Advertisement

New Techniques for Checking Dynamic Controllability of Simple Temporal Networks with Uncertainty

  • Luke HunsbergerEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8946)

Abstract

A Simple Temporal Network with Uncertainty (STNU) is a structure for representing time-points, temporal constraints, and temporal intervals with uncertain—but bounded—durations. The most important property of an STNU is whether it is dynamically controllable (DC)—that is, whether there exists a strategy for executing its time-points such that all constraints will necessarily be satisfied no matter how the uncertain durations turn out. Algorithms for checking from scratch whether STNUs are dynamically controllable are called (full) DC-checking algorithms. Algorithms for checking whether the insertion of one new constraint into an STNU preserves its dynamic controllability are called incremental DC-checking algorithms. This paper introduces novel techniques for speeding up both full and incremental DC checking. The first technique, called rotating Dijkstra, enables constraints generated by propagation to be immediately incorporated into the network. The second uses novel heuristics that exploit the nesting structure of certain paths in STNU graphs to determine good orders in which to propagate constraints. The third technique, which only applies to incremental DC checking, maintains information acquired from previous invocations to reduce redundant computation. The most important contribution of the paper is the incremental algorithm, called Inky, that results from using these techniques. Like its fastest known competitors, Inky is a cubic-time algorithm. However, a comparative empirical evaluation of the top incremental algorithms, all of which have only very recently appeared in the literature, must be left to future work.

Keywords

Temporal Networks Uncertainty Dynamic controllability 

References

  1. 1.
    Chien, S., Sherwood, R., Rabideau, G., Zetocha, P., Wainwright, R., Klupar, P., Gaasbeck, J.V., Castano, R., Davies, A., Burl, M., Knight, R., Stough, T., Roden, J.: The techsat-21 autonomous space science agent. In: The First International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-2002), pp. 570–577, ACM Press (2002)Google Scholar
  2. 2.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms. MIT Press, Cambridge (2009)zbMATHGoogle Scholar
  3. 3.
    Dechter, R., Meiri, I., Pearl, J.: Temporal constraint networks. Artif. Intell. 49, 61–95 (1991). ElsevierMathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Hunsberger, L.: A faster execution algorithm for dynamically controllable STNUs. In: Proceedings of the 20th Symposium on Temporal Representation and Reasoning (TIME-2013) (2013)Google Scholar
  5. 5.
    Hunsberger, L.: Magic loops in simple temporal networks with uncertainty. In: Proceedings of the Fifth International Conference on Agents and Artificial Intelligence (ICAART-2013) (2013)Google Scholar
  6. 6.
    Hunsberger, L.: A faster algorithm for checking the dynamic controllability of simple temporal networks with uncertainty. In: Proceedings of the 6th International Conference on Agents and Artificial Intelligence (ICAART-2014), SciTePress (2014)Google Scholar
  7. 7.
    Hunsberger, L., Posenato, R., Combi, C.: The dynamic controllability of conditional STNs with uncertainty. In: Proceedings of the PlanEx Workshop at ICAPS-2012, pp. 121–128 (2012)Google Scholar
  8. 8.
    Morris, P.: A structural characterization of temporal dynamic controllability. In: Benhamou, F. (ed.) CP 2006. LNCS, vol. 4204, pp. 375–389. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  9. 9.
    Morris, P.: Dynamic controllability and dispatchability relationships. In: Simonis, H. (ed.) CPAIOR 2014. LNCS, vol. 8451, pp. 464–479. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  10. 10.
    Morris, P., Muscettola, N., Vidal, T.: Dynamic control of plans with temporal uncertainty. In: Nebel, B. (ed.) 17th International Joint Conference on Artificial Intelligence (IJCAI-01), pp. 494–499, Morgan Kaufmann (2001)Google Scholar
  11. 11.
    Morris, P.H., Muscettola, N.: Temporal dynamic controllability revisited. In: Veloso, M.M., Kambhampati, S. (eds.) The 20th National Conference on Artificial Intelligence (AAAI-2005), pp. 1193–1198, MIT Press (2005)Google Scholar
  12. 12.
    Nilsson, M., Kvarnstrom, J., Doherty, P.: Incremental dynamic controllability revisited. In: Proceedings of the 23rd International Conference on Automated Planning and Scheduling (ICAPS-2013) (2013)Google Scholar
  13. 13.
    Nilsson, M., Kvarnstrom, J., Doherty, P.: EfficientIDC: a faster incremental dynamic controllability algorithm. In: Proceedings of the 24th International Conference on Automated Planning and Scheduling (ICAPS-2014) (2014)Google Scholar
  14. 14.
    Nilsson, M., Kvarnstrom, J., Doherty, P.: Incremental dynamic controllability in cubic worst-case time. In: Proceedings of the 21st International Symposium on Temporal Representation and Reasoning (TIME-2014) (2014)Google Scholar
  15. 15.
    Shah, J., Stedl, J., Robertson, P., Williams, B.C.: A fast incremental algorithm for maintaining dispatchability of partially controllable plans. In: Boddy, M., Fox, M., Thiébaux, S. (eds.) Proceedings of the Seventeenth International Conference on Automated Planning and Scheduling (ICAPS 2007), AAAI Press (2007)Google Scholar
  16. 16.
    Stedl, J., Williams, B.C.: A fast incremental dynamic controllability algorithm. In: Proceedings of the ICAPS Workshop on Plan Execution: A Reality Check, pp. 69–75 (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Computer Science DepartmentVassar CollegePoughkeepsieUSA

Personalised recommendations