R&D is going on to train Combinatorial AI to
We look for application-oriented strategic partners interested to train Combinatorial AI in a specific application domain.
There’re three facets of knowledge to be maintained in a development project:
A newer problem understanding usually requires new concepts, which leads to development of languages used for formal definitions of the problems. This part of work is the field for humans.
Languages for solutions are often fixed in a project and give little or no room for development. These languages are usually learned “as is”, without questions on how they can be changed. This kind of coding is the right role for the combinatorial agent.
Coding solutions can be shifted to Combinatorial AI.
Problems are usually defined using descriptive languages, which can be extentions of XML or other languages adopted for the development needs in your team. (Note that many data file formats of modern applications are XML-based).
There is no difference for the agent between training on model problems and solving real problems. The agent can learn an additional concept or language on model problems first and use this knowledge on the next iteration of develpment.
As development continues, the language for problem definitions can be developed along with your understanding of the problems.