Language Evolution and Computation Bibliography

The tasks that animals perform require a high degree of intelligence. Animals forage for food, migrate, navigate, court mates, rear offspring, defend against predators, construct nests, and so on. These tasks commonly require social interaction/cooperation and are accomplished by animal nervous systems, which are the result of billions of years of evolution and complex developmental/learning processes. The Artificial Life (AL) approach to synthesizing intelligent behavior is guided by this biological perspective. In this article we examine some of the numerous open problems in synthesizing intelligent animal behavior (especially cooperative behavior involving communication) that face the field of AL, a discipline still in its infancy.

A population of artificial organisms evolved simple communication protocols for mate finding. Female animals in our artificial environment had the ability to see males and to emit sounds. Male animals were blind, but could hear signals from females. Thus, the environment was designed to favor organisms that evolved to generate and interpret meaningful signals. Starting with random neural networks, the simulation resulted in a progression of generations that exhibit increasingly effective mate-finding strategies. In addition, a number of distinct subspecies, i.e., groups with different signaling protocols or 'dialects,' evolve and compete. These protocols become a behavioral barrier to mating that supports the formation of distinct subspecies. Experiments with physical barriers in the environment were also performed. A partially permeable barrier allows a separate subspecies to evolve and survive for indefinite periods of time, in spite of occasional migration and contact from members of other subspecies.