Formation of Motivated Adaptive Erudite AGI Twin with Reflexive Multimodal Ontology by Ensembles of Intelligent Agents

Abstract

The development of artificial intelligence and ensembles of intelligent agents has led to the formation of a motivated, adaptive and erudite AGI twin with a reflexive multimodal ontology. Formation of a motivated adaptive intelligent multimodal digital twin with reflexive erudition and ontology based on ensembles of intelligent agents combines several key technologies and methods for creating highly effective systems for modeling and simulating real objects or processes. Motivation allows creating a digital twin that is capable of not only accurately reproducing the characteristics of the original object or system, but also independently determining goals, motives and interaction strategy, which ensures its adaptability to changing conditions and tasks. Multimodal use of various types of data and sensory channels (visual, auditory, tactile, etc.) allows the twin to perceive and process information in a variety of formats, increasing the accuracy and completeness of results. Creating a digital twin from specialized agents interacting with each other and uniting into ensembles to solve complex problems allows distributing functions, increasing flexibility and its scalability. Providing it with reflection, analysis of its own decisions and behavior, as well as erudition for accumulation and use of knowledge improves and expands the scope of activity and learning from experience. The ontology of knowledge, describing the entities, properties and relationships of objects, as well as practical skills, promotes compatibility and expandability of activity with people. Practical implementation includes, firstly, the development of the architecture of multimodal data and algorithms for their processing, secondly, the creation and training of agent ensembles using machine learning methods and neural networks, thirdly, the introduction of reflection and self-learning mechanisms to increase motivation and adaptability of the system, fourthly, the formalization of ontologies for structuring knowledge and integrating skills with other systems. The information approach finds application in robotics, virtual assistants, monitoring and control systems, as well as in modeling complex dynamic systems where a high degree of flexibility and AGI intelligence is required.