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123
A POMDP Formulation of Preference Elicitation Problems
, 2002
"... Preference elicitation is a key problem facing the deployment of intelligent systems that make or recommend decisions on the behalf of users. Since not all aspects of a utility function have the same impact on objectlevel decision quality, determining which information to extract from a user i ..."
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Cited by 134 (24 self)
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Preference elicitation is a key problem facing the deployment of intelligent systems that make or recommend decisions on the behalf of users. Since not all aspects of a utility function have the same impact on objectlevel decision quality, determining which information to extract from a user is itself a sequential decision problem, balancing the amount of elicitation effort and time with decision quality.
UCPnetworks: A directed graphical representation of conditional utilities
 In Proceedings of UAI’01
, 2001
"... We propose a directed graphical representation of utility functions, called UCPnetworks, that combines aspects of two existing preference models: generalized additive models and CPnetworks. The network decomposes a utility function into a number of additive factors, with the directionality of the ..."
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Cited by 117 (21 self)
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We propose a directed graphical representation of utility functions, called UCPnetworks, that combines aspects of two existing preference models: generalized additive models and CPnetworks. The network decomposes a utility function into a number of additive factors, with the directionality of the arcs reflecting conditional dependence in the underlying (qualitative) preference ordering under a ceteris paribus interpretation. The CPsemantics ensures that computing optimization and dominance queries is very efficient. We also demonstrate the value of this representation in decision making. Finally, we describe an interactive elicitation procedure that takes advantage of the linear nature of the constraints on “tradeoff weights ” imposed by a UCPnetwork. 1
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 108 (27 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.
Vote Elicitation: Complexity and StrategyProofness
, 2002
"... significant attention in singleagent settings. It is also a key problem in multiagent systems, but has received little attention here so far. In this setting, the agents may have different preferences that often must be aggregated using voting. ..."
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Cited by 86 (20 self)
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significant attention in singleagent settings. It is also a key problem in multiagent systems, but has received little attention here so far. In this setting, the agents may have different preferences that often must be aggregated using voting.
Active Exploration for Learning Rankings from Clickthrough Data
 KDD'07
, 2007
"... We address the task of learning rankings of documents from search engine logs of user behavior. Previous work on this problem has relied on passively collected clickthrough data. In contrast, we show that an active exploration strategy can provide data that leads to much faster learning. Specificall ..."
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Cited by 77 (5 self)
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We address the task of learning rankings of documents from search engine logs of user behavior. Previous work on this problem has relied on passively collected clickthrough data. In contrast, we show that an active exploration strategy can provide data that leads to much faster learning. Specifically, we develop a Bayesian approach for selecting rankings to present users so that interactions result in more informative training data. Our results using the TREC10 Web corpus, as well as synthetic data, demonstrate that a directed exploration strategy quickly leads to users being presented improved rankings in an online learning setting. We find that active exploration substantially outperforms passive observation and random exploration.
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 66 (11 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.
Learning an Agent's Utility Function by Observing Behavior
 In Proc. of the 18th Int’l Conf. on Machine Learning
, 2001
"... This paper considers the task of predicting the future decisions of an agent A based on his past decisions. We assume that A is rational  he uses the principle of maximum expected utility. We also assume that the probability distribution P he assigns to random events is known, so that we need only ..."
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Cited by 65 (0 self)
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This paper considers the task of predicting the future decisions of an agent A based on his past decisions. We assume that A is rational  he uses the principle of maximum expected utility. We also assume that the probability distribution P he assigns to random events is known, so that we need only infer his utility function u to model his decision process. We consider the task of using A's previous decisions to learn about u. In particular, A's past decisions can be viewed as constraints on u. If we have a prior probability distribution p(u) over u (e.g., learned from a set of utility functions in the population), we can then condition on these constraints to obtain a posterior distribution q(u). We present an efficient Markov Chain Monte Carlo scheme to generate samples from q(u), which can be used to estimate not only a single "expected" course of action for A, but a distribution over possible courses of action. We show that this capability is particularly useful in a twoplayer setting where a second learning agent is trying to optimize her own payoff, which also depends on A's actions and utilities.
Prospects for preferences
 Computational Intelligence
, 2004
"... This article examines prospects for theories and methods of preferences, both in the specific sense of the preferences of the ideal rational agents considered in economics and decision theory and in the broader interplay between reasoning and rationality considered in philosophy, psychology, and art ..."
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Cited by 65 (0 self)
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This article examines prospects for theories and methods of preferences, both in the specific sense of the preferences of the ideal rational agents considered in economics and decision theory and in the broader interplay between reasoning and rationality considered in philosophy, psychology, and artificial intelligence. Modern applications seek to employ preferences as means for specifying, designing, and controlling rational behaviors as well as descriptive means for understanding behaviors. We seek to understand the nature and representation of preferences by examining the roles, origins, meaning, structure, evolution, and application of preferences.
GAI networks for utility elicitation
 In Proccedings of the Ninth International Conference on the Principles of Knowledge Representation and Reasoning (KR’04
, 2004
"... This paper deals with preference representation and elicitation in the context of multiattribute utility theory under risk. Assuming the decision maker behaves according to the EU model, we investigate the elicitation of generalized additively decomposable utility functions on a product set (GAIdec ..."
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Cited by 55 (10 self)
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This paper deals with preference representation and elicitation in the context of multiattribute utility theory under risk. Assuming the decision maker behaves according to the EU model, we investigate the elicitation of generalized additively decomposable utility functions on a product set (GAIdecomposable utilities). We propose a general elicitation procedure based on a new graphical model called a GAInetwork. The latter is used to represent and manage independences between attributes, as junction graphs model independences between random variables in Bayesian networks. It is used to design an elicitation questionnaire based on simple lotteries involving completely specified outcomes. Our elicitation procedure is convenient for any GAIdecomposable utility function, thus enhancing the possibilities offered by UCPnetworks.
Pairwise Preference Learning and Ranking
 Proceedings of the 14th European Conference on Machine Learning
, 2003
"... We consider supervised learning of a ranking function, which is a mapping from instances to total orders over a set of labels (options). The training information consists of examples with partial (and possibly inconsistent) information about their associated rankings. From these, we induce a rank ..."
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Cited by 53 (11 self)
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We consider supervised learning of a ranking function, which is a mapping from instances to total orders over a set of labels (options). The training information consists of examples with partial (and possibly inconsistent) information about their associated rankings. From these, we induce a ranking function by reducing the original problem to a number of binary classification problems, one for each pair of labels. The main objective of this work is to investigate the tradeoff between the quality of the induced ranking function and the computational complexity of the algorithm, both depending on the amount of preference information given for each example. To this end, we present theoretical results on the complexity of pairwise preference learning.