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Optimal temporal planning using the plangraph framework

Dinh, Tien Ba (2007) Optimal temporal planning using the plangraph framework. Doctoral thesis, University of Huddersfield.

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Abstract

The past few years have seen a rapid development in AI Planning and Scheduling. Many
algorithms and techniques have been studied and improved to deal with more complex and
difficult planning domains.
One such innovation was Graphplan, first developed by Blum and Furst in 1995 and soon
became one of the best approaches for optimal classical planning systems. Planning systems
that use Graphplan’s plangraph framework can find optimal plans for temporal planning
problems, in which actions have durations. However, these systems have had strict
assumptions on the preconditions and effects of actions, for instance, effects happen only
at the end of the execution. In addition, the algorithm used in the solution extraction phase
of these plangraph-based systems does not take full advantage of the information provided
by the expansion phase to prune irrelevant search branches early.
With the ambition to make temporal planning problems more realistic, the thesis proposes
an extension to the Planning Domain Definition Language (PDDL) 2.1 level 3, to allow
actions to have intermediate effects. Our optimal temporal planning system, CPPlanner,
is introduced as the first Graphplan-based optimal planner to handle the richer temporal
domains (i.e. actions can have intermediate effects). Futhermore, the planner applies “critical
paths” as a backbone for the search in the solution extraction phase, so that irrelevant
search branches are pruned early. This improves the performance even in more restricted
temporal planning domains.
In our experimental evaluation, CPPlanner outperforms two leading plangraph-based optimal
temporal planning systems, TGP and TPSYS, in almost all test cases. The state-of-theart
optimal planner CPT and latest temporal planning domains in the international planning
competition in 2004 and 2006 are also used in the experimental evaluation.

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Item Type: Thesis (Doctoral)
Additional Information: © Tien Ba Dinh
Uncontrolled Keywords: ai artificial intelligence planning scheduling systems plangraph framework temporal
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools: School of Computing and Engineering
References: [1] Choco constraint programming system. Available at http://chocosolver. net/index.php?title=Main Page. [2] CPT: Constraint Programming Temporal planner. Available at http://www.cril.univartois. fr/ vidal/cpt.html. [3] F. Bacchus and M. Ady. Planning with Resources and Concurrency: A Forward Chaining Approach. In Proceeding of International Joint Conference on Artificial Intelligence, pages 417–424, 2001. [4] F. Bacchus and F. Kabanza. Using Temporal Logic to Control Search in a Forward Chaining Planner. In M. Ghallab and A. Milani, editor, New Directions in Planning, pages 141–153. IOS Press, 1996. [5] F. Bacchus and F. Kabanza. Planning for Temporally Extended Goals. Annals of Mathematics and Artificial Intelligence, 22:5–27, 1998. [6] F. Bacchus and F. Kabanza. Using Temporal logics to express search control knowledge for planning. Artificial Intelligence, 22:5–27, 2000. [7] F. Bacchus and F. Kabanza. Using Temporal Logics to Express Search Control Knowledge for Planning. Artificial Intelligence, 116, 2000. [8] A. Barrett and D. S. Weld. Partial order planning: Evaluating possible efficiency gains. Artificial Intelligence, 67(1):71–112, 1994. [9] Eric Beaudry, Froduald Kabanza, and Franois Michaud. Planning for a Mobile