Acting, Planning, and Learning

Erscheinungsjahr 2025
ISBN: 978-1-009-57938-4
Verlag: Cambridge University Press

Buch, Englisch, 632 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 1369 g

AI's next big challenge is to master the cognitive abilities needed by intelligent agents that perform actions. Such agents may be physical devices such as robots, or they may act in simulated or virtual environments through graphic animation or electronic web transactions. This book is about integrating and automating these essential cognitive abilities: planning what actions to undertake and under what conditions, acting (choosing what steps to execute, deciding how and when to execute them, monitoring their execution, and reacting to events), and learning about ways to act and plan. This comprehensive, coherent synthesis covers a range of state-of-the-art approaches and models –deterministic, probabilistic (including MDP and reinforcement learning), hierarchical, nondeterministic, temporal, spatial, and LLMs –and applications in robotics. The insights it provides into important techniques and research challenges will make it invaluable to researchers and practitioners in AI, robotics, cognitive science, and autonomous and interactive systems.

Autoren/Hrsg.

Ghallab, Malik

Nau, Dana

Traverso, Paolo

Weitere Mitwirkende

Milano, Michela

Inhaltsverzeichnis

About the authors; Foreword; Preface; Acknowledgements; 1. Introduction; Part I. Deterministic State-Transition Systems: 2. Deterministic representation and acting; 3. Planning with deterministic models; 4. Learning deterministic models; Part II. Hierarchical Task Networks: 5. HTN representation and planning; 6. Acting with HTNs; 7. Learning HTN methods; Part III. Probabilistic Models: 8. Probabilistic representation and acting; 9. Planning with probabilistic models; 10. Reinforcement learning; Part IV. Nondeterministic Models: 11. Acting with nondeterministic models; 12. Planning with nondeterministic models; 13. Learning nondeterministic models; Part V. Hierarchical Refinement Models: 14. Acting with hierarchical refinement; 15. Hierarchical refinement planning; 16. Learning hierarchical refinement models; Part VI. Temporal Models: 17. Temporal representation and planning; 18. Acting with temporal controllability; 19. Learning for temporal acting and planning; Part VII. Motion and Manipulation Models in Robotics: 20. Motion and manipulation actions; 21. Task and motion planning; 22. Learning for movement actions; Part VIII. Other Topics and Perspectives: 23. Large language models for acting and planning; 24. Perceiving, monitoring and goal reasoning; A. Graphs and search; B. Other mathematical background; List of algorithms; Bibliographic abbreviations; References; Index.

Über Autor(innen)

Traverso, Paolo
Paolo Traverso is Director of Strategic Planning at Fondazione Bruno Kessler (FBK), Trento, Italy. His main research interests are in automated planning and learning under uncertainty. He is the author and co-author of more than 100 scientific articles. He is a EurAI Fellow, AAIA Fellow, and AIIA Fellow.

Ghallab, Malik
Malik Ghallab is Directeur de Recherche Emeritus at CNRS and the University of Toulouse. He has (co-)authored more than 200 scientific publications and books on AI and robotics, especially on acting, planning, and learning. He is a EurAI Fellow, and Docteur Honoris Causa of Linköping University, Sweden.

Nau, Dana
Dana Nau is Professor Emeritus at the University of Maryland in the Computer Science Department and the Institute for Systems Research. He has more than 400 refereed scientific publications, primarily on AI, game theory, and several interdisciplinary topics. He is an AAAI Fellow, ACM Fellow, and AAAS Fellow.