"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"--
"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"--
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.
Inhaltsangabe
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 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.
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