- Title
- A survey of multi-objective sequential decision-making
- Creator
- Roijers, Diederik; Vamplew, Peter; Whiteson, Shimon; Dazeley, Richard
- Date
- 2013
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/43586
- Identifier
- vital:5319
- Identifier
-
https://doi.org/10.1613/jair.3987
- Identifier
- ISSN:1076-9757
- Abstract
- 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. © 2013 AI Access Foundation.; C1
- Relation
- Journal of Artificial Intelligence Research Vol. 48, no. (2013), p. 67-113
- Rights
- © 2013 AI Access Foundation
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Pareto front
- Full Text
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