Brahim Chaib-draa – författare

This book discusses the Partially Observable Markov Decision Process (POMDP) framework applied in dialogue systems. It presents POMDP as a formal framework to represent uncertainty explicitly while supporting automated policy solving. The authors propose and implement an end-to-end learning approach for dialogue POMDP model components. Starting from scratch, they present the state, the transition model, the observation model and then finally the reward model from unannotated and noisy dialogues. These altogether form a significant set of contributions that can potentially inspire substantial further work. This concise manuscript is written in a simple language, full of illustrative examples, figures, and tables.

The area intersecting supply chains management with multiagent systems has attracted in the last decade books, workshops, conferences, etc. Recently however, multiagent systems have became more formal borrowing from operations research, game theory and other facets from distributed decision making. The authors takes a close look at what has been done recently in the field of supply chain management using agent technology and more specifically multiagent systems. The book contains sixteen chapters organized in four main parts; Introductory Papers; Multiagent Based Supply Chain Modeling, Collaboration and Coordination Between Agents in a Supply Chain and Multiagent Based Supply Chain Management: Applications. This book is intended as reference material for researchers, graduate students, and practitioners alike who are interested in pursuing research and development in this area, and who need for that, a comprehensive view of the existing literature, and some ideas for future directions.