Synergetic Learning Systems: Concept, Architecture, and Algorithms
This work addresses the challenge of developing artificial general intelligence by introducing a novel system architecture, though it appears incremental as it builds on existing ideas of evolution and multi-agent systems.
The authors propose Synergetic Learning Systems, an AI architecture inspired by natural evolution and self-organization, aiming to achieve intelligent processing through cooperative/competitive learning among subsystems, with the goal of eventually reaching artificial general intelligence through long-term coevolution.
Drawing on the idea that brain development is a Darwinian process of ``evolution + selection'' and the idea that the current state is a local equilibrium state of many bodies with self-organization and evolution processes driven by the temperature and gravity in our universe, in this work, we describe an artificial intelligence system called the ``Synergetic Learning Systems''. The system is composed of two or more subsystems (models, agents or virtual bodies), and it is an open complex giant system. Inspired by natural intelligence, the system achieves intelligent information processing and decision-making in a given environment through cooperative/competitive synergetic learning. The intelligence evolved by the natural law of ``it is not the strongest of the species that survives, but the one most responsive to change,'' while an artificial intelligence system should adopt the law of ``human selection'' in the evolution process. Therefore, we expect that the proposed system architecture can also be adapted in human-machine synergy or multi-agent synergetic systems. It is also expected that under our design criteria, the proposed system will eventually achieve artificial general intelligence through long term coevolution.