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.
Knowledge-based adaptive agents for manufacturing domains
Springer, London , Regno Unito
Engineering with computers 35 (2019): 755–779. doi:10.1007/s00366-018-0630-6
info:cnr-pdr/source/autori:Borgo, Stefano; Cesta, Amedeo; Orlandini, Andrea; Umbrico, Alessandro/titolo:Knowledge-based adaptive agents for manufacturing domains/doi:10.1007/s00366-018-0630-6/rivista:Engineering with computers/anno:2019/pagina_da:755/pagi