The artificial intelligence topology that does not appear in the machine learning literature to my knowledge is that of officiated teams or round robins of them. The paradigm is a proven one in the world of sports. If the rules of the game are well designed, the result is sensational, in multiple meanings of that word.
Is anyone working on convergence in this topological space?
Does anyone want to discuss it with those considering it in my lab as a creative commons initiative?
Two teams of players engage in game play officiated by a team of officials. Team members (each a network itself in the machine learning context) collaborate to achieve a goal. The two teams collaborate to create the show of ability in goal achievement. The officials (each a network) make rulings in boundary cases. This is a network-ish way of achieving what fuzzy logic attempts to achieve.
In sports, the abilities are athletic, but that is arbitrary. The abilities could be linguistic, social and/or intellectual as in debate teams, hack-a-thons, or the competition between Google and FaceBook.
Round robin or elimination tournaments exist in sports to create events of extended duration. Seasons are simply iterations. This is, in computer science, like a batch approach, but it could be reenterant and continuous as in ML reinforcement. In this way teams of neural networks could be used in real time learning and this may be what occurs in some of the structures of mammalian brains.
Humans may have projected its own inner workings onto playing fields, and that is why sports may be so popular. Enthusiasts are, in an unconscious sense, introspecting when they intensely following sports.