For the purposes of this question, a technique is considered adversarial if it develops two systems (or two kinds of system), the first trying to process certain kinds of data well, and the second trying to provide the first with hard-to-process data. Examples include:
- Adversarial machine learning, in which the processor tries to identify security risks, while the agent trying to fool it seeks to bypass security checks;
- Generative adversarial networks, in which the processor is discriminative (i.e. it models a conditional probability distribution) and the agent trying to fool it is generative (i.e. it models a joint probability distribution).
Judging by the dates discussed in the above links, AML dates to the early 2000s while GANs date to 2014. These both seem very recent, and I suspect the general meaning of "adversarial" I specify above has been used for much longer. Clearly, the idea of requiring continual adaptation by both agents to shifting goalposts need not be tied to such specific examples as security or Bayesian models. Any simulation of two competing species evolving, e.g. a predator and its prey, will be "adversarial" in the sense of this question.
So my question is: when was some kind of adversarial technique first used, in the hope it would lead to machines learning to solve problems more effectively than would happen without adversarial techniques? (A simulation that illustrates how biological evolution works, without wanting to solve a specific data-driven problem of human interest, would not qualify.)