Language Evolution and Computation Bibliography

Artificial Life models have consistently implemented communication as an exchange of signals over dedicated and functionally isolated channels. I argue that such a feature prevents models from providing a satisfactory account of the origins of communication and present a model in which there are no dedicated channels. Agents controlled by neural networks and equipped with proximity sensors and wheels are presented with a co-ordinated movement task. It is observed that functional, but non-communicative, behaviours which evolve in the early stages of the simulation both make possible, and form the basis of, the communicative behaviour which subsequently evolves.

This paper investigates the development of experience-based meaning creation and explores the problem of establishing successful communication systems in a population of agents. The aim of the work is to investigate how such systems can develop, without reliance on phe- nomena not found in actual human language learning, such as the explicit transmission of meaning or the provision of reliable error feedback to guide learning. Agents develop individual, distinct meaning structures, and although they can communicate despite this, communicative success is closely related to the proportion of shared lexicalised meaning, and the communicative systems have a large degree of redundant synonymy.

Turkel [16] studies a computational model in which agents try to establish communication. It is observed that over the course of evolution, initial plasticity is significantly nativised. This result supports the idea that innate language knowledge is explained by the Baldwin effect [2][14]. A more biologically plausible computational model, however, reveals the result is unsatisfactory. Implications of this new representation system in language evolution are discussed with a consideration of the Baldwin effect.

Recent work in the field of computational evolutionary linguistics suggests that the dynamics arising from the cultural evolution of language can explain the emergence of syntactic structure. We build on this work by introducing a model of language acquisition based on the Minimum Description Length Principle. Our experiments show that compositional syntax is most likely to occur under two conditions specific to hominids: (i) A complex meaning space structure, and (ii) the poverty of the stimulus.

Oliphant [5,6] contends that language is the only naturally-occurring, learned symbolic communication system, because only humans can accurately observe meaning during the cultural transmission of communication. This paper outlines several objections to Oliphant's argument. In particular, it is argued that the learning biases necessary to support learned symbolic communication may not be common and that the speed of cultural convergence during cultural evolution of communication may be a key factor in the evolution of such learning biases.

In this paper we explore the similarities between a mathematical model of language evolution and several A-life simulations. We argue that the mathematical model makes some problematic simplifications, but that a combination with computational models can help to adapt and extend existing language evolution scenario's.