HIERARCHICAL ASSOCIATIVE MEMORY MODEL FOR ARTIFICIAL GENERAL-PURPOSE COGNITIVE AGENTS

Abstract

This paper presents a model of hierarchical associative memory, which can be used as a basis for building artificial cognitive agents of general purpose. With the help of this model, one of the most important problems of modern machine learning and artificial intelligence in general can be solved - the ability for a cognitive agent to use "life experience" to process the context of the situation in which he was, is and, possibly, will be. This model is applicable for artificial cognitive agents functioning both in specially designed virtual worlds and in objective reality. The use of hierarchical associative memory as a long-term memory of artificial cognitive agents will allow the latter to effectively navigate both in the general knowledge accumulated by mankind and in their life experience. The novelty of the presented work is based on the author's approach to the construction of contextdependent artificial cognitive agents using an interdisciplinary approach, in particular, based on the achievements of artificial intelligence, cognitology, neurophysiology, psychology and sociology. The relevance of this work is based on the keen interest of the scientific community and the high social demand for the creation of general-level artificial intelligence systems. Associative hierarchical memory, based on the use of an approach similar to the hypercolumns of the human cerebral cortex, is becoming one of the important components of an artificial intelligent agent of the general level. The article will be of interest to all researchers working in the field of building artificial cognitive agents and related fields.