A survey of multi-objective sequential decision-making
TLDR
This article surveys algorithms designed for sequential decision-making problems with multiple objectives and proposes a taxonomy that classifies multi-objective methods according to the applicable scenario, the nature of the scalarization function, and the type of policies considered.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.read more
Citations
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Monte Carlo tree search algorithms for risk-aware and multi-objective reinforcement learning
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Visualizing Clinical Significance with Prediction and Tolerance Regions
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TL;DR: A framework for computing and presenting prediction regions and tolerance regions for the outcomes of a treatment policy operating within a multi-objective Markov decision process (MOMDP).
Proceedings ArticleDOI
Linguistic Style Accommodation in Disagreements
TL;DR: Using a new model for measuring style accommodation, it is found that speakers coordinate on style more noticeably if they disagree than if they agree, especially if they want to establish rapport and possibly persuade their interlocutors.
Proceedings Article
A Utility-Based Perspective on Multi-Objective Multi-Agent Decision Making
TL;DR: A new taxonomy on the basis of the reward structures and utility functions is developed, to offer a more structured view of the field and analyse the effect of non-linear utility functions on the set of equilibria in general multi-objective normal form games.
Dissertation
Bayesian Optimisation for Planning And Reinforcement Learning
TL;DR: This thesis addresses the problem of achieving efficient non-myopic decision making in continuous spaces by explicitly balancing exploration and exploitation by proposing a reward function based on Bayesian optimisation and is combined to a non- myopic planner to achieve efficient spatial monitoring.
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