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Dynamic Controllability Made Simple

Authors Massimo Cairo, Romeo Rizzi



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Massimo Cairo
Romeo Rizzi

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Massimo Cairo and Romeo Rizzi. Dynamic Controllability Made Simple. In 24th International Symposium on Temporal Representation and Reasoning (TIME 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 90, pp. 8:1-8:16, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2017)
https://doi.org/10.4230/LIPIcs.TIME.2017.8

Abstract

Simple Temporal Networks with Uncertainty (STNUs) are a well-studied model for representing temporal constraints, where some intervals (contingent links) have an unknown but bounded duration, discovered only during execution. An STNU is dynamically controllable (DC) if there exists a strategy to execute its time-points satisfying all the constraints, regardless of the actual duration of contingent links revealed during execution. In this work we present a new system of constraint propagation rules for STNUs, which is sound-and-complete for DC checking. Our system comprises just three rules which, differently from the ones proposed in all previous works, only generate unconditioned constraints. In particular, after applying our sound rules, the network remains an STNU in all respects. Moreover, our completeness proof is short and non-algorithmic, based on the explicit construction of a valid execution strategy. This is a substantial simplification of the theory which underlies all the polynomial-time algorithms for DC-checking. Our analysis also shows: (1) the existence of late execution strategies for STNUs, (2) the equivalence of several variants of the notion of DC, (3) the existence of a fast algorithm for real-time execution of STNUs, which runs in O(KN) total time in a network with K contingent links and N time points, considerably improving the previous O(N^3)-time bound.
Keywords
  • Simple Temporal Network with Uncertainty
  • Dynamic controllability

