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OBDD-based Universal Planning: Specifying and Solving Planning Problems for Synchronized Agents in Non-deterministic Domains

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Artificial Intelligence Today

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1600))

Abstract

Recently model checking representation and search techniques were shown to be efficiently applicable to planning, in particular to non-deterministic planning. Such planning approaches use Ordered Binary Decision Diagrams (OBDDs) to encode a planning domain as a non-deterministic finite automaton (NFA) and then apply fast algorithms from model checking to search for a solution. OBDDs can effectively scale and can provide universal plans for complex planning domains. We are particularly interested in addressing the complexities arising in non-deterministic, multi-agent domains. In this chapter, we present UMOP, a new universal OBDD-based planning framework for non-deterministic, multi-agent domains, which is also applicable to deterministic single-agent domains as a special case. We introduce a new planning domain description language, NADL, to specify non-deterministic multi-agent domains. The language contributes the explicit definition of controllable agents and uncontrollable environment agents. We describe the syntax and semantics of NADL and show how to build an efficient OBDD-based representation of an NADL description. The UMOP planning system uses NADL and different OBDD-based universal planning algorithms. It includes the previously developed strong and strong cyclic planning algorithms [9, 10]. In addition, we introduce our new optimistic planning algorithm, which relaxes optimality guarantees and generates plausible universal plans in some domains where no strong or strong cyclic solution exist. We present empirical results from domains ranging from deterministic and single-agent with no environment actions to non-deterministic and multi-agent with complex environment actions. Umop is shown to be a rich and efficient planning system.

UMOP stands for Universal Multi-agent Obdd-based Planner.

NADL stands for Non-deterministic Agent Domain Language.

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References

  1. F. Bacchus and F. Kabanza. Using temporal logic to control search in a forward chaining planner. In M. Ghallab and A. Milani, editors, New directions in AI planning, pages 141–153. ISO Press, 1996.

    Google Scholar 

  2. C. Baral and M. Gelfond. Reasoning about effects of concurrent actions. The Journal of Logic Programming, pages 85–117, 1997.

    Google Scholar 

  3. A. Blum and M. L. Furst. Fast planning through planning graph analysis. In Proceedings of the 14’th International Conference on Artificial Intelligence (IJCAI-95), pages 1636–1642. Morgan Kaufmann, 1995.

    Google Scholar 

  4. B. Bonet, G. Loerincs, and H. Geffner. A robust and fast action selection mechanism for planning. In Proceedings of the 14’th National Conference on Artificial Intelligence (AAAI’97), pages 714–719. AAAI Press / The MIT Press, 1997.

    Google Scholar 

  5. R. E. Bryant. Graph-based algorithms for boolean function manipulation. IEEE Transactions on Computers, 8:677–691, 1986.

    Article  MATH  Google Scholar 

  6. J.R. Burch, E.M. Clarke, and D.E. Long. Symbolic model checking with partitioned transition relations. In International Conference on Very Large Scale Integration, pages 49–58. North-Holland, 1991.

    Google Scholar 

  7. E. Carke, O. Grumberg, and D. Peled. Model Checking. MIT Press, 1999. In Press.

    Google Scholar 

  8. A. Cimatti, E. Giunchiglia, F. Giunchiglia, and P. Traverso. Planning via model checking: A decision procedure for . In Proceedings of the 4’th European Confer ence on Planning (ECP’97), Lecture Notes in Artificial Intelligence, pages 130–142. Springer-Verlag, 1997.

    Google Scholar 

  9. A. Cimatti, M. Roveri, and P. Traverso. Automatic OBDD-based generation of universal plans in non-deterministic domains. In Proceedings of the 15’th National Conference on Artificial Intelligence (AAAI’98), pages 875–881. AAAI Press/The MIT Press, 1998.

    Google Scholar 

  10. A. Cimatti, M. Roveri, and P. Traverso. Strong planning in non-deterministic domains via model checking. In Proceedings of the 4’th International Conference on Artificial Intelligence Planning System (AIPS’98), pages 36–43. AAAI Press, 1998.

    Google Scholar 

  11. E. M. Clarke, E. A. Emerson, and A. P. Sistla. Automatic verification of finite-state concurrent systems using temporal logic specifications. ACM transactions on Programming Languages and Systems, 8(2):244–263, 1986.