Robot to Attend a Conference. In Proceeding of the Canadian Conference on Artificial Intelligence, pages 48–52, 2005. [10] M. Beetz. Plan-based Control of Robotics Agents. Lecture Notes om Artificial Intelligence, 2554, 2002. [11] M. Beetz and T. Belker. Environment and task adaptation for robotics aganets. In Proceeding of the European Conference on Artificial Intelligence Robotics and Automation in Space, pages 648–657. IOS Press, 2000. [12] M. Beetz, J. Hertzberg, M. Ghallab, and M. Pollack. Advances in Plan-Based Control of Robotics Agents. Lecture Notes om Artificial Intelligence, 2266, 2002. [13] A. L. Blum and M. L. Furst. Fast planning through Planning Graph Analysis. Artificial Intelligence, 90:281–300, 1997. [14] Blai Bonet and Hector Geffner. Planning as heuristic search. Artificial Intelligence, 129(1-2):5–33, 2001. [15] Luis Castillo, Juan Fdez-Olivares, and Antonio Gonzalez. A temporal constraint network based temporal planner. In Proceedings of the 21st workshop of the UK Planning and Scheduling Special Interest Group PLANSIG2002, 2002. [16] A. Cesta and A. Odi. Gaining efficiency and flexibility in the simple temporal problem. In Proceeding of 3rd International Workshop on Temporal Representation and Reasoning. IEEE-CS Press, 1996. [17] S. Chien. Static and completion analysis for planning knowledge base development and verification. In Proceeding of the Third International Conference on Artificial Intelligence Planning Systems, pages 53–61, 1996. [18] Steve Chien, Benjamin Smith, Gregg Rabideau, Nicola Muscettola, and Kanna Rajan. Automated Planning and Scheduling for Goal-Based Autonomous Spacecraft. IEEE Intelligent Systems, 13:5:50–55, 1998. [19] K. Currie and A. Tate. O-PLAN: The open planning architecture. Artificial Intelligence, 51(1):49–86, 1991. [20] Rina Dechter, Itay Meiri, and Judea Pearl. Temporal constraint networks. Artificial Intelligence, 49(1-3):61–95, 1991. [21] R. Detchter, I. Meiri, and J. Pearl. Temporal constraint networks. Artificial Intelligence, 49:61–95, 91. [22] M. B. Do and S. Kambhampati. Solving Planning Graph by compiling it into a CSP. In Proceedings of the Fifth International Conferenceon Artificial Intelligence Planning and Scheduling, 2000. [23] M. B. Do and S. Kambhampati. Sapa: A Domain-Independent Heuristic Metric Temporal Planner. In Proceedings of European Conference on Planning, 2001. [24] P. Doherty and J. Kvarnstrm. TALplanner: An Empirical Investigation of a Temporal Logic-based Forward Chaining Planner. In Proceedings of the 6th International Workshop on the Temporal Representation and Reasoning, 1999. [25] P. Doherty and J. Kvarnstrm. TALplanner: A Temporal Logic Based Planner. AI Magazine, 2001. [26] P. Drabble and A. Tate. The use of optimistic and pessimistic resource profiles to inform search in an activity based planner. In Proceedings of 2nd International Conference AI Planning Systems, 1994. [27] S. Edelkamp and J. Hoffmann. The language for the 2004 international planning competition. Technical Report, available at: http://ls5-www.cs.uni-dortmund.de/ edelkamp/ipc-4/pddl.html, 2004. [28] Stefan Edelkamp, Jorg Hoffmann, Michael Littman, Hakan Younes, et al. International Planning Competition 2004 - IPC4. Available at:http://andorfer.cs.unidortmund. de/ edelkamp/ipc-4/, 2004. [29] Amin El-Kholy and Barry Richards. Temporal and resource reasoning in planning: The parcPLAN approach. In W. Wahlster, editor, Proceedings of the 12th European Conference on Artificial Intelligence (ECAI-96), pages 614–618.Wiley & Sons, 1996. [30] Kultuhan Erol, Dana Nau, and V.S. Subrahmania. Complexity, Decidability and Undecidability for Domain Independent Planning. Artificial Intelligence, 76:75–88, 1995. [31] R. E. Fikes and