Preference Elicitation and Query Learning
نویسندگان
چکیده
In this paper we explore the relationship between “preference elicitation”, a learning-style problem that arises in combinatorial auctions, and the problem of learning via queries studied in computational learning theory. Preference elicitation is the process of asking questions about the preferences of bidders so as to best divide some set of goods. As a learning problem, it can be thought of as a setting in which there are multiple target concepts that can each be queried separately, but where the goal is not so much to learn each concept as it is to produce an “optimal example”. In this work, we prove a number of similarities and differences between two-bidder preference elicitation and query learning, giving both separation results and proving some connections between these problems.
منابع مشابه
A Design Preference Elicitation Query as an Optimization Process
We seek to elicit individual design preferences through human-computer interaction. During an iteration of the interactive session, the computer queries the subject by presenting a set of designs from which the subject must make a choice. The computer uses this choice feedback and creates the next set of designs using knowledge accumulated from previous choices. Under the hypothesis that human ...
متن کاملDesign Preference Elicitation : Exploration and Learning
We study design preference elicitation, namely discovery of an individual " s design preferences, through human-computer interactions. In each interaction, the computer presents a set of designs to the human subject who is then asked to pick preferred designs from the set. The computer learns from this feedback in a cumulative fashion and creates new sets of designs to query the subject. Under ...
متن کاملCoactive Critiquing: Elicitation of Preferences and Features
When faced with complex choices, users refine their own preference criteria as they explore the catalogue of options. In this paper we propose an approach to preference elicitation suited for this scenario. We extend Coactive Learning, which iteratively collects manipulative feedback, to optionally query example critiques. User critiques are integrated into the learning model by dynamically ext...
متن کاملA Modular Framework for Multi-Agent Preference Elicitation
I present a framework for multi-agent preference elicitation in the context of a discrete resource-allocation problem, known as the combinatorial allocation problem (CAP). There are several distinct, indivisible items, which must be allocated among a set of agents. The agents value bundles rather than just individual items. Because the number of bundles can be very large, agent preferences cann...
متن کاملElicitation for Preferences Single Peaked on Trees
In multiagent systems, we often have a set of agents each of which have a preference ordering over a set of items and one would like to know these preference orderings for various tasks, for example, data analysis, preference aggregation, voting etc. However, we often have a large number of items which makes it impractical to ask the agents for their complete preference ordering. In such scenar...
متن کامل