Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. To illustrate this, we employ the popular problem classes of multi-objective Markov decision processes (MOMDPs) and multi-objective coordination graphs (MO-CoGs).

First, we discuss different use cases for multi-objective decision making, and why they often necessitate explicitly multi-objective algorithms. We advocate a utility-based approach to multi-objective decision making, i.e., that what constitutes an optimal solution to a multi-objective decision problem should be derived from the available information about user utility. We show how different assumptions about user utility and what types of policies are allowed lead to different solution concepts, which we outline in a taxonomy of multi-objective decision problems.

Second, we show how to create new methods for multi-objective decision making using existing single-objective methods as a basis. Focusing on planning, we describe two ways to creating multi-objective algorithms: in the inner loop approach, the inner workings of a single-objective method are adapted to work with multi-objective solution concepts; in the outer loop approach, a wrapper is created around a single-objective method that solves the multi-objective problem as a series of single-objective problems. After discussing the creation of such methods for the planning setting, we discuss how these approaches apply to the learning setting.

Next, we discuss three promising application domains for multi-objective decision making algorithms: energy, health, and infrastructure and transportation. Finally, we conclude by outlining important open problems and promising future directions.

Diederik Roijers, University of Oxford
Diederik M. Roijers completed his master's in Computing Science at Utrecht University before obtaining his Ph.D. in Artificial Intelligence under the supervision of Shimon Whiteson and Frans A. Oliehoek at the University of Amsterdam in 2016. He then joined the University of Oxford as a postdoctoral research assistant. He was awarded a Postdoctoral Fellowship Grant from the FWO (Research Foundation - Flanders) and started as an FWO Postdoctoral Fellow at the Vrije Universiteit Brussel in October 2016. His research focuses on creating intelligent autonomous systems that assist humans in solving complex problems, especially those with multiple objectives. To this end, he focuses ondecision-theoretic planning and learning, which enable agents to use mathematical models to reason about the environments in which they operate. In the multi-objective problems he has been studying, the agents produce a set of possibly optimal policies that offer different trade-offs with respect to the objectives, to help users make an informed decision.

Table of Contents
Preface
Acknowledgments
Table of Abbreviations
Introduction
Multi-Objective Decision Problems
Taxonomy
Inner Loop Planning
Outer Loop Planning
Learning
Applications
Conclusions and Future Work
Bibliography
Authors' Biographies