Acting, Planning, and Learning
Acting, Planning, and Learning
Dana Nau, University of Maryland, College Park
Paolo Traverso, Fondazione Bruno Kessler, Trento, Italy
Michela Milano, Università degli Studi, Bologna, Italy
AUD$183.59
exc GSTAI'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.
- Helps researchers and practitioners improve the performance of intelligent systems
- Brings readers up to speed on an active subfield of AI which overcomes some of the limitations of 'black box' models
- Synthesizes published findings scattered across disconnected areas into a central reference
Product details
June 2025Hardback
9781009579384
622 pages
254 × 178 mm
100 b/w illus. 50 tables
Not yet published - available from June 2025
Table of Contents
- 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.
