Decomposition Strategies for Constructive Preference Elicitation

We tackle the problem of constructive preference elicitation, that is the problem of learning user preferences over very large decision problems, involving a combinatorial space of possible outcomes. In this setting, the suggested configuration is synthesized on-the-fly by solving a constrained optimization problem, while the preferences are learned itera tively by interacting with the user. Previous work has shown that Coactive Learning is a suitable method for learning user preferences in constructive scenarios. In Coactive Learning the user provides feedback to the algorithm in the form of an improvement to a suggested configuration. When the problem involves many decision variables and constraints, this type of interaction poses a significant cognitive burden on the user. We propose a decomposition technique for large preference-based decision problems relying exclusively on inference and feedback over partial configurations. This has the clear advantage of drastically reducing the user cognitive load. Additionally, part-wise inference can be (up to exponentially) less computationally demanding than inference over full configurations. We discuss the theoretical implications of working with parts and present promising empirical results on one synthetic and two realistic constructive problems.Comment: Accepted at the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18

Robust ordinal regression in preference learning and ranking

Multiple Criteria Decision Aiding (MCDA) offers a diversity of approaches designed for providing the decision maker (DM) with a recommendation concerning a set of alternatives (items, actions) evaluated from multiple points of view, called criteria. This paper aims at drawing attention of the Machine Learning (ML) community upon recent advances in a representative MCDA methodology, called Robust Ordinal Regression (ROR). ROR learns by examples in order to rank a set of alternatives, thus considering a similar problem as Preference Learning (ML-PL) does. However, ROR implements the interactive preference construction paradigm, which should be perceived as a mutual learning of the model and the DM. The paper clarifies the specific interpretation of the concept of preference learning adopted in ROR and MCDA, comparing it to the usual concept of preference learning considered within ML. This comparison concerns a structure of the considered problem, types of admitted preference information, a character of the employed preference models, ways of exploiting them, and techniques to arrive at a final ranking

Maximizing Expected Value of Information in Decision Problems by Querying on a Wish-to-Know Basis.

An agent acting under uncertainty regarding how it should complete the task assigned to it by its human user can learn more about how it should behave by posing queries to its human user. Asking too many queries, however, may risk requiring undue attentional demand of the user, and so the agent should prioritize asking the most valuable queries. For decision-making agents, Expected Value of Information (EVOI) measures the value of a query, and so given a set of queries the agent can ask, the agent should ask the query that is expected to maximally improve its performance by selecting the query with highest EVOI in its set. Unfortunately, to compute the EVOI of a query, the agent must consider how each possible response would influence its future behavior, which makes query selection particularly challenging in settings where planning the agent's behavior would be expensive even without the added complication of considering queries to ask, especially when there are many potential queries the agent should consider. The focus of this dissertation is on developing query selection algorithms that can be feasibly applied to such settings. The main novel approach studied, Wishful Query Projection (WQP), is based on the intuition that the agent should consider which query to ask on the basis of obtaining knowledge that would help it resolve a particular dilemma that it wishes could be resolved, as opposed to blindly searching its entire query set in hopes of finding one that would give it valuable knowledge. In implementing WQP, this dissertation contributes algorithms that are founded upon the following novel result: for myopic settings, when the agent can ask any query as long as the query has no more than some set number of possible responses, the best query takes the form of asking the user to choose from a specified subset of ways for the agent to behave. The work presented shows that WQP selects queries with near-optimal EVOI when the agent's query set is (1) balanced in the range of queries it contains; and (2) rich in terms of the highest contained query EVOI.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120772/1/rwcohn_1.pd