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References

  1. Carlo Combi, Luke Hunsberger, and Roberto Posenato. An algorithm for checking the dynamic controllability of a conditional simple temporal network with uncertainty. In Joaquim Filipe and Ana L. N. Fred, editors, ICAART 2013 - Proceedings of the 5th International Conference on Agents and Artificial Intelligence, Volume 2, Barcelona, Spain, 15-18 February, 2013, pages 144-156. SciTePress, 2013. Google Scholar
  2. Patrick R. Conrad and Brian Charles Williams. Drake: An efficient executive for temporal plans with choice. CoRR, abs/1401.4606, 2014. URL: http://arxiv.org/abs/1401.4606.
  3. Robert T. Effinger, Brian Charles Williams, Gerard Kelly, and Michael Sheehy. Dynamic controllability of temporally-flexible reactive programs. In Alfonso Gerevini, Adele E. Howe, Amedeo Cesta, and Ioannis Refanidis, editors, Proceedings of the 19th International Conference on Automated Planning and Scheduling, ICAPS 2009, Thessaloniki, Greece, September 19-23, 2009. AAAI, 2009. URL: http://aaai.org/ocs/index.php/ICAPS/ICAPS09/paper/view/739.
  4. Luke Hunsberger. Fixing the semantics for dynamic controllability and providing a more practical characterization of dynamic execution strategies. In Carsten Lutz and Jean-François Raskin, editors, TIME 2009, 16th International Symposium on Temporal Representation and Reasoning, Bressanone-Brixen, Italy, 23-25 July 2009, Proceedings, pages 155-162. IEEE Computer Society, 2009. URL: http://dx.doi.org/10.1109/TIME.2009.25.
  5. Luke Hunsberger. Efficient execution of dynamically controllable simple temporal networks with uncertainty. Acta Inf., 53(2):89-147, 2016. URL: http://dx.doi.org/10.1007/s00236-015-0227-0.
  6. Lina Khatib, Paul H. Morris, Robert A. Morris, and Francesca Rossi. Temporal constraint reasoning with preferences. In Bernhard Nebel, editor, Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI 2001, Seattle, Washington, USA, August 4-10, 2001, pages 322-327. Morgan Kaufmann, 2001. Google Scholar
  7. Paul Morris. A structural characterization of temporal dynamic controllability. In Frédéric Benhamou, editor, Principles and Practice of Constraint Programming - CP 2006, 12th International Conference, CP 2006, Nantes, France, September 25-29, 2006, Proceedings, volume 4204 of Lecture Notes in Computer Science, pages 375-389. Springer, 2006. URL: http://dx.doi.org/10.1007/11889205_28.
  8. Paul Morris. Dynamic controllability and dispatchability relationships. In Helmut Simonis, editor, Integration of AI and OR Techniques in Constraint Programming - 11th International Conference, CPAIOR 2014, Cork, Ireland, May 19-23, 2014. Proceedings, volume 8451 of Lecture Notes in Computer Science, pages 464-479. Springer, 2014. URL: http://dx.doi.org/10.1007/978-3-319-07046-9_33.
  9. Paul H. Morris and Nicola Muscettola. Temporal dynamic controllability revisited. In Manuela M. Veloso and Subbarao Kambhampati, editors, Proceedings, The Twentieth National Conference on Artificial Intelligence and the Seventeenth Innovative Applications of Artificial Intelligence Conference, July 9-13, 2005, Pittsburgh, Pennsylvania, USA, pages 1193-1198. AAAI Press / The MIT Press, 2005. URL: http://www.aaai.org/Library/AAAI/2005/aaai05-189.php.
  10. Paul H. Morris, Nicola Muscettola, and Thierry Vidal. Dynamic control of plans with temporal uncertainty. In Bernhard Nebel, editor, Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI 2001, Seattle, Washington, USA, August 4-10, 2001, pages 494-502. Morgan Kaufmann, 2001. Google Scholar
  11. Mikael Nilsson, Jonas Kvarnström, and Patrick Doherty. Incremental dynamic controllability revisited. In Daniel Borrajo, Subbarao Kambhampati, Angelo Oddi, and Simone Fratini, editors, Proceedings of the Twenty-Third International Conference on Automated Planning and Scheduling, ICAPS 2013, Rome, Italy, June 10-14, 2013. AAAI, 2013. URL: http://www.aaai.org/ocs/index.php/ICAPS/ICAPS13/paper/view/6028.
  12. Mikael Nilsson, Jonas Kvarnström, and Patrick Doherty. EfficientIDC: A faster incremental dynamic controllability algorithm. In Steve A. Chien, Minh Binh Do, Alan Fern, and Wheeler Ruml, editors, Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling, ICAPS 2014, Portsmouth, New Hampshire, USA, June 21-26, 2014. AAAI, 2014. URL: http://www.aaai.org/ocs/index.php/ICAPS/ICAPS14/paper/view/7936.
  13. Mikael Nilsson, Jonas Kvarnström, and Patrick Doherty. Incremental dynamic controllability in cubic worst-case time. In Amedeo Cesta, Carlo Combi, and François Laroussinie, editors, 21st International Symposium on Temporal Representation and Reasoning, TIME 2014, Verona, Italy, September 8-10, 2014, pages 17-26. IEEE Computer Society, 2014. URL: http://dx.doi.org/10.1109/TIME.2014.13.
  14. Francesca Rossi, Kristen Brent Venable, and Neil Yorke-Smith. Uncertainty in soft temporal constraint problems: A general framework and controllability algorithms forthe fuzzy case. J. Artif. Intell. Res. (JAIR), 27:617-674, 2006. URL: http://dx.doi.org/10.1613/jair.2135.
  15. Julie A. Shah, John Stedl, Brian Charles Williams, and Paul Robertson. A fast incremental algorithm for maintaining dispatchability of partially controllable plans. In Mark S. Boddy, Maria Fox, and Sylvie Thiébaux, editors, Proceedings of the Seventeenth International Conference on Automated Planning and Scheduling, ICAPS 2007, Providence, Rhode Island, USA, September 22-26, 2007, pages 296-303. AAAI, 2007. URL: http://www.aaai.org/Library/ICAPS/2007/icaps07-038.php.
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