Language Evolution and Computation Bibliography

We consider a simple computational model of the evolution of a quantitative trait and its phenotypic plasticity based on directional and positive frequency-dependent selection in order to explore whether and how leaning might facilitate evolution under the dynamics that arise from communicative interactions among individuals. In the model, each individual expresses, at many different times in its lifetime, its real-valued trait depending on the probability distribution determined by its own genotypes. In communicative interactions between two individuals, the contribution of an interaction to the fitness is high when their trait values are close to each other as well as large, which represents the positive frequency-dependent and directional components of selection, respectively. The iterative interactions allow individuals to acquire a more adaptive trait pair through trial and error. Under the stochastic evolution process with the limited number of individuals, we show that learning allows the population to avoid getting stuck in the global but low optimum of the innate and individual-level fitness landscape via both aspects of the components of selection, and brings about the successful evolution by increasing the genetic variation of the population. We also analyze how such an effect of learning can be realized by measuring the degree of the two different contributions for increasing the adaptivity and similarity of communicative traits, respectively. We show that this effect of learning arises from these different types of contributions depending on the biological and environmental conditions such as the mutation rate and the duration of communicative interactions. We further show the condition for the complete genetic assimilation to occur.

It is commonly agreed upon that misperception is detrimental. However, misperception might have a beneficial effect from a collective viewpoint when individuals mispercept incoming information that promotes a specific kind of behavior, which leads to an increase in diversity. First, this paper proposes our hypothesis regarding adaptive property of misperception based on the argument of the relationship between misperception and behavioral diversity, and the effects of communication on diversity. Then, a simple computational model is constructed for a resource-searching problem by using the multi-agent modeling method. We investigate both direct misperception, that are caused when obtaining information directly from surrounding environment, and indirect misperception, that are caused when obtaining information indirectly through communication by conducting simulation experiments. The experimental results have shown that misperception could increase diversity in behavior of agents, