| 書目名稱 | Multi-Objective Decision Making | | 編輯 | Diederik M. Roijers,Shimon Whiteson | | 視頻video | http://file.papertrans.cn/640/639984/639984.mp4 | | 叢書名稱 | Synthesis Lectures on Artificial Intelligence and Machine Learning | | 圖書封面 |  | | 描述 | .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 availableinformation 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 decisio | | 出版日期 | Book 2017 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01576-2 | | isbn_softcover | 978-3-031-00448-3 | | isbn_ebook | 978-3-031-01576-2Series ISSN 1939-4608 Series E-ISSN 1939-4616 | | issn_series | 1939-4608 | | copyright | Springer Nature Switzerland AG 2017 |
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