Collaborative dialogue system for plant simulation based on neuropsychological agents of universal artificial intelligence

The relevance of this work stems from the need to enhance the productivity, manageability, and efficiency of plant breeding and cultivation processes by creating predictive models. A general architecture for plant simulation systems based on universal artificial intelligence agents is developed. The feasibility of using a design metaphor for decentralized collaborative dialog systems based on universal artificial intelligence agents for developing such systems is substantiated. Generalized multi-agent training algorithms are developed for controlling neurocognitive architectures of artificial intelligence agents in plant simulation models; these algorithms are based on knowledge extracted from texts and natural language utterances, as well as the implementation of exploratory behavior by autonomous mobile robots in a real environment. Aim. The study is to develop a methodology for creating simulation models of plants based on dialogue agents of universal artificial intelligence. Research methods. The possibility of using a design metaphor for decentralized collaborative dialogue systems based on universal artificial intelligence agents to develop such systems is substantiated. Results. Fundamental principles for constructing open-source plant simulation models with high expressiveness and predictive power have been developed based on neuropsychological agents from universal artificial intelligence. Conclusion. A general architecture for plant simulation systems has been developed created on universal artificial intelligence agents and autonomous mobile robots.