Abstract
Robots are increasingly used to perform a wide variety of tasks, especially those involving dangerous or inaccessible locations. As the complexity of such tasks grow, robots are being deployed in teams, with complex coordination schemes aimed at maximizing the chance of mission success. Such teams operate under inherently uncertain conditions – the robots themselves fail, and have to continuously adapt to changing environmental conditions. A key challenge facing robotic mission designers is therefore to construct a mission – i.e., specify number and type of robots, number and size of teams, coordination and planning mechanisms etc. – so as to maximize some overall utility, such as the probability of mission success. In this paper, we advocate, formalize, and empirically justify an approach to compute quantitative utility of robotic missions using probabilistic model checking. We show how to express a robotic demining mission as a restricted type of discrete time Markov chain (called α PA), and its utility as either a linear temporal logic formula or a reward. We prove a set of compositionality theorems that enable us to compute the utility of a system composed of several α PA s by combining the utilities of each α PA in isolation. This ameliorates the statespace explosion problem, even when the system being verified is composed of a large number of robots. We validate our approach empirically, using the probabilistic model checker prism.
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References
Baier, C.: On algorithmic verification methods for probabilistic systems. PhD thesis, University of Mannheim, Habilitation thesis (1998)
Chaki, S., Dolan, J.M., Giampapa, J.A.: Toward A Quantitative Method for Assuring Coordinated Autonomy. In: Proc. of ARMS Workshop (to appear, 2013)
Chen, T., Diciolla, M., Kwiatkowska, M.Z., Mereacre, A.: Quantitative Verification of Implantable Cardiac Pacemakers. In: Proc. of RTSS (2012)
de Alfaro, L., Henzinger, T.A., Jhala, R.: Compositional Methods for Probabilistic Systems. In: Larsen, K.G., Nielsen, M. (eds.) CONCUR 2001. LNCS, vol. 2154, pp. 351–365. Springer, Heidelberg (2001)
Feng, L., Han, T., Kwiatkowska, M., Parker, D.: Learning-Based Compositional Verification for Synchronous Probabilistic Systems. In: Bultan, T., Hsiung, P.-A. (eds.) ATVA 2011. LNCS, vol. 6996, pp. 511–521. Springer, Heidelberg (2011)
Feng, L., Kwiatkowska, M.Z., Parker, D.: Compositional Verification of Probabilistic Systems Using Learning. In: Proc. of QEST (2010)
Feng, L., Kwiatkowska, M., Parker, D.: Automated Learning of Probabilistic Assumptions for Compositional Reasoning. In: Giannakopoulou, D., Orejas, F. (eds.) FASE 2011. LNCS, vol. 6603, pp. 2–17. Springer, Heidelberg (2011)
Heath, J., Kwiatkowska, M.Z., Norman, G., Parker, D., Tymchyshyn, O.: Probabilistic model checking of complex biological pathways. Theoretical Computer Science (TCS) 391(3) (2008)
Komuravelli, A., Păsăreanu, C.S., Clarke, E.M.: Assume-Guarantee Abstraction Refinement for Probabilistic Systems. In: Madhusudan, P., Seshia, S.A. (eds.) CAV 2012. LNCS, vol. 7358, pp. 310–326. Springer, Heidelberg (2012)
Kumar, J.A., Vasudevan, S.: Automatic Compositional Reasoning for Probabilistic Model Checking of Hardware Designs. In: Proc. of QEST (2010)
Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: Verification of Probabilistic Real-Time Systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 585–591. Springer, Heidelberg (2011)
Kwiatkowska, M., Norman, G., Parker, D., Qu, H.: Assume-Guarantee Verification for Probabilistic Systems. In: Esparza, J., Majumdar, R. (eds.) TACAS 2010. LNCS, vol. 6015, pp. 23–37. Springer, Heidelberg (2010)
Kwiatkowska, M.Z., Norman, G., Sproston, J.: Probabilistic Model Checking of Deadline Properties in the IEEE 1394 FireWire Root Contention Protocol. Formal Aspects of Computing (FACJ) 14(3) (2003)
Segala, R.: Modeling and Verification of Randomized Distributed Real-Time Systems. PhD thesis, Massachusetts Institute of Technology. Available as Technical Report MIT/LCS/TR-676 (1995)
Stoelinga, M.: Alea jacta est: verification of probabilistic, real-time and parametric systems. PhD thesis, University of Nijmegen (2002), Available via http://www.soe.ucsc.edu/~marielle
Sukthankar, G., Sycara, K.: Team-aware Robotic Demining Agents for Military Simulation, http://www.cs.cmu.edu/~softagents/iaai00/iaai00.html
Vardi, M.Y.: Automatic Verification of Probabilistic Concurrent Finite-State Programs. In: Proc. of FOCS (1985)
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Chaki, S., Giampapa, J.A. (2013). Probabilistic Verification of Coordinated Multi-robot Missions. In: Bartocci, E., Ramakrishnan, C.R. (eds) Model Checking Software. SPIN 2013. Lecture Notes in Computer Science, vol 7976. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39176-7_9
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DOI: https://doi.org/10.1007/978-3-642-39176-7_9
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