Language Evolution and Computation Bibliography

Previous simulation work on the evolution of communication has not shown how a large signal repertoire could emerge in situated agents. We present an artificial life simulation of agents, situated in a two-dimensional world, that must search for other agents with whom they can trade resources. With strong restrictions on which resources can be traded for others, initially non-communicating agents evolve/learn a signal system that describes the resource they seek and the resource they are willing to offer in return. A large signal repertoire emerges mainly through an evolutionary process. Agents whose production and comprehension abilities rely on a single mechanism fare best, although learning enables agents with separate mechanisms to achieve some measure of success. These results demonstrate that substantial signaling repertoires can evolve in situated multi-agent systems, and suggest that simulated social interactions such as trading may provide a useful context for further computational studies of the evolution of communication.

This article reviews recent progress made by computational studies investigating the emergence, via learning or evolutionary mechanisms, of communication among a collection of agents. This work spans issues related to animal communication and the origins and evolution of language. The studies reviewed show how population size, spatial constraints on agent interactions, and the tasks involved can all influence the nature of the communication systems and the ease with which they are learned and/or evolved. Although progress in this area has been substantial, we are able to identify some important areas for future research in the evolution of language, including the need for further computational investigation of key aspects of language such as open vocabulary and the more complex aspects of syntax.

How does a group of individuals who lack a shared communication system evolve to achieve a consensus, so that every member of the group uses each signal in a manner consistent with others in the group? There are many factors that affect the difficulty of this task, including the number of signals available, the number of meanings or situations to convey, the population size, and whether or not any learning occurs. Each of these factors is explored in simulations which use a genetic algorithm that selects for agents who communicate meanings effectively with other agents. The difficulty of gaining consensus among a population of signalers increases as the number of meanings (and signals) increases, but decreases if more signals than meanings are allowed. Surprisingly, difficulty decreases as population size increases. An analysis is made of the exponentially increasing difficulty of achieving consensus as the number of meanings and signals grows. The implications for the evolution of communication are discussed.

Using communication is not the only cooperative strategy that can evolve when organisms need to solve a problem together. This article describes a model that extends MacLennan and BurghardtOs [37] synthetic ethology simulation to show that using a spatial world in a simulation allows a wider range of strategies to evolve in response to environmental demands. The model specifically explores the interaction between population density and resource abundance and their effect on the kinds of cooperative strategies that evolve. Signaling strategies evolve except when population density is high or resource abundance is low.