. To date, most algorithms for modelbased POMDPs are restricted to discrete states, actions, and observations, but many realworld problems such as, for instance, robot navigation, are naturally defined on continuous spaces. In this work, we demonstrate that the value function for continuous POMDPs is convex in the beliefs over continuous state spaces, and piecewiselinear convex for the particular case of discrete observations and actions but still continuous states. We also demonstrate that continuous Bellman backups are contracting and isotonic ensuring the monotonic convergence of valueiteration algorithms. Relying on those properties, we extend the PERSEUS algorithm, originally developed for discrete POMDPs, to work in continuous state spaces by representing the observation, transition, and reward models using Gaussian mixtures, and the beliefs using Gaussian mixtures or particle sets. With these representations, the integrals that appear in the Bellman backup can be computed in closed form and, therefore, the algorithm is computationally feasible. Finally, we further extend PERSEUS to deal with continuous action and observation sets by designing effective sampling approaches.
Cooperative Negotiation in Autonomic Systems Using Incremental Utility Elicitation
 In Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence
, 2003
"... Decentralized resource allocation is a key problem for largescale autonomic (or selfmanaging) computing systems. Motivated by a data center scenario, we explore efficient techniques for resolving resource conflicts via cooperative negotiation. ..."
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Cited by 50 (9 self)
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Decentralized resource allocation is a key problem for largescale autonomic (or selfmanaging) computing systems. Motivated by a data center scenario, we explore efficient techniques for resolving resource conflicts via cooperative negotiation.
Uncertainty in preference elicitation and aggregation
 In AAAI’07
, 2007
"... Uncertainty arises in preference aggregation in several ways. There may, for example, be uncertainty in the votes or the voting rule. Such uncertainty can introduce computational complexity in determining which candidate or candidates can or must win the election. In this paper, we survey recent wor ..."
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Cited by 50 (12 self)
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Uncertainty arises in preference aggregation in several ways. There may, for example, be uncertainty in the votes or the voting rule. Such uncertainty can introduce computational complexity in determining which candidate or candidates can or must win the election. In this paper, we survey recent work in this area and give some new results. We argue, for example, that the set of possible winners can be computationally harder to compute than the necessary winner. As a second example, we show that, even if the unknown votes are assumed to be singlepeaked, it remains computationally hard to compute the possible and necessary winners, or to manipulate the election.
Preference Elicitation for Interface Optimization
 In Proceedings of UIST 2005
, 2005
"... Decisiontheoretic optimization is becoming a popular tool in the user interface community, but creating accurate cost (or utility) functions has become a bottleneck  in most cases the numerous parameters of these functions are chosen manually, which is a tedious and errorprone process. This pap ..."
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Cited by 49 (11 self)
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Decisiontheoretic optimization is becoming a popular tool in the user interface community, but creating accurate cost (or utility) functions has become a bottleneck  in most cases the numerous parameters of these functions are chosen manually, which is a tedious and errorprone process. This paper describes ARNAULD, a general interactive tool for eliciting user preferences concerning concrete outcomes and using this feedback to automatically learn a factored cost function. We empirically evaluate our machine learning algorithm and two automatic query generation approaches and report on an informal user study.
Preferencebased search using examplecritiquing with suggestions
 Journal of Artificial Intelligence Research
, 2006
"... We consider interactive tools that help users search for their most preferred item in a large collection of options. In particular, we examine examplecritiquing, a technique for enabling users to incrementally construct preference models by critiquing example options that are presented to them. We ..."
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Cited by 47 (4 self)
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We consider interactive tools that help users search for their most preferred item in a large collection of options. In particular, we examine examplecritiquing, a technique for enabling users to incrementally construct preference models by critiquing example options that are presented to them. We present novel techniques for improving the examplecritiquing technology by adding suggestions to its displayed options. Such suggestions are calculated based on an analysis of users’ current preference model and their potential hidden preferences. We evaluate the performance of our modelbased suggestion techniques with both synthetic and real users. Results show that such suggestions are highly attractive to users and can stimulate them to express more preferences to improve the chance of identifying their most preferred item by up to 78%. 1.
Active Collaborative Filtering
 In Proceedings of the Nineteenth Annual Conference on Uncertainty in Artificial Intelligence
, 2003
"... Collaborative filtering (CF) allows the preferences of multiple users to be pooled to make recommendations regarding unseen products. We consider in this paper the problem of online and interactive CF: given the current ratings associated with a user, what queries (new ratings) would most improve th ..."
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Cited by 43 (8 self)
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Collaborative filtering (CF) allows the preferences of multiple users to be pooled to make recommendations regarding unseen products. We consider in this paper the problem of online and interactive CF: given the current ratings associated with a user, what queries (new ratings) would most improve the quality of the recommendations made? We cast this in terms of expected value of information (EVOI)