Preference elicitation and generalized additive utility

AAAI Spring Symposium on …, 1997

We investigate the solution of constraint-based configuration problems in which the preference function over outcomes is unknown or incompletely specified. The aim is to configure a system, such as a personal computer, so that it will be optimal for a given user. The goal of this project is to develop algorithms that generate the most preferred feasible configuration by posing preference queries to the user. In order to minimize the number and the complexity of preference queries posed to the user, the algorithm reasons about the user's preferences while taking into account constraints over the set of feasible configurations. We assume that the user can structure their preferences in a particular way that, while natural in many settings, can be exploited during the optimization process. We also address in a preliminary fashion the trade-offs between computational effort in the solution of a problem and the degree of interaction with the user.

The basic problem discussed in the article is a question how to model a decision-maker's preferences concerning the investment location or the choice of a given product or service. To make such a decision they have to consider plenty of factors. Their large number makes the optimal choice a complex issue. There are, however, certain IT systems equipped with algorithms based on multidimensional comparative analysis methods proposed by K. Nermend. The multidimensional vector of users' preferences can be a starting point for choosing an optimum solution. of the EU. INTRODUCTION We face a problem of making a right decision with every step we take. Whether we are buying a house, a car or a computer, we have to consider several options to eventually choose the one which in our view is the best. Yet, defining the best option is often a perplexed task. The multitude of data make the right assessment difficult. Mounting problems with making rational decisions have become a subject of...

2004

We present an approach to elicitation of user preference models in which assumptions can be used to guide but not constrain the elicitation process. We demonstrate that when domain knowledge is available, even in the form of weak and somewhat inaccurate assumptions, significantly less data is required to build an accurate model of user preferences than when no domain knowledge is provided. This approach is based on the KBANN (Knowledge-Based Artificial Neural Network) algorithm pioneered by . We demonstrate this approach through two examples, one involves preferences under certainty, and the other involves preferences under uncertainty. In the case of certainty, we show how to encode assumptions concerning preferential independence and monotonicity in a KBANN network, which can be trained using a variety of preferential information including simple binary classification. In the case of uncertainty, we show how to construct a KBANN network that encodes certain types of dominance relations and attitude toward risk. The resulting network can be trained using answers to standard gamble questions and can be used as an approximate representation of a person's preferences. We empirically evaluate our claims by comparing the KBANN networks with simple backpropagation artificial neural networks in terms of learning rate and accuracy. For the case of uncertainty, the answers to standard gamble questions used in the experiment are taken from an actual medical data set first used by . In the case of certainty, we define a measure to which a set of prefer-ences violate a domain theory, and examine the robustness of the KBANN network as this measure of domain theory violation varies.

1998

Abstract We investigate the application of classification techniques to utility elicitation. In a decision problem, two sets of parameters must generally be elicited: the probabilities and the utilities. While the prior and conditional probabilities in the model do not change from user to user, the utility models do. Thus it is necessary to elicit a utility model separately for each new user. Elicitation is long and tedious, particularly if the outcome space is large and not decomposable. There are two common approaches to utility function elicitation.

European Journal of Operational …, 2010

In multiple criteria decision aiding, it is common to use methods that are capable of automatically extracting a decision or evaluation model from partial information provided by the decision maker about a preference structure. In general, there is more than one possible model, leading to an indetermination which is dealt with sometimes arbitrarily in existing methods. This paper aims at filling this theoretical gap: we present a novel method, based on the computation of the analytic center of a polyhedron, for the selection of additive value functions that are compatible with holistic assessments of preferences. We demonstrate the most important characteristics of this technique with an experimental and comparative study of several existing methods belonging to the UTA family.

AI Magazine, 2009

As automated decision support becomes increasingly accessible in a wide variety of AI applications, addressing the preference bottleneck is vital. Specifically, since the ability to make reasonable decisions on behalf of a user depends on that user's preferences over outcomes in the domain in question, AI systems must assess or estimate these preferences before making decisions. Designing effective preference assessment techniques to incorporate such user-specific considerations (that is, breaking the preference bottleneck) is one of the most important problems facing AI.

Behavior Research Methods, Instruments, & Computers, 1989

Alternative actions or objects can be characterized by differences among defining attributes or dimensions. Making choices among these alternatives is often a complex and difficult task, particularly when the number of alternatives or defining attributes is large or the time to decide is limited. This paper describes two programs that allow users to define a multidimensional problem domain and derive a multiplicative utility function that quantitatively describes their judgment policy within that domain. It also discusses potential applications for the derived preference models.

2009

. The numerous,reports on preference reversals in preference elicitations pose a great challenge to mainstream economics. Taking the preference reversals as a

Santa Fe Institute, 2004

Modern economic theory developed under the assumption that only two basic facts are needed concerning human preferences. The first is that agents have consistent preferences that permit modeling choice as the result of the maximization of a preference function subject to appropriate material and informational constraints. The second is that economic agents are self-interested in the sense that the sole criteria relevant to an agent in evaluating an economic transaction is the effect of the transaction on his personal holdings. The core of ...

2009

Abstract Utility or preference elicitation is a critical component in many recommender and decision support systems. However, most frameworks for elicitation assume a predefined set of features (eg, as derived from catalog descriptions) over which user preferences are expressed. Just as user preferences vary considerably, so too can the features over which they are most comfortable expressing these preferences. In this work, we consider preference elicitation in the presence of subjective or user-defined features.