Language Evolution and Computation Bibliography

This paper investigates the dynamics of cumulative cultural evolution in a simulation concerning the evolution of language. This simulation integrates the iterated learning model with the Talking Heads experiment in which a population of agents evolves a language to communicate geometrical coloured objects by playing guessing games and transmitting the language from one generation to the next. The results show that cumulative cultural evolution is possible if the language becomes highly regular, which only happens if the language is transmitted from generation to generation.

We study the role of the agent interaction topology in distributed language learning. In particular, we utilize the replicator-mutator framework of language evolution for the creation of an emergent agent interaction topology that leads to quick convergence. In our system, it is the links between agents that are treated as the units of selection and replication, rather than the languages themselves. We use the Noisy Preferential Attachment algorithm, which is a special case of the replicator-mutator process, for generating the topology. The advantage of the NPA algorithm is that, in the short-term, it produces a scale-free interaction network, which is helpful for rapid exploration of the space of languages present in the population. A change of parameter settings then ensures convergence because it guarantees the emergence of a single dominant node which is chosen as teacher almost always

In the context of minimally cognitive behavior, we used multi-robotic systems to investigate the emergence of communication and cooperation during the evolution of recurrent neural networks. The networks are systematically analyzed to identify their relevant dynamical properties. Evolution efficiently adapts these properties through small structural changes within the networks when specific environmental conditions are altered, such as the number of interacting robots. The findings signify the importance of reducing the predefined knowledge about resulting behaviors, dynamical properties of control, and the topology of neural networks in order to utilize the strength of the Evolutionary Robotics approach to Artificial Life.

In this paper we describe how a population of simulated robots evolved for the ability to solve a collective navigation problem develop individual and social/communication skills. In particular, we analyze the evolutionary origins of motor and signaling behaviors. Obtained results indicate that signals and the meaning of the signals produced by evolved robots are grounded not only on the robots sensory-motor system but also on robots' behavioral capabilities previously acquired. Moreover, the analysis of the co-evolution of robots individual and communicative abilities indicate how innovation in the former might create the adaptive basis for further innovations in the latter and vice versa.

An important problem for societies of natural and artificial animals is to converge upon a similar language in order to communicate. We call this the language convergence problem. In this paper we study the complexity of finding the optimal (in terms of time to convergence) algorithm for language convergence. We map the language convergence problem to instances of a Decentralized Partially Observable Markov Decision Process to show that the complexity can vary from P-complete to NEXP-complete based on the scenario being studied.