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"AI's next big challenge is integrating and automating the essential cognitive abilities of acting, planning, and learning. This comprehensive overview covers a range of models -deterministic, probabilistic (including MDP and reinforcement learning), hierarchical, nondeterministic, temporal, spatial - and applications in robotics"--
List 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.
About the author
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.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.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.
Summary
AI's next big challenge is integrating and automating the essential cognitive abilities of acting, planning, and learning. This comprehensive overview covers a range of models –deterministic, probabilistic (including MDP and reinforcement learning), hierarchical, nondeterministic, temporal, spatial – and applications in robotics.
Foreword
An overview of AI's next big challenge: integrating the essential cognitive functions needed by robots and other automated agents.
