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Knowledge-based adaptive agents for manufacturing domains

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Abstract

Modern production systems are increasingly using artificial agents (e.g., robots) of different kinds. Ideally, these agents should be able to recognize the state of the world, to act optimizing their work toward the achievement of a set of goals, to change the plan of action when problems arise, and to collaborate with other artificial and human agents. The development of such an ideal agent presents several challenges. We concentrate on two of them: the construction of a single and coherent knowledge base which includes different types of knowledge with which to understand and reason on the state of the world in a human-like way; and the isolation of types of contexts that the agent can exploit to make sense of the actual situation from a perspective and to interact accordingly with humans. We show how to build such a knowledge base (KB) and how it can be updated as time passes. The KB we propose is based on a foundational ontology, is cognitively inspired, and includes a notion of context to discriminate information. The KB has been partially implemented to test the use and suitability of the knowledge representation for the agent’s control model via a temporal planning and execution system. Some experimental results showing the feasibility of our approach are reported.

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Notes

  1. The existence of quality a is enforced by formula (1) and the theory in [11]. The characterization of the space of behaviors is still under investigation.

  2. The location is fixed for robots such as robotic arms, it is parametric (in particular, it may depend on the task) for mobile robots.

  3. In general, it suffices that the components have connected working areas. E.g. in a robot with a robotic arm and a container, the locations of the arm and the container are disconnected but the arm must be able to reach objects in the container to implement a Channel function, so the working areas must be connected.

  4. http://protege.stanford.edu.

  5. http://jena.apache.org.

  6. All the experiments have been performed on a workstation endowed with an Intel Core2 Duo 2.26 GHz and 8 GB RAM.

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Acknowledgements

CNR authors are supported by MIUR/CNR within the GECKO Project - Progetto Bandiera “La Fabbrica del Futuro”.

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Authors and Affiliations

  1. LOA - Laboratory for Applied Ontology, CNR - Italian National Research Council, ISTC - Institute for Cognitive Sciences and Technologies, Trento, Italy

    Stefano Borgo

  2. PST Lab - Laboratory for Planning and Scheduling Technology, CNR - Italian National Research Council, ISTC - Institute for Cognitive Sciences and Technologies, Rome, Italy

    Amedeo Cesta, Andrea Orlandini & Alessandro Umbrico

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  1. Stefano Borgo
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  2. Amedeo Cesta
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  4. Alessandro Umbrico
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Correspondence to Stefano Borgo.

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Borgo, S., Cesta, A., Orlandini, A. et al. Knowledge-based adaptive agents for manufacturing domains. Engineering with Computers 35, 755–779 (2019). https://doi.org/10.1007/s00366-018-0630-6

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