Abstract
Next-generation autonomous manufacturing plants create individualized products by automatically deriving manufacturing schedules from design specifications. However, because planning and scheduling are computationally hard, they must typically be done offline using a simplified system model, meaning that online observations and potential component faults cannot be considered. This leads to the problem of plan assessment: Given behavior models and current observations of the plant’s (possibly faulty) behavior, what is the probability of a partially executed manufacturing plan succeeding? In this work, we propose 1) a statistical relational behavior model for a class of manufacturing scenarios and 2) a method to derive statistical bounds on plan success probabilities for each product from confidence intervals based on sampled system behaviors. Experimental results are presented for three hypothetical yet realistic manufacturing scenarios.
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Maier, P., Sachenbacher, M., Rühr, T., Kuhn, L.: Constraint-Based Integration of Plan Tracking and Prognosis for Autonomous Production. In: Mertsching, B., Hund, M., Aziz, Z. (eds.) KI 2009. LNCS, vol. 5803, pp. 403–410. Springer, Heidelberg (2009)
Jain, D., Waldherr, S., Beetz, M.: Bayesian Logic Networks. Technical report, Technische Universität München (2009)
Clopper, C., Pearson, E.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404 (1934)
Rutten, J., Kwiatkowska, M., Norman, G., Parker, D.: Mathematical Techniques for Analyzing Concurrent and Probabilistic Systems. CRM Monograph Series, vol. 23. American Mathematical Society, Providence (2004)
Althoff, M., Stursberg, O., Buss, M.: Online Verification of Cognitive Car Decisions. In: Proc. IV 2007, pp. 728–733 (2007)
Mahtab, T., Sullivan, G., Williams, B.C.: Automated Verification of Model-Based Programs Under Uncertainty. In: Proc. ISDA 2004 (2004)
Nau, D.S., Gupta, S.K., Regli, W.C.: Manufacturing-Operation Planning versus AI Planning. In: Integrated Planning Applications: Papers from the 1995 AAAI Spring Symposium, pp. 92–101. AAAI Press, Menlo Park (1995)
Kiritsis, D., Neuendorf, K.P., Xirouchakis, P.: Petri Net Techniques for Process Planning Cost Estimation. Advances in Engineering Software 30, 375–387 (1999)
Mateescu, R., Dechter, R.: Mixed Deterministic and Probabilistic Networks. Annals of Mathematics and Artificial Intelligence 54, 3–51 (2008)
Gogate, V., Dechter, R.: SampleSearch: A Scheme that Searches for Consistent Samples. In: Proc. AISTATS 2007 (2007)
Bishop, C., et al.: 2. Probability Distributions. In: Pattern Recognition and Machine Learning, pp. 67–74. Springer, Heidelberg (2006)
Fung, R.M., Chang, K.C.: Weighting and integrating evidence for stochastic simulation in bayesian networks. In: Proc. UAI 1989, pp. 209–220. North-Holland Publishing, Amsterdam (1989)
Fung, R., Del Favero, B.: Backward Simulation in Bayesian Networks. In: Proc. UAI 1994, p. 227. Morgan Kaufmann, San Francisco (1994)
Kuhn, L., Price, B., de Kleer, J., Do, M.B., Zhou, R.: Pervasive Diagnosis: The Integration of Diagnostic Goals into Production Plans. In: Fox, D., Gomes, C.P. (eds.) Proc. AAAI 2008, pp. 1306–1312. AAAI Press, Menlo Park (2008)
de Salvo Braz, R., Amir, E., Roth, D.: Lifted First-Order Probabilistic Inference. In: IJCAI, pp. 1319–1325 (2005)
Singla, P., Domingos, P.: Lifted First-Order Belief Propagation. In: Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008)
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Maier, P., Jain, D., Waldherr, S., Sachenbacher, M. (2010). Plan Assessment for Autonomous Manufacturing as Bayesian Inference. In: Dillmann, R., Beyerer, J., Hanebeck, U.D., Schultz, T. (eds) KI 2010: Advances in Artificial Intelligence. KI 2010. Lecture Notes in Computer Science(), vol 6359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16111-7_30
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DOI: https://doi.org/10.1007/978-3-642-16111-7_30
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