5.7 Comparing an Inner and Outer Loop Method
5.7.1 Theoretical Comparison
5.7.2 Empirical Comparison
5.8 Outer Loop Methods for PCS Planning
6.1 Offline MORL
6.2 Online MORL
7.1 Energy
7.2 Health
7.3 Infrastructure and Transportation
8.1 Conclusions
8.2 Future Work
8.2.1 Scalarization of Expectation vs. Expectation of Scalarization
8.2.2 Other Decision Problems
8.2.3 Users in the Loop
Preface
Many real-world decision problems have multiple, possibly conflicting, objectives. For example, an autonomous vehicle typically wants to minimize both travel time and fuel costs, while maximizing safety; when seeking medical treatment, we want to maximize the probability of being cured, but minimize the severity of the side-effects, etcetera.
Although interest in multi-objective decision making has grown in recent years, the majority of decision-theoretic research still assumes only a single objective. In this book, we argue that multi-objective methods are underrepresented and present three scenarios to justify the need for explicitly multi-objective approaches. Key to these scenarios is that, although the utility the user derives from a policy—which is what we ultimately aim to optimize—is scalar, it is sometimes impossible, undesirable, or infeasible to formulate the problem as single-objective at the moment when the policies need to be planned or learned. We also present the case for a utility-based view of multi-objective decision making, i.e., that the appropriate multi-objective solution concept should be derived from what we know about the user’s utility function.
This book is based on our research activities over the years. In particular, the survey we wrote together with Peter Vamplew and Richard Dazeley [Roijers et al., 2013a] forms the basis of how we organize concepts in multi-objective decision making. Furthermore, we use insights from our work on multi-objective planning over the years, particularly in the context of the PhD research of the first author [Roijers, 2016]. Another important source for writing this book were the lectures we gave on the topic at the University of Amsterdam, and the tutorials we did at the IJCAI-2015 and ICAPS-2016 conferences, as well as the EASSS-2016 summer school.
Aim and Readership This book aims to provide a structured introduction to the field of multi-objective decision making, and to make the differences with single-objective decision theory clear. We hope that, after reading this book, the reader will be equipped to conduct research in multi-objective decision-theory or apply multi-objective methods in practice.
We expect our readers to have a basic understanding of decision theory, at a graduate or undergraduate level. In order to remain accessible to a wide range of readers, we provide intuitive explanations and examples of key concepts before formalizing them. In some cases, we omit detailed proofs of theorems in order to better focus on the intuition behind and implications of these theorems. In such cases, we provide references to the detailed proofs.
Outline This book is structured as follows. In Chapter 1, we motivate multi-objective decision making by providing examples of multi-objective decision problems and scenarios that require explicitly multi-objective solution methods. In Chapter 2, we introduce two popular classes of decision problems that we use throughout the book to illustrate specific algorithms and general theoretical results. In Chapter 3, we present a taxonomy of solution concepts for multi-objective decision problems. Using this taxonomy, we discuss different solution methods. First, we assume that the model of the environment is known to the agents, leading to a planning setting. In Chapters 4 and 5, we discuss two different approaches for finding a coverage set using planning algorithms. In Chapter 6, we remove the assumption that the agents are given a model of the environment, and consider cases where they must learn about the environment through interaction. Finally, we discuss several illustrating applications in Chapter 6, followed by conclusions and future work in Chapter 8.
Diederik M. Roijers and Shimon Whiteson
April 2017
Acknowledgments
This book is based on our research on multi-objective decision making over the years. During this research, we collaborated with people whose input has been essential to our understanding of the field. We would like to thank several of them explicitly.
Together with Peter Vamplew and Richard Dazeley we wrote our 2013 survey article on multi-objective sequential decision making. The discussions we had about the nature of multi-objective decision problems were vital in shaping our ideas about this field, and lay the foundation for how we view multi-objective decision problems.
In the past few years, one of our main collaborators (and Diederik’s other PhD supervisor), has been Frans A. Oliehoek. Together, we developed many algorithms for multi-objective decision making, including the CMOVE and OLS algorithms that we discuss in Chapters 4 and 5. Frans’s vast expertise on partially observable decision problems and limitless capacity for generating new ideas have been invaluable to our work in the field of multi-objective decision making.
Together with Joris Scharpff, Matthijs Spaan, and Mathijs de Weerdt, we worked on the traffic network maintenance planning problem (which we discuss in Section 7.3), and in this context improved upon the original OLS algorithm (Chapter 5). We enjoyed this productive collaboration.
We would also like to thank our other past and present co-authors and collaborators who we have worked with on multi-objective decision making problems: Alexander Ihler, João Messias, Maarten van Someren, Chiel Kooijman, Maarten Inja, Maarten de Waard, Luisa Zintgraf, Timon Kanters, Philipp Beau, Richard Pronk, Carla Groenland, Elise van der Pol, Joost van Doorn, Daan Odijk, Maarten de Rijke, Ayumi Igarashi, Hossam Mossalam, and Yannis Assael.
Finally, we would like to thank several people with whom we had interesting discussions about multi-objective decision making over the years: Ann Nowé, Kristof van Moffaert, Tim Brys, Abdel-Illah Mouaddib, Paul Weng, Grégory Bonnet, Rina Dechter, Radu Marinescu, Shlomo Zilberstein, Kyle Wray, Patrice Perny, Paolo Viappiani, Pascal Poupart,