Abstract
Smart factories are on the verge of becoming the new industrial paradigm, wherein optimization permeates all aspects of production, from concept generation to sales. To fully pursue this paradigm, flexibility in the production means as well as in their timely organization is of paramount importance. AI planning can play a major role in this transition, but the scenarios encountered in practice might be challenging for current tools. We explore the use of SMT at the core of planning techniques to deal with real-world scenarios in the emerging smart factory paradigm. We present special-purpose and general-purpose algorithms, based on current automated reasoning technology and designed to tackle complex application domains. We evaluate their effectiveness and respective merits on a logistic scenario, also extending the comparison to other state-of-the-art task planners.
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Notes
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RCLLPlan exploits a simple cost definition in its current state, i.e., minimize time to delivery for each product. However, richer goal structures could be specified.
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Acknowledgements
The research of Arthur Bit-Monnot and Luca Pulina has been funded by the EU Commission’s H2020 Program under grant agreement N.732105 (CERBERO project). The research of Luca Pulina has been also partially funded by the Sardinian Regional Project PROSSIMO (POR FESR 2014/20-ASSE I) and the FitOptiVis (ID: 783162) project.
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Bit-Monnot, A., Leofante, F., Pulina, L., Tacchella, A. (2019). SMT-based Planning for Robots in Smart Factories. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_58
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