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35
Making rational decisions using adaptive utility elicitation
 In AAAI
, 2000
"... Rational decision making requires full knowledge of the utility function of the person affected by the decisions. However, in many cases, the task of acquiring such knowledge is not feasible due to the size of the outcome space and the complexity of the utility elicitation process. Given that the am ..."
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Cited by 127 (3 self)
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Rational decision making requires full knowledge of the utility function of the person affected by the decisions. However, in many cases, the task of acquiring such knowledge is not feasible due to the size of the outcome space and the complexity of the utility elicitation process. Given that the amount of utility information we can acquire is limited, we need to make decisions with partial utility information and should carefully select which utility elicitation questions we ask. In this paper, we propose a new approach for this problem that utilizes a prior probability distribution over the person’s utility function, perhaps learned from a population of similar people. The relevance of a utility elicitation question for the current decision problem can then be measured using its value of information. We propose an algorithm that interleaves the analysis of the decision problem and utility elicitation to allow these two tasks to inform each other. At every step, it asks the utility elicitation question giving us the highest value of information and computes the best strategy based on the information acquired so far, stopping when the expected utility loss resulting from our recommendation falls below a prespecified threshold. We show how the various steps of this algorithm can be implemented efficiently.
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 57 (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.
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.
Resourceaware wireless sensoractuator networks
 IEEE Data Engineering
, 2005
"... Innovations in wireless sensor networks (WSNs) have dramatically expanded the applicability of control technology in daytoday life, by enabling the costeffective deployment of large scale sensoractuator systems. In this paper, we discuss the issues and challenges involved in deploying controlor ..."
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Cited by 25 (0 self)
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Innovations in wireless sensor networks (WSNs) have dramatically expanded the applicability of control technology in daytoday life, by enabling the costeffective deployment of large scale sensoractuator systems. In this paper, we discuss the issues and challenges involved in deploying controloriented applications over unreliable, resourceconstrained WSNs, and describe the design of our planned Sensor Control System (SCS) that can enable the rapid development and deployment of such applications. 1
Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets
"... Bayesian approaches to utility elicitation typically adopt (myopic) expected value of information (EVOI) as a natural criterion for selecting queries. However, EVOIoptimization is usually computationally prohibitive. In this paper, we examine EVOI optimization using choice queries, queries in which ..."
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Cited by 20 (2 self)
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Bayesian approaches to utility elicitation typically adopt (myopic) expected value of information (EVOI) as a natural criterion for selecting queries. However, EVOIoptimization is usually computationally prohibitive. In this paper, we examine EVOI optimization using choice queries, queries in which a user is ask to select her most preferred product from a set. We show that, under very general assumptions, the optimal choice query w.r.t. EVOI coincides with the optimal recommendation set, that is, a set maximizing the expected utility of the user selection. Since recommendation set optimization is a simpler, submodular problem, this can greatly reduce the complexity of both exact and approximate (greedy) computation of optimal choice queries. We also examine the case where user responses to choice queries are errorprone (using both constant and mixed multinomial logit noise models) and provide worstcase guarantees. Finally we present a local search technique for query optimization that works extremely well with large outcome spaces. 1
Gaussian Process Preference Elicitation
"... Bayesian approaches to preference elicitation (PE) are particularly attractive due to their ability to explicitly model uncertainty in users ’ latent utility functions. However, previous approaches to Bayesian PE have ignored the important problem of generalizing from previous users to an unseen use ..."
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Cited by 18 (2 self)
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Bayesian approaches to preference elicitation (PE) are particularly attractive due to their ability to explicitly model uncertainty in users ’ latent utility functions. However, previous approaches to Bayesian PE have ignored the important problem of generalizing from previous users to an unseen user in order to reduce the elicitation burden on new users. In this paper, we address this deficiency by introducing a Gaussian Process (GP) prior over users ’ latent utility functions on the joint space of user and item features. We learn the hyperparameters of this GP on a set of preferences of previous users and use it to aid in the elicitation process for a new user. This approach provides a flexible model of a multiuser utility function, facilitates an efficient value of information (VOI) heuristic query selection strategy, and provides a principled way to incorporate the elicitations of multiple users back into the model. We show the effectiveness of our method in comparison to previous work on a real dataset of user preferences over sushi types. 1
On the Foundations of Expected Expected Utility
, 2001
"... Intelligent agents often need to assess user utility functions in order to make decisions on their behalf, or predict their behavior. When uncertainty exists over the precise nature of this utility function, one can model this uncertainty using a distribution over utility functions. This view l ..."
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Cited by 17 (2 self)
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Intelligent agents often need to assess user utility functions in order to make decisions on their behalf, or predict their behavior. When uncertainty exists over the precise nature of this utility function, one can model this uncertainty using a distribution over utility functions. This view lies at the core of games with incomplete information and, more recently, several proposals for incremental preference elicitation. In such cases, decisions (or predicted behavior) are based on computing the expected expected utility (EEU) of decisions with respect to the distribution over utility functions. Unfortunately, decisions made under EEU are sensitive to the precise representation of the utility function. We examine the conditions under which EEU provides for sensible decisions by appeal to the foundational axioms of decision theory. We also discuss the impact these conditions have on the enterprise of preference elicitation more broadly.