N. J. Nilsson. STRIPS: a new approach to the application of theoremproving to problem solving. Artificial Intelligence, 2(3-4):189–208, 1971. [32] M. Fox and D.Long. PDDL+: An extension to PDDL2.1 for modelling planning domains with continuous time-dependent effects. In Technical Report, Dept of Computer Science, University of Durham, 2001. [33] M. Fox and D. Long. Hybrid STAN: Identifying and Managing Combinatorial Optimisation Sub-problems in Planning. In Proceedings of IJCAI, 2001. [34] M. Fox and D. Long. PDDL 2.1: An extension to PDDL for expressing temporal planning domains. Technical Report, Department of Computer Science, University of Durham, UK, 2001. [35] M. Fox and D. Long. PDDL+ Level 5: An extension to PDDL2.1 for Modelling Planning Domains with Continous Time-dependent Effects. Technical Report, available at: http://planning.cis.strath.ac.uk/publications/oldpapers/pddllevel5.ps.gz, 2001. [36] M. Fox, D. Long, and K. Halsey. An investigation into the expressive power of PDDL2.1. In Proceedings of the Tenth European Conference on Artificial Intelligence, 2004. [37] Maria Fox and Derek Long. PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains. Journal of Artificial Intelligence Research, 20:61– 124, 2003. [38] A. Garrido, M. Fox, and D. Long. Temporal Planning with PDDL2.1. In Proceeding of ECAI’02, 2002. [39] Antonio Garrido and Eva Onaindia. On the application of least-commitment and heuristic search in temporal planning. In Proceedings of the International Joint Conference on Artificial Intellience, pages 942–947, 2003. [40] A. Gerevini and D. Long. Plan Constraints and Preferences in PDDL3. Technical Report, RT 2005-08-47, Dept. of Electronics for Automation, University of Brescia, Italy. Available at: http://zeus.ing.unibs.it/ipc-5/pddl-ipc5.pdf, 2006. [41] A. Gerevini and L. Schubert. Efficient algorithms for handling qualitative reasoning about time. Artificial Intelligence, 74(1):207–248, 1995. [42] M. Ghallab, D. Nau, and P. Traverso. Automated Planning: Theory and Pratice. Elsevier, 2004. [43] Keith Golden, Wanlin Pang, Ramakrishna Nemani, and Petr Votava. Automated Data Processing as an AI Planning Problem. In NASA, avalaible at: http://ase.arc.nasa.gov/publications/pdf/0629.pdf, 2003. [44] C. Green. Theorem proving by resolution as a basis for questionanswering systems. Machine Intelligence, 4, 1969. [45] S.K. Gupta, D.S. Nau, andW.C. Regli. IMACS: A case study in real-world planning. In IEEE Expert and Intelligent Systems, volume 3, pages 49–60, 1998. [46] Patrik Haslum. Improving heuristics through search. In Proceedings of the 16th European Conference on Artificial Intelligence (ECAI-2004), pages 1031–1032, 2004. [47] Patrik Haslum. TP’04 and HSP¤a. The 4th International Planning Competition Booklet. Available at http://ls5-web.cs.uni-dortmund.de/ edelkamp/ipc-4/, pages 38– 40, 2004. [48] Patrik Haslum and Hector Geffner. Admissible heuristics for optimal planning. In Proceedings of the Fifth International Conference on Artificial Intelligence Planning and Scheduling, pages 70–82, 2000. [49] Patrik Haslum and Hector Geffner. Heuristic planning with time and resources. In Proceedings of the European Conference on Planning, 2001. [50] J. Hoffman. FF: The Fast Forward planning system. AI Magazine, 22(3):57–62, 2001. [51] Luke Hunsberger. Algorithms for a temporal decoupling problem in multi-agent planning. In The eighteenth national conference on Artificial intelligence, pages 468–475. American Association for Artificial Intelligence, 2002. [52] Nathanael Hyafil and Fahiem Bacchus. Utilizing structured representations and csp’s in conformant probabilistic planning. In Proceedings of the Tenth European Conference on Artificial Intelligence, pages 1033–1034, 