NEUROCOGNITIVE METHODS AND ALGORITHMS OF FEDERATED LEARNING OF INTELLIGENT INTEGRATED INFORMATION MANAGEMENT SYSTEMS IN A REAL COMMUNICATIVE ENVIRONMENT

Abstract

Unlike existing methods of teaching artificial intelligence systems, approaches based on federated learning will not require a long and expensive procedure for preparing a training sample when creating and mass practical application of "smart" agricultural systems, autonomous unmanned agricultural machines and robots, and the knowledge obtained by the decision-making system will be updated on an ongoing basis. The aim of the research is to develop and implement end-to-end artificial intelligence technology, the lack of which today prevents the creation of integrated information management systems for crop and livestock production ("smart" agricultural systems) based on the group application of unmanned ground and aerial agricultural machines and robots. The introduction of such intelligent systems is necessary to preserve and improve the products produced and ensure the sustainable development of agriculture. The article describes neurocognitive methods and algorithms of federated learning of intelligent agricultural process management systems in a real environment. The structure of data and knowledge exchange in the smart field system based on a distributed network of intelligent agents managing smart field systems on various agricultural lands, based on federated learning, is also proposed. Each intelligent agent is a software model of the neurocognitive processes of reasoning and decision-making within the framework of solving a specific task. The proposed structure will facilitate the joint accumulation of a knowledge base in the field of agriculture and will be able to become the basis for many different intelligent agents that effectively perform specific tasks within a distributed network of smart field management systems. There is also a description of intelligent agents performing various tasks in a real environment. Examples of autonomous robotic and software complexes being developed are given, on the basis of which it is planned to test the proposed concept of federated training of "smart" field systems. At the same time, the article describes the expected effects of the introduction of technologies based on the developed methods and algorithms of federated training of intelligent agents controlling smart field systems.