Preference elicitation via theory refinement

2021

In this paper we propose efficient methods for elicitation of complexly structured preferences and utilize these in problems of decision making under (severe) uncertainty. Based on the general framework introduced in Jansen, Schollmeyer & Augustin (2018, Int. J. Approx. Reason), we now design elicitation procedures and algorithms that enable decision makers to reveal their underlying preference system (i.e. two relations, one encoding the ordinal, the other the cardinal part of the preferences) while having to answer as few as possible simple ranking questions. Here, two different approaches are followed. The first approach directly utilizes the collected ranking data for obtaining the ordinal part of the preferences, while their cardinal part is constructed implicitly by measuring meta data on the decision maker’s consideration times. In contrast, the second approach explicitly elicits also the cardinal part of the decision maker’s preference system, however, only an approximate ve...

2017

It is a truth universally acknowledged that e-commerce platform users in search of an item that best suits their preferences may be o‚ered a lot of choices. An item may be characterised by many aŠributes, which can complicate the process. Here the classic approach in decision support systems to put weights on the importance of each aŠribute is not always helpful as users may €nd it hard to formulate their priorities explicitly. Pairwise comparisons provide an easy way to elicit the user’s preferences in the form of the simplest possible qualitative preferences, which can then be combined to rank the available alternatives. We focus on this type of preference elicitation and learn the individual preference by applying one statistical approach based on Support Vector Machines (SVM), and two logic-based approaches: Inductive Logic Programming (ILP) and Decision Trees. All approaches are compared on two datasets of car preferences and sushi preferences collected from human participants....

2010

Abstract Most frameworks for utility elicitation assume a predefined set of features over which user preferences are expressed. We consider utility elicitation in the presence of subjective or user-defined features, whose definitions are not known in advance. We treat the problem of learning a user's feature definition as one of concept learning, but whose goal is to learn only enough about the concept definition to enable a good decision to be made. This is complicated by the fact that user utility is unknown.

Computers & Operations Research, 1996

In real-life decision problems, the preference statements elicited from a decision maker are bound to be somewhat imprecise. Most traditional decision analytic tools do not take this impreciseness into account explicitly. It is well-known that neural networks have been used successfully for solving pattern recognition problems in the presence of incomplete, fuzzy or imprecise information.

Lecture Notes in Computer Science, 2015

Many mathematical frameworks aim at modeling human preferences, employing a number of methods including utility functions, qualitative preference statements, constraint optimization, and logic formalisms. The choice of one model over another is usually based on the assumption that it can accurately describe the preferences of humans or other subjects/processes in the considered setting and is computationally tractable. Verification of these preference models often leverages some form of real life or domain specific data; demonstrating the models can predict the series of choices observed in the past. We argue that this is not enough: to evaluate a preference model, humans must be brought into the loop. Human experiments in controlled environments are needed to avoid common pitfalls associated with exclusively using prior data including introducing bias in the attempt to clean the data, mistaking correlation for causality, or testing data in a context that is different from the one where the data were produced. Human experiments need to be done carefully and we advocate a multidisciplinary research environment that includes experimental psychologists and AI researchers. We argue that experiments should be used to validate models. We detail the design of an experiment in order to highlight some of the significant computational, conceptual, ethical, mathematical, psychological, and statistical hurdles to testing whether decision makers' preferences are consistent with a particular mathematical model of preferences.

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.

American Journal of Bioethics, 2024

When making substituted judgments for incapacitated patients, surrogates may often struggle to guess what the patient would want if they had capacity. Surrogates may also agonise over having the (sole) responsibility of making such a determination. To address such concerns, a Patient Preference Predictor (PPP) has been proposed that would use an algorithm to infer the treatment preferences of individual patients from population-level data about the known preferences of people with similar demographic characteristics. However, critics have suggested that even if such a PPP were more accurate, on average, than human surrogates in accurately identifying patient preferences, the proposed algorithm would nevertheless fail to respect the patient’s (former) autonomy since it draws on the ‘wrong’ kind of data: namely, data that are not specific to the individual patient and which therefore may not reflect their actual values, or their reasons for having the preferences they do. Taking such criticisms on board, we here propose a new approach: the Personalized Patient Preference Predictor (P4). The P4 is based on recent advances in machine learning, which allow technologies including large language models to be more cheaply and efficiently ‘fine-tuned’ on person-specific data. The P4, unlike the PPP, would be able to infer an individual patient’s preferences from material (e.g., prior treatment decisions) that is in fact specific to them. Thus, we argue, in addition to being potentially more accurate at the individual level than the previously proposed PPP, the predictions of a P4 would also more directly reflect each patient’s own reasons and values. In this article, we review recent discoveries in artificial intelligence research that suggest a P4 is technically feasible, and argue that, if it is developed and appropriately deployed, it should assuage some of the main autonomy-based concerns of critics of the original PPP. We then consider various objections to our proposal and offer some tentative replies.

2021

Identifying the preferences of a given user through elicitation is a central part of multi-criteria decision aid (MCDA) or preference learning tasks. Two classical ways to perform this elicitation is to use either a robust or a Bayesian approach. However, both have their shortcoming: the robust approach has strong guarantees through very strong hypotheses, but cannot integrate uncertain information. While the Bayesian approach can integrate uncertainties, but sacrifices the previous guarantees and asks for stronger model assumptions. In this paper, we propose and test a method based on possibility theory, which keeps the guarantees of the robust approach without needing its strong hypotheses. Among other things, we show that it can detect user errors as well as model misspecification.

Knowledge-Based Systems, 2013

The problem of learning Conditional Preference Networks (CP-nets) from a set of pairwise comparisons between outcomes has received great attention recently. However, because of the randomicity of the users' behaviors or the observation errors, there exists some noise (errors) in the training samples. Most existing methods neglect to handle the case with noisy samples. In this work, we introduce a new model of learning CP-nets from noisy samples. Based on chi-squared testing, we propose an algorithm to solve this problem in polynomial time. We prove that the obtained CP-net converges in mean to initial CP-net as sample size increases. The proposed method is verified on both simulated data and real data. Compared with the previous methods, our method achieves more accurate results on noisy sample sets.

Preference Learning, 2010

A recurrent issue in automated decision making is to extract a preference structure from a set of examples. In this paper, we investigate the problem of learning ordinal preference orderings over discrete multi-attribute, or combinatorial, domains. Specifically, we concentrate on the learnability issue of conditional preference networks, or CP-nets, that have recently emerged as a popular graphical language for representing ordinal preferences in a concise and intuitive manner. This paper provides results in both passive and active learning. In passive learning, the learner aims at finding a CP-net compatible with a given set of examples, while in active learning the learner searches for the cheapest interaction policy with the user for acquiring the target CP-net. * Partially supported by the ANR projects CANAR (ANR-06-BLAN-0383-02) and PHAC (ANR-05-BLAN-0384-01