    Article  MATH  Google Scholar 

  12. K. Currie and A. Tate. O-plan: the open planning architecture. Artificial Intelligence, 52:49–86, 1991.

    Article  Google Scholar 

  13. T. Dean, L. P. Kaelbling, J. Kirman, and A. Nicholson. Planning under time constraints in stochastic domains. Artificial Intelligence, 76:35–74, 1995.

    Article  Google Scholar 

  14. M. Di Manzo, E. Giunchiglia, and S. Ruffino. Planning via model checking in deterministic domains: Preliminary report. In Proceedings of the 8’th International Conference on Artificial Intelligence: Methodology, Systems and Applications (AIMSA’98), pages 221–229. Springer-Verlag, 1998.

    Google Scholar 

  15. M. Drummond. Situated control rules. In Proceedings of the 1’st International Conference on Principles of Knowledge Representation and Reasoning (KR’ 89), pages 103–113. Morgan Kaufmann, 1989.

    Google Scholar 

  16. M. Drummond and J. Bresina. Anytime synthetic projection: Maximizing the probability of goal satisfaction. In Proceedings of the 8’th Conference on Arti ficial Intelligence, pages 138–144. AAAI Press / The MIT Press, 1990.

    Google Scholar 

  17. O. Etzioni, S. Hanks, D. Weld, D. Draper, N. Lesh, and M. Williamson. An ap proach for planning with incomplete information. In Proceedings of the 3’rd In ternational Conference on Principles of Knowledge Representation and Reasoning, 1992.

    Google Scholar 

  18. R. E. Fikes and N. J. Nilsson. STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2:189–208, 1971.

    Article  MATH  Google Scholar 

  19. E. Gat. Integrating planning and reacting in a heterogeneous asynchronous architecture for controlling real-world mobile robots. In Proceedings of the 10’th National Conference on Artificial Intelligence (AAAI’92), pages 809–815. MIT Press, 1992.

    Google Scholar 

  20. M. Gelfond and V. Liftschitz. Representing action and change by logic programs. The Journal of Logic Programming, 17:301–322, 1993.

    Article  MathSciNet  MATH  Google Scholar 

  21. M. P. Georgeff and A. L. Lansky. Reactive reasoning and planning. In Proceedings of the 6’th National Conference on Artificial Intelligence (AAAI’87), pages 677–682, 1987.

    Google Scholar 

  22. M.L. Ginsberg. Universal planning: An (almost) universal bad idea. AI Magazine, 10(4):40–44, 1989.

    Google Scholar 

  23. E. Giunchiglia, G. N. Kartha, and Y. Lifschitz. Representing action: Indetermi nacy and ramifications. Aritificial Intelligence, 95:409–438, 1997.

    Article  MATH  Google Scholar 

  24. E. Giunchiglia and V. Lifschitz. An action language based on causal explanation: Preliminary report. In Proceedings of the 15’th National Conference on Artificial Intelligence (AAAI’98), pages 623–630. AAAI Press/The MIT Press, 1998.

    Google Scholar 

  25. K. Z. Haigh and M. M. Veloso. Planning, execution and learning in a robotic agent. In Proceedings of the 4’th International Conference on Artificial Intelligence Planning Systems (AIPS’98), pages 120–127. AAAI Press, 1998.

    Google Scholar 

  26. R. M. Jensen. OBDD-based universal planning in multi-agent, non-deterministic domains. Master’s thesis, Technical University of Denmark, Department of Automation, 1999. IAU99F02.

    Google Scholar 

  27. F. Kabanza, M. Barbeau, and R. St-Denis. Planning control rules for reactive agents. Artificial Intelligence, 95:67–113, 1997.

    Article  MATH  Google Scholar 

  28. L. P. Kaebling, M. L. Littman, and A. W. Moore. Reinforcement learning: a survey. Journal of Artificial Intelligence Research, 4:237–285, 1996.

    Google Scholar 

  29. H. Kautz and B. Selman. Pushing the envelope: Planning, propositional logic and stochastic search. In Proceedings of the 13’th National Conference on Artificial Intelligence (AAAI’96), volume 2, pages 1194–1201. AAAI Press/MIT Press, 1996.

