Efficient exact computation of setwise minimax regret for interactive preference elicitation

Toffano, Federico; Viappiani, Paolo; Wilson, Nic

Efficient exact computation of setwise minimax regret for interactive preference elicitation

Files

3463952.3464105.pdf(1.18 MB)

Published version

Date

2021-05-03

Authors

Toffano, Federico

Viappiani, Paolo

Wilson, Nic

Publisher

ACM; International Foundation for Autonomous Agents and Multiagent Systems

Abstract

A key issue in artificial intelligence methods for interactive preference elicitation is choosing at each stage an appropriate query to the user, in order to find a near-optimal solution as quickly as possible. A theoretically attractive method is to choose a query that minimises max setwise regret (which corresponds to the worst case loss response in terms of value of information). We focus here on the situation in which the choices are represented explicitly in a database, and with a model of user utility as a weighted sum of the criteria; in this case when the user makes a choice, an agent learns a linear constraint on the unknown vector of weights. We develop an algorithmic method for computing minimax setwise regret for this form of preference model, by making use of a SAT solver with cardinality constraints to prune the search space, and computing max setwise regret using an extreme points method. Our experimental results demonstrate the feasibility of the approach and the very substantial speed up over the state of the art.

Keywords

Setwise minimax regret , Human-agent interaction , Interactive preference elicitation

Citation

Toffano, F., Viappiani, P. and Wilson, N. (2021) 'Efficient Exact Computation of Setwise Minimax Regret for Interactive Preference Elicitation', Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems, Virtual Event, United Kingdom: International Foundation for Autonomous Agents and Multiagent Systems, pp. 1326–1334.