Acting, Planning, and Learning

An overview of AI's next big challenge: integrating the essential cognitive functions needed by robots and other automated agents.

Malik Ghallab (Author), Dana Nau (Author), Paolo Traverso (Author), Michela Milano (Foreword by)

9781009579384, Cambridge University Press

Hardback, published 5 June 2025

632 pages, 100 b/w illus. 50 tables
26 x 18.5 x 3.9 cm, 1.38 kg

'After focusing on automated planning in their first book and its integration with acting in the second book, Malik Ghallab, Dana Nau, and Paolo Traverso close the loop by adding the last missing component, learning. With more than 600 pages of text and more than 1200 references, this massive effort is a must-read for anyone who builds autonomous agents that plan their actions and improve their behavior via learning. It is not only an excellent textbook, but it also serves as a reference book for researchers.' Roman Barták, Charles University, Czech Republic

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.

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.

Subject Areas: Artificial intelligence [UYQ]