Coarse preferences: representation, elicitation, and decision making

In this thesis we present a theory for learning and inference of user preferences with a novel hierarchical representation that captures preferential indifference. Such models of ’Coarse Preferences’ represent the space of solutions with a uni-dimensional, discrete latent space of ’categories’. This results in a partitioning of the space of solutions into preferential equivalence classes. This hierarchical model significantly reduces the computational burden of learning and inference, with improvements both in computation time and convergence behaviour with respect to number of samples. We argue that this Coarse Preferences model facilitates the efficient solution of previously computationally prohibitive recommendation procedures. The new problem of ’coordination through set recommendation’ is one such procedure where we formulate an optimisation problem by leveraging the factored nature of our representation. Furthermore, we show how an on-line learning algorithm can be used for the efficient solution of this problem. Other benefits of our proposed model include increased quality of recommendations in Recommender Systems applications, in domains where users’ behaviour is consistent with such a hierarchical preference structure. We evaluate the usefulness of our proposed model and algorithms through experiments with two recommendation domains - a clothing retailer’s online interface, and a popular movie database. Our experimental results demonstrate computational gains over state of the art methods that use an additive decomposition of preferences in on-line active learning for recommendation

Designing a Wise Home: Leveraging Lightweight Dialogue, Proactive Coaching, Guided Experimentation and Mutual-learning to Support Mixed-initiative Homes --Comfort-aware Thermostats as a Case

Science fiction writers have been dreaming of homes that can understand our preferences, assist our daily chores and teach us to be healthier, more sustainable and more knowledgeable. While we are still far from achieving this dream, the recent development of mobile devices, wearable interfaces, smart home appliances, and machine learning offer unprecedented opportunities for homes to better understand our goals, preferences and contexts, as well as to facilitate our everyday tasks and decision making. These recent advancement open a new possibility to create homes beyond simple automation: they enable creation of homes to coach us to achieve our better selves. That is, homes that are not just smart, but wise as well. However, to develop a wise home, there are still two key questions: How can a wise home coach its occupants while considering their different goals and needs? How can a home integrates emerging sensors, devices and interfaces to better understand their goals, preferences and contexts in order to support coaching? To answer these two questions, in this dissertation I use residential heating and cooling control as a lens to advance the development of wiser homes. Based on the three studies conducted, this thesis provides three contributions. First, I show that we can integrate a diverse class of emerging devices, including mobile phones, smartwatches, in-home sensors and home appliances to capture important user contexts, such as individual preferences for thermal comfort. The integration of these emerging devices enables a home to better coach its occupants and potentially better support automation. Secondly, I show that mixed-initiative interaction is an effective approach in the design of a wise home, and further propose four design strategies to support mixed-initiative homes, namely, lightweight dialogue, proactive coaching, guided experimentation and mutual-learning. Finally, I demonstrate a novel system that integrates the above-mentioned strategies to support the development of wise homes, facilitating home occupants to identify actions to achieve a better balance between their comfort and savings goals.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153457/1/chuanche_1.pd

A Study in Preference Elicitation under Uncertainty

In many areas of Artificial Intelligence (AI), we are interested in helping people make better decisions. This help can result in two advantages. First, computers can process large amounts of data and perform quick calculations, leading to better decisions. Second, if a user does not have to think about some decisions, they have more time to focus on other things they find important. Since users' preferences are private, in order to make intelligent decisions, we need to elicit an accurate model of the users' preferences for different outcomes. We are specifically interested in outcomes involving a degree of risk or uncertainty. A common goal in AI preference elicitation is minimizing regret, or loss of utility. We are often interested in minimax regret, or minimizing the worst-case regret. This thesis examines three important aspects of preference elicitation and minimax regret. First, the standard elicitation process in AI assumes users' preferences follow the axioms of Expected Utility Theory (EUT). However, there is strong evidence from psychology that people may systematically deviate from EUT. Cumulative prospect theory (CPT) is an alternative model to expected utility theory which has been shown empirically to better explain humans' decision-making in risky settings. We show that the standard elicitation process can be incompatible with CPT. We develop a new elicitation process that is compatible with both CPT and minimax regret. Second, since minimax regret focuses on the worst-case regret, minimax regret is often an overly cautious estimate of the actual regret. As a result, using minimax regret can often create an unnecessarily long elicitation process. We create a new measure of regret that can be a more accurate estimate of the actual regret. Our measurement of regret is especially well suited for eliciting preferences from multiple users. Finally, we examine issues of multiattribute preferences. Multiattribute preferences provide a natural way for people to reason about preferences. Unfortunately, in the worst-case, the complexity of a user's preferences grows exponentially with respect to the number of attributes. Several models have been proposed to help create compact representations of multiattribute preferences. We compare both the worst-case and average-case relative compactness