    Google Scholar 

  30. S. Koenig and R. G. Simmons. Real-time search in non-deterministic domains. In Proceedings of the 14’th International Joint Conference on Artificial Intelligence (IJCAI-95), pages 1660–1667. Morgan Kaufmann, 1995.

    Google Scholar 

  31. J. Lever and B. Richards. Parcplan: a planning architecture with parallel actions and constraints. In Lecture Notes in Artificial Intelligence, pages 213–222. ISMIS’94, Springer-Verlag, 1994.

    Google Scholar 

  32. J. Lind-Nielsen. BuDDy-A Binary Decision Diagram Package. Technical Report IT-TR: 1999-028, Institute of Information Technology, Technical University of Denmark, 1999. http://cs.it.dtu.dk/buddy.

  33. A. R. Lingard and E. B. Richards. Planning parallel actions. Artificial Intelligence, 99:261–324, 1998.

    Article  MATH  Google Scholar 

  34. K. L. McMillan. Symbolic Model Checking. Kluwer Academic Publ., 1993.

    Google Scholar 

  35. J. S. Penberthy and D. S. Weld. UCPOP: A sound, complete, partial order planner for ADL. In Proceedings of the 3’rd International Conference on Principles of Knowledge Representation and Reasoning, pages 103–114. Morgan Kaufmann, 1992.

    Google Scholar 

  36. M. Peot and D. Smith. Conditional nonlinear planning. In Proceedings of the 1’st International Conference on Artificial Intelligence Planning Systems (AIPS’92), pages 189–197. Morgan Kaufmann, 1992.

    Google Scholar 

  37. R. K. Ranjan, A. Aziz, R. K. Brayton, B. Plessier, and C. Pixley. Efficient BDD algorithms for FSM synthesis and verification. In IEEE/ACM Proceedings Inter national Workshop on Logic Synthesis, 1995.

    Google Scholar 

  38. M. J. Schoppers. Universal plans for reactive robots in unpredictable environ ments. In Proceedings of the 10’th International Joint Conference on Artificial Intelligence (IJCAI-87), pages 1039–1046. Morgan Kaufmann, 1987.

    Google Scholar 

  39. P. Stone and M. M. Veloso. Towards collaborative and adversarial learning: A case study in robotic soccer. International Journal of Human-Computer Studies (IJHCS), 1998.

    Google Scholar 

  40. R. S. Sutton and Barto A.G. Reinforcement Learning: An Introduction. MIT Press, 1998.

    Google Scholar 

  41. M. Veloso, J. Carbonell, A. Pérez, D. Borrajo, E. Fink, and J. Blythe. Integrating planning and learning: The PRODIGY architecture. Journal of Experimental and Theoretical Artificial Intelligence, 7(1), 1995.

    Google Scholar 

  42. M. M. Veloso, M. E. Pollack, and M. T. Cox. Rationale-based monitoring for plan ning in dynamic environments. In Proceedings of the 4’ th International Conference on Artificial Intelligence Planning Systems (AIPS’98), pages 171–179. AAAI Press, 1998.

    Google Scholar 

  43. D. Weld. Recent advances in AI planning. Artificial Intelligence Magazine, 1999. (in press).

    Google Scholar 

  44. D. E. Wilkins. K. L. Myers, J. D. Lowrance, and L. P. Wesley. Planning and re acting in uncertain and dynamic environments. Journal of Experimental and The oretical Artificial Intelligence, 6:197–227, 1994.

    Google Scholar 

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Authors and Affiliations

  1. Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, 15213

    Rune M. Jensen & Manuela M. Veloso

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  1. Rune M. Jensen
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  2. Manuela M. Veloso
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Editors and Affiliations

  1. Department of Electronic Engineering, Queen Mary and Westfield College, University of London, London, E1 4NS, UK

    Michael J. Wooldridge

  2. Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Manuela Veloso

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© 1999 Springer-Verlag Berlin Heidelberg

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Jensen, R.M., Veloso, M.M. (1999). OBDD-based Universal Planning: Specifying and Solving Planning Problems for Synchronized Agents in Non-deterministic Domains. In: Wooldridge, M.J., Veloso, M. (eds) Artificial Intelligence Today. Lecture Notes in Computer Science(), vol 1600. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48317-9_9

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  • DOI: https://doi.org/10.1007/3-540-48317-9_9

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  • Print ISBN: 978-3-540-66428-4

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