The Internet as a Virtual Ecology: Coevolutionary Arms Races Between Human and Artificial Populations

In this paper, we propose that learning complex behaviors can be achieved in a coevolutionary environment where one population consists of the human users of an interactive adaptive software tool and the “opposing” population is artificial, generated by a coevolutionary learning engine. We take advantage of the Internet, a connected community where people and software coexist. A new kind of adaptive agent can exploit its interactions with thousands of users—inside a virtual “niche”—to learn in a coevolutionary human-robot arms race. Our model is Tron, a simple dynamic game where introspective self-play quickly leads to collusive stagnation. We describe an application where thousands of small programs are sent to play with people through the Java interpreter running in their web browsers. The feedback provided by these agents is collected in our server and used to augment an ever improving fitness landscape for local robot-robot games. Speciation and fitness sharing provide diversity to challenge humans with a variety of different strategies. In this way, we obtain an evolving environment where human as well as artificial adaptation are simultaneously taking place.