Automated Planning

Objectives

The intent of this course is to give students an understanding of automated planning theory and practice, and how they relate to each other to devise intelligent robots. This course contains a set of theoretical lessons to introduce the theory of automated planning, illustrated by examples from robotics.

Outline of the Course

• Part I. Classical approaches to planning
  • Chapter 1. Introduction and Overview of AI Planning
  • Chapter 2. Classical Planning Representation and Complexity
  • Chapter 3. State-space Planning
  • Chapter 4. Plan-space Planning
• Part II. Neo-classical approaches to planning
  • Chapter 5. Planning Graph Techniques
  • Chapter 6. Propositional Planning Techniques
  • Chapter 7. Constraints Satisfaction Techniques
• Part III. Heuristics and control strategies for planning
  • Chapter 8. Heuristic Planning
  • Chapter 9. Hierarchical Task Network Planning
• Part IV. Planning under uncertainty
  • Chapter 10. Planning based on Markov Decision Processes
  • Chapter 11. Planning based on Model Checking

Exercises

All the exercises for the course are available online.

Consignes de rendu

TPs must be sent by email to damien.pellier@imag.fr in zip format.

• TP n°1 Sokoban is due on 16/10.
• TP n°2 Monte Carlo Tree Search is due on 08/11.
• TP n°3 SAT Planner is due on 06/12.

Merci de respecter les dates limites. Aucun rendu ne sera accepté après les deadlines sans justification sérieuse.

Bibliography

• M. Ghallab, D. Nau, and P. Traverso, "Automated Planning", Morgan-Kaufman, 2004.
• M. Ghallab, D. Nau, and P. Traverso, "Automated Planning and Acting", Morgan-Kaufman, 2018.
• S. Russell and P. Norvig, "Artificial Intelligence: A Modern Approach", chapter XI, Prentice Hall, 2002.
• S. LaValle, Planning Algorithms, Cambrige Press , 1999.