Deakin University
dazeley-surveyofmulti-2013.pdf (574.56 kB)
Download file

A survey of multi-objective sequential decision-making

Download (574.56 kB)
journal contribution
posted on 2013-01-01, 00:00 authored by Diederik Marijn Roijers, Peter Vamplew, Shimon Whiteson, Richard DazeleyRichard Dazeley
Sequential decision-making problems with multiple objectives arise naturally in practice and pose unique challenges for research in decision-theoretic planning and learning, which has largely focused on single-objective settings. This article surveys algorithms designed for sequential decision-making problems with multiple objectives. Though there is a growing body of literature on this subject, little of it makes explicit under what circumstances special methods are needed to solve multi-objective problems. Therefore, we identify three distinct scenarios in which converting such a problem to a single-objective one is impossible, infeasible, or undesirable. Furthermore, we propose a taxonomy that classifies multi-objective methods according to the applicable scenario, the nature of the scalarization function (which projects multi-objective values to scalar ones), and the type of policies considered. We show how these factors determine the nature of an optimal solution, which can be a single policy, a convex hull, or a Pareto front. Using this taxonomy, we survey the literature on multi-objective methods for planning and learning. Finally, we discuss key applications of such methods and outline opportunities for future work.



Journal of artificial intelligence research




AI Access Foundation





Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2013, AI Access Foundation

Usage metrics