15-887A: AI Planning, Execution, and Learning
Lecture Notes

• September 12: Representation and Search
• September 17: Linear and Non-Linear Planning
• September 19: Nonlinear State-Space Planning: Prodigy4.0
  The papers to read for this lecture are:
  • Integrating planning and learning: The Prodigy architecture,
    Manuela M. Veloso, Jaime Carbonell, M. Alicia P\'erez, Daniel Borrajo, Eugene Fink, and Jim Blythe.
    Journal of Experimental and Theoretical Artificial Intelligence, 7(1):81--120, 1995.
  • FLECS: Planning with a flexible commitment strategy,
    Manuela M. Veloso and Peter Stone. Journal of Artificial Intelligence Research, 3:25-52, 1995.
• September 24: Partial Order Planning
  The papers to read for this lecture are:
  • An introduction to least-commitment planning,
    Daniel Weld.
    Artificial Intelligence Magazine, 27--61, Winter 1994.
  • Systematic nonlinear planning,
    D. McAllester & D. Rosenblitt.
    In Proceedings of the Ninth National Conference on Artificial Intelligence, 1991.
• September 26: Comparison of State-Space and Plan-Space Planning
  The paper to read for this lecture is:
  • Linkability: Examining causal link commitments in partial-order planning,
    Manuela M. Veloso and Jim Blythe.
    In Proceedings of the Second International Conference on AI Planning Systems, pages 170-175, June 1994.
• October 1: Transformational Planning
• October 3: Heuristic Search Planning
  The papers to read for this lecture are:
  • Planning as heuristic search,
    Blai Bonet and Hector Geffner
    Artificial Intelligence, 129(1-2):5-33, June 2001.
  • The FF planning system: Fast planning through heuristic search,
    Joerg Hoffmann and Bernhard Nebel
    Journal of Artificial Intelligence Research, 14:253-302, 2001.
  • Flaw selection strategies for partial-order planning,
    Martha Pollack, David Joslin, Massimo Paolucci
    Journal of Artificial Intelligence Research, 6:223-262, 1997.
• October 8: No class
• October 10: GraphPlan - SatPlan
  The paper to read for this lecture is:
  • Fast planning through planning graph analysis,
    Avrim Blum and Merrick Furst.
    Artificial Intelligence, 90:281--300, 1997.
• October 15: Discussion of the First Homework
• October 17: BDD-based Planning
  The papers to read for this lecture are:
  • OBBD-based Universal planning for synchronized agents in non-deterministic domains,
    Rune Jensen and Manuela Veloso,
    Journal of Artificial Intelligence Research, 13, pages 189-226, 2000.
    A shorter version of this paper appeared at AIPS'00:
    OBDD-based Universal Planning for Multiple Synchronized Agents in Non-Deterministic Domains,
    In Proceedings of AIPS-00, 2000, pages 167-176.
  • Strong Planning in Non-Deterministic Domains via Model Checking,
    A. Cimatti, M. Rovero, P. Traverso
    In Proceedings of AIPS-98.
  • Automatic OBDD-based Generation of Universal Plans in Non-Deterministic Domains,
    A. Cimatti, M. Rovero, P. Traverso
    In Proceedings of AAAI-98.
• October 22: Conditional Planning
• October 24: Decision Theoretic Planning
• October 29: Towards Learning in Probabilistic Action Selection: Markov Systems and Markov Decision Processes
• October 31: Reinforcement Learning and Plan Recognition
  The readings for this lecture are:
  • Chapter 13, Machine Learning , Tom Mitchell, McGraw-Hill, 1997.
  • Automated robot behavior recognition applied to robotic soccer,
    Kwun Han and Manuela Veloso.
    In Proceedings of the 9th International Symposium of Robotics Research (ISSR'99), pages 199-204, October 1999.
• November 5: Planning with POMDPs
  The readings for this lecture are:
  • Planning and acting in partially observable stochastic domains Leslie Pack Kaelbling, Michael L. Littman, and Anthony R. Cassandra. Artificial Intelligence, Volume 101, pp. 99-134, 1998.
• November 7: Planning with POMDPs II
  The readings for this lecture are:
  • An Improved Policy Iteration Algorithm for Partially Observable MDPs E. Hansen. Proceedings of the Tenth Neural Information Processing Systems Conference (NIPS-97). Denver, Colorado, December 1997.
  • An improved grid-based approximation algorithm for POMDPs R. Zhou and E. Hansen. Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI-01), Seattle, Washington, August 2001.
  • Approximate Planning in Large POMDPs via Reusable Trajectories M. Kearns, Y. Mansour and A. Ng. Advances in Neural Information Processing Systems 12, MIT Press, 2000.
• November 12: Hierarchical Task Net Planning
• November 14: Execution Architectures
• November 19: Scheduling and Planning
• November 26: Reactive Planning
• December 5: Monitoring and Diagnosis