Yamazaki, Seiya, Hiroyuki Iizuka, and Masahito Yamamoto. “Complexity of bird song caused by adversarial imitation learning.” Artificial life and robotics 25, no. 1 (2020): 124-132.
Biological evolution produces complexity through genetic variations based on randomness. In conventional communication or language simulation models, genetic variations based on randomness and fitness function rewarding task achievements play an important role in evolving communication signals to complex ones. However, it is known that not only genetic variations evolve communication but also imitative learning during developmental processes contributes to the evolution of communication. What we investigated here was to find a different principle of generating complexity which does not rely on the randomness or external environmental complexity but only on the learning processes in communication. Our hypothesis is that the contradictory learning mechanism we call the adversarial imitation learning can work to increase the complexity without relying on the random processes. To investigate our hypothesis, we implemented the adversarial imitation learning on a simulation where two agents interact with and imitate each other. Our results showed that the adversarial imitation learning causes chaotic dynamics and investigating the learning results in different types of interaction between the two; it was clarified that the adversarial imitation learning is necessary for the emergence of the chaotic time series.