Language Evolution and Computation Bibliography

The complexity, variation, and change of languages make evident the importance of representation and learning in the acquisition and evolution of language. For example, analytic studies of simple language in unstructured populations have shown complex dynamics, depending on the fidelity of language transmission. In this study we extend these analysis of evolutionary dynamics to include grammars inspired by the principles and parameters paradigm. In particular, the space of languages is structured so that some pairs of languages are more similar than others, and mutations tend to change languages to nearby variants. We found that coherence emerges with lower learning fidelity than predicted by earlier work with an unstructured language space.

The question of language evolution is of interest to linguistics, biology and recently, engineering communicating networks. Previous work on these problems has focused mostly on a fully-connected population. We are extending this study to structured populations, which are generally more realistic and offer rich opportunities for linguistic diversification. Our work focuses on the convergence properties of a spatially structured population of learners acquiring a language from one another. We investigate several metrics, including mean language coherence and the critical learning fidelity threshold.

In an effort to better guide research into self-configuring wireless sensor networks, we discuss a technical definition of the term self-organization. We define a self-organizing system as one where a collection of units coordinate with each other to form a system that adapts to achieve a goal more efficiently. We then lay out some conditions that must hold for a system to meet this definition and discuss some examples of self-organizing systems. Finally, we explore some of the ways this definition applies to wireless sensor networks.

This paper describes a framework for studies of the adaptive acquisition and evolution of language, with the following components: language learning begins by associating words with cognitively salient representations (``grounding''); the sentences of each language are determined by properties of lexical items, and so only these need to be transmitted by learning; the learnable languages allow multiple agreements, multiple crossing agreements, and reduplication, as mildly context sensitive and human languages do; infinitely many different languages are learnable; many of the learnable languages include infinitely many sentences; in each language, inferential processes can be defined over succinct representations of the derivations themselves; the languages can be extended by innovative responses to communicative demands. Preliminary analytic results and a robotic implementation are described.

We investigate common design decisions for constructing a computational genetic language in an autoadaptive system. Such languages must support self-replication and are typically Turing-complete so as not to limit the types of computations they can perform. We examine the importance of using templates to denote locations in the genome, the methods by which those templates are located (direct-matching versus complementmatching), methods used in the calculation of genome length and the size and complexity of the language. For each test, we examine the effects on the rate of evolution of the populations and isolate those factors that contribute to it, most notably the organisms' ability to withstand mutations.