A Concise Introduction To Decentralized Pomdps
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A Concise Introduction to Decentralized POMDPs
This book introduces multiagent planning under uncertainty as formalized by decentralized partially observable Markov decision processes (Dec-POMDPs). The intended audience is researchers and graduate students working in the fields of artificial intelligence related to sequential decision making: reinforcement learning, decision-theoretic planning for single agents, classical multiagent planning, decentralized control, and operations research.
Artificial Intelligence and Machine Learning
This book constitutes the refereed proceedings of the 35th Benelux Conference on Artificial Intelligence and Machine Learning, BNAIC/Benelearn 2023, held in Delft, The Netherlands, during November 8–10, 2023. The 14 papers included in these proceedings were carefully reviewed and selected from 47 submissions. These papers focus on various aspects of Artificial Intelligence and Machine learning, including Natural Language Processing and Reinforcement Learning, and their applications.
A Concise Introduction to Models and Methods for Automated Planning
Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective. Table of Contents: Preface / Planning and Autonomous Behavior / Classical Planning: Full Information and Deterministic Actions / Classical Planning: Variations and Extensions / Beyond Classical Planning: Transformations / Planning with Sensing: Logical Models / MDP Planning: Stochastic Actions and Full Feedback / POMDP Planning: Stochastic Actions and Partial Feedback / Discussion / Bibliography / Author's Biography