Plan assessment for autonomous manufacturing as Bayesian inference

Authors:
Paul Maier;Dominik Jain;Stefan Waldherr;Martin Sachenbacher
Affiliations:
Technische Universität München, Department of Informatics, Garching, Germany;Technische Universität München, Department of Informatics, Garching, Germany;Technische Universität München, Department of Informatics, Garching, Germany;Technische Universität München, Department of Informatics, Garching, Germany
Venue:
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Year:
2010

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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.