Causality-Based Planning and Diagnostic Reasoning for Cognitive Factories. Erdem, E., Haspalamutgil, K., Patoglu, V., & Uras, T. In IEEE International Confence on Emerging Technologies on Factory Automation (ETFA 2012), 2012.
abstract   bibtex   
We propose the use of causality-based formal representation and automated reasoning methods from artificial intelligence to endow multiple teams of robots in a factory, with high-level cognitive capabilities, such as, optimal planning and diagnostic reasoning. In particular, we introduce algorithms for finding optimal decoupled plans and diagnosing the cause of a failure/discrepancy (e.g., robots may get broken or tasks may get reassigned to teams). We discuss how these algorithms can be embedded in an execution and monitoring framework effectively by allowing reusability of computed plans in case of failures, and show the applicability of these algorithms on an intelligent factory scenario.
@InProceedings{Erdem2012b,
	booktitle = {IEEE International Confence on Emerging Technologies on Factory Automation (ETFA 2012)},
	author = {Esra Erdem and Kadir Haspalamutgil and Volkan Patoglu and Tansel Uras},
	title = {Causality-Based Planning and Diagnostic Reasoning for Cognitive Factories},
	year = {2012},
	abstract = {We propose the use of causality-based formal representation and automated reasoning methods from artificial
intelligence to endow multiple teams of robots in a factory, with high-level cognitive capabilities, such as, optimal
planning and diagnostic reasoning. In particular, we introduce algorithms for finding optimal decoupled plans and diagnosing the cause of a failure/discrepancy
(e.g., robots may get broken or tasks may get reassigned to teams). We discuss how these algorithms can be embedded in an execution and monitoring framework effectively
by allowing reusability of computed plans in case of failures, and show the applicability of these algorithms on an intelligent factory scenario. }
}
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