Towards VEsNA, a Framework for Managing Virtual Environments via Natural Language Agents

Andrea Gatti
(University of Genova)
Viviana Mascardi
(University of Genova)

Automating a factory where robots are involved is neither trivial nor cheap. Engineering the factory automation process in such a way that return of interest is maximized and risk for workers and equipment is minimized, is hence of paramount importance. Simulation can be a game changer in this scenario but requires advanced programming skills that domain experts and industrial designers might not have. In this paper we present the preliminary design and implementation of a general-purpose framework for creating and exploiting Virtual Environments via Natural language Agents (VEsNA). VEsNA takes advantage of agent-based technologies and natural language processing to enhance the design of virtual environments. The natural language input provided to VEsNA is understood by a chatbot and passed to a cognitive intelligent agent that implements the logic behind displacing objects in the virtual environment. In the VEsNA vision, the intelligent agent will be able to reason on this displacement and on its compliance to legal and normative constraints. It will also be able to implement what-if analysis and case-based reasoning. Objects populating the virtual environment will include active objects and will populate a dynamic simulation whose outcomes will be interpreted by the cognitive agent; explanations and suggestions will be passed back to the user by the chatbot.

In Rafael C. Cardoso, Angelo Ferrando, Fabio Papacchini, Mehrnoosh Askarpour and Louise A. Dennis: Proceedings of the Second Workshop on Agents and Robots for reliable Engineered Autonomy (AREA 2022), Vienna, Austria, 24th July 2022, Electronic Proceedings in Theoretical Computer Science 362, pp. 65–80.
Published: 20th July 2022.

ArXived at: http://dx.doi.org/10.4204/EPTCS.362.8 bibtex PDF
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