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.

This book takes a close look at recent progress in the field of supply chain management using agent technology and more specifically multiagent systems. Sixteen chapters are 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. The result is a comprehensive review of existing literature, and ideas for future research.

This book takes a close look at recent progress in the field of supply chain management using agent technology and more specifically multiagent systems. Sixteen chapters are 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. The result is a comprehensive review of existing literature, and ideas for future research.