2004. [53] S. Kambhampati. Planning Graph as a (dynamic) CSP: Exploiting EBL, DDB, and other CSP search techniques in Graphplan. Artificial Intelligence Research, 12:1– 34, 2000. [54] Henry Kautz and Bart Selman. Unifying SAT-Based and Graph-Based Planning. In Jack Minker, editor, Workshop on Logic-Based Artificial Intelligence, Washington, DC, June 14–16, 1999. Computer Science Department, University of Maryland, 1999. [55] Henry Kautz, Bart Selman, and Joerg Hoffmann. Satplan: Planning as satisfiability. http://www.cs.rochester.edu/u/kautz/satplan/index.htm, 2006. [56] Henry A. Kautz and Bart Selman. Planning as Satisfiability. In Proceedings of the Tenth European Conference on Artificial Intelligence (ECAI’92), pages 359–363, 1992. [57] Russell Knight, Gregg Rabideau, Steve Chien, Barbara Engelhardt, and Rob Sherwood. Casper: Space Exploration through Continuous Planning. IEEE Intelligent Systems, 16:5:70–55, 2001. [58] J. Koehler. Planning under resource constraints. In Proceedings of the 15th European Conference on AI, 1998. [59] J. Koehler. Metric planning using planning graphs - A first investigation. In Technical report No. 127, Albert Ludwings University, 1999. [60] J. Koehler, B. Nebel, J. Hoffmann, and Y. Dimopoulos. Extending Planning Graphs to an ADL Subset. In Proceedings of 5th the European Conference in Planning, pages 273–285, 1997. [61] J. Kvarnstrm and P. Doherty. TALplanner: A Temporal Logic Based Forward Chaining Planner. Annals of Mathematics and Artificial Intelligence (AMAI), 30:119–169, 2001. [62] J. Kvarnstrom, P. Doherty, and P. Haslum. Extending TALplanner with Concurrency and Resources. In Proceedings of the 14th European Conference on Artificial Intelligence, 2000. [63] P. Laborie and M. Ghallab. Planning with sharable resource constraints. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pages 1643–1649, 1995. [64] P. Laborie and M. Ghallab. Planning with sharable resource constraints. In Proceedings of the International Joint Conference on Artificial Intellience, pages 1643– 1649, 1995. [65] Olivier Lhomme. Consistency techniques for numeric csps. In Proceedings of the International Joint Conference on Artificial Intellience, pages 232–238, 1993. [66] D. Long and M. Fox. Progress in AI Planning Research and Applications. Upgrade, 5:10–25, 2002. [67] D. Long and M. Fox. Exploiting a graphplan framework in temporal planning. In Proceedings of The Thirteenth International Conference on Automated Planning and Scheduling, pages 51–62, 2003. [68] Adriana Lopez and Fahiem Bacchus. Generalizing graphplan by formulating planning as a csp. In Proceedings of the International Joint Conference on Artificial Intellience, pages 954–960, 2003. [69] Amol Mali. Encoding temporal planning as CSP. In Proceeding of the AIPS workshop on planning in temporal domains, Toulouse, France, pages 18–25. [70] J. McCarthy. Programs with common sense. In Memo No 7, Stanford Artificial Intelligence Project. Stanford University, 1963. [71] J. McCarthy and P.J. Hayes. Some philosophical problems from the standpoint of artificial intelligence. Machine Intelligence, 4, 1969. [72] D. McDermott and the AIPS-98 Planning Competition Committee. PDDLthe planning domain definition language. Technical report, available at: http://www.cs.yale.edu/homes/dvm, 1998. [73] Drew McDermott. The 1998 AI Planning Systems Competition. AI Magazine, 21(2):35–55, 2000. [74] N. Muscettola, B. Smith, S. Chien, C. Fry, K. Rajan, S. Mohan, G. Rabideau, and D. Yan. On-Board Planning for the New Millennium Deep Space One Spacecraft. In Proceeding of the IEEE Aerospace Conference, pages 303–318, 1997. [75] D. Nau, S. Gupta, and W. Regli. Artificial intelligence planning versus manufacturing-operation planning: a case study. In Proceeding of 14th International Joint Conference on Artificial Intelligence, pages 1670–1676. Morgan Kaufmann, 1995. [76] D. S. Nau, T. C. Au, O. Ilghami, U. Kuter, W. Murdock, D. Wu, and F. Yaman.SHOP2: An HTN planning system. Journal of Artificial Intelligence Research, 20(3):379–404, 2003. [77] D. S. Nau, Y. Cao, A. Lotem, and H. Munoz-Avila. SHOP: Simple Hierarchical Ordered Planner. In Proceedings of the International Joint Conference on Artificial Intellience, pages 968–973, 1999. [78] D. S. Nau, H. Munoz-Avila, Y. Cao, A. Lotem, , and S. Mitchell. Total ordering with partially ordered subtasks. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2001. [79] A. Newell and H. Simon. GPS, a program that simulates human thought. Computers and Thought, 1963. [80] R. Nigenda, X. Nguyen, and S. Kambhampati. AltAlt: Combining the advantages of Graphplan and heuristic state search, 2000. [81] Edwin P. D. Pednault. ADL: exploring the middle ground between STRIPS and the situation calculus. In Proceedings of the first international conference on Principles of knowledge representation and reasoning, pages 324–332, San Francisco, CA, USA, 1989. Morgan Kaufmann Publishers Inc. [82] J. Penberthy and D. S. Weld. UCPOP: A sound, complete, partial order planner for ADL. In Proceedings of the International Conference on Knowledge Representation and Reasoning(KR), pages 103–114, 1992. [83] J. Penberthy and D. S. Weld. Temporal planning with continuous change. In Proceedings of the National Conference on Artificial Intelligence (AAAI), pages 1010– 1015, 1994. [84] J.S. Penberthy. Planning with Continuous Change. PhD thesis, 1993. [85] P. Prosser. Domain filtering can degrade intelligent backtracking search. In Proceedings of IJCAI, pages 262–267, 1993. [86] G. Rabideau, R. Knight, S. Chien, A. Fukunaga, and A. Govindjee. Iterative Repair Planning for Spacecraft Operations in the ASPEN System. In Proceeding of the Fifth International Symposium on Artificial Intelligence Robotics and Automation in Space, pages 99–106. ESA Publications Division, 1999. [87] J. Rintanen and J. Hoffmann. An Overview of Recent Algorithms for AI Planning. Knstliche Intelligenz, 2/01:5–11, 2001. [88] D. Smith and D. Weld. Temporal Planning with Mutual Exclusion Reasoning. In Proceedings of IJCAI, pages 326–337, 1999. [89] D. Smith and D. Weld. Temporal Planning with Mutual Exclusion Reasoning. In Proceedings of IJCAI, pages 326–337, 1999. [90] David Smith. Coping with Time and Continuous Quantities. Talks at the AIPS 2000. Available at http://ic.arc.nasa.gov/people/de2smith/publications/AIPS-2000- talk.pdf, 2000. [91] David Smith. The case for durative actions: A commentary on PDDL2.1. Journal of Artificial Intelligence Research, 20:149–154, 2003. [92] David Smith and Daniel Weld. Temporal Graphplan. Homepage for Temporal Graphplan (TGP). Available at http://www.cs.washington.edu/ai/tgp.html. [93] S. Smith, D. Nau, and T. Throop. Total-order multi-agent task-network planning for contract bridge. In Proceeding of the Fourteenth National Conference on Artificial Intelligence, pages 108–113, 1996. [94] B. Srivastava and S. Kambhampati. Scaling up planning by teasing out resource scheduling. In Proceedings of 5th the European Conference in Planning, 1999. [95] B. Srivastava and S. Kambhampati. Realplan: decoupling causal and resource reasoning in planning. In Proceedings of the National Conference on Artificial Intelligence (AAAI), 2000. [96] Pavel Surynek and Roman Bart´ak. Encoding htn planning as a dynamic csp. In Procee
Depositing User: Sara Taylor
Date Deposited: 20 Dec 2007
Last Modified: 31 Mar 2018 15:49
URI: http://eprints.hud.ac.uk/id/eprint/250

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