Combinatorial Approach to Research and Learning
Combinatorial AI is an approach to building machines able to participate in research and development along with humans. A combinatorial agent proposes new models and tries to prove them on data when learning models under supervision or when doing something new and challenging. It starts from simple elementary models and combines them to more complex, learning which models to combine, what for, and how.
Agent’s learning has two sides, the first is about reading the structured problem descriptions, and the second is about building solutions. If trained steadily step by step, the agent might learn properties of individual items as well as systems build from them, follow dependencies, and use formal languages to share knowledge with others. All these activities are manifestations of understanding, which is a feature of human thinking and a key for developing strong AI.
Combinatorial AI is similar to a child, as it needs to learn how to build complex systems from the very basics. It may need to learn math, physics, material properties, chemistry, and even common sense before it builds something really useful. This training scheme is not less important than AI itself.