The Belief Algorithm
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Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Author: Claudio Sossai
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-06-19
These are the proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2009, held in Verona (Italy), July 1–3, 2009. The biennial ECSQARU conferences are a major forum for advances in the theory and practice of reasoning under uncertainty. The ?rst ECSQARU conf- ence was held in Marseille (1991), and since then it has been held in Granada (1993), Fribourg (1995), Bonn (1997), London (1999), Toulouse (2001), Aalborg (2003), Barcelona (2005) and Hammamet (2007). The 76 papers gathered in this volume were selected out of 118 submissions from 34 countries, after a rigorous review process. In addition, the conference included invited lectures by three outstanding researchers in the area: Isabelle Bloch (“Fuzzy and bipolar mathematical morphology, applications in spatial reasoning”), Petr Cintula (“From (deductive) fuzzy logic to (logic-based) fuzzy mathematics”),andDaniele Mundici(“Conditionalsandindependence inma- valued logics”). Twospecialsessionswerepresentedduringtheconference:“Conditioning,- dependence, inference” (organizedby Giulianella Coletti and BarbaraVantaggi) and “Mathematicalfuzzy logic” (organizedby Stefano Aguzzoli,Brunella Gerla, Llu´ ?s Godo, Vincenzo Marra, Franco Montagna) On the whole, the program of the conference provided a broad, rich and up-to-date perspective of the current high-level research in the area which is re?ected in the contents of this volume.
Algorithms for Decision Making
A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.
Mobile Wireless Middleware, Operating Systems and Applications
This book constitutes the refereed conference proceedings of the 11th International Conference on Mobile Wireless Middleware, Operating Systems and Applications, MOBILWARE 2022, via Virtual Event on 28-29, 2022 Due to COVID-19 pandemic the conference was held virtually. The 23 revised full papers were reviewed and selected from 59 submissions and are organized in tracks on Middleware, Wireless, and Future Networks; Integrated Satellite-Terrestrial Information Network; and Integrated Satellite-Terrestrial Intelligent Information Processing, Decision and Planning.