Language Evolution and Computation Bibliography

We present a series of studies investigating the formation, generative power, and evolution of toponyms (i.e. topographic names). The domain chosen for this project is the spatial concepts related to movement through the environment, one of the key sets of concepts to be grounded in autonomous agents. Concepts for spatial locations cannot be directly perceived and require representations built from interactions and inferred from ambiguous sensory data. A generative toponymic language game has been developed to allow the agents to interact, forming concepts for locations and spatial relations. The studies have shown that a grounded generative toponymic language may form and evolve in a population of agents interacting through language games. Initially, terms are grounded in simple spatial concepts directly experienced by the robots. The generative process then enables the robots to learn about and refer to locations beyond their direct experience, enabling concepts and toponyms to co-evolve.

A set of simulations are presented that investigate generalization in languages evolved for mobile robots. The mobile robot platform is RatSLAM, a model for Simultaneous Localization and Mapping based on rodent hippocampus that uses visual and odometric information to build up a map of the explored environment. The language agents use information from this system as inputs and are based on simple recurrent neural networks. This paper describes two sets of experiments exploring the nature of generalization in evolved languages. The first study investigated languages evolved from visual inputs and the second study investigated languages evolved from position representations. These studies showed that processing the input prior to the language agent affects the expressivity of the languages and the performance of the agents. Some generalization occurs in these languages. Studies are ongoing to extend these simulations using the simulated world of the robots.

We present a framework and first set of simulations for evolving a language for communicating about space. The framework comprises two components: (1) An established mobile robot platform, RatSLAM, which has a 'brain' architecture based on rodent hippocampus with the ability to integrate visual and odometric cues to create internal maps of its environment. (2) A language learning system based on a neural network architecture that has been designed and implemented with the ability to evolve generalizable languages which can be learned by naive learners. A study using visual scenes and internal maps streamed from the simulated world of the robots to evolve languages is presented. This study investigated the structure of the evolved languages showing that with these inputs, expressive languages can effectively categorize the world. Ongoing studies are extending these investigations to evolve languages that use the full power of the robots representations in populations of agents.