EAN: 9783031004483

Bilder-Quelle: discount24.de - Sport-Freizeit

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.

Produktinformationen zuletzt aktualisiert am
19.03.2025 um 09:31 Uhr

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