Cognitive Architecture for Decision-Making Based on Brain Principles Programming
This proposes a novel cognitive architecture for AI decision-making, but appears incremental as it builds on existing brain-inspired principles without demonstrated performance gains.
The authors tackled the problem of creating a general-purpose cognitive architecture by implementing five brain activity principles across three subsystems, resulting in a task-driven framework with basic and domain-specific ontologies for practical applications.
We describe a cognitive architecture intended to solve a wide range of problems based on the five identified principles of brain activity, with their implementation in three subsystems: logical-probabilistic inference, probabilistic formal concepts, and functional systems theory. Building an architecture involves the implementation of a task-driven approach that allows defining the target functions of applied applications as tasks formulated in terms of the operating environment corresponding to the task, expressed in the applied ontology. We provide a basic ontology for a number of practical applications as well as for the subject domain ontologies based upon it, describe the proposed architecture, and give possible examples of the execution of these applications in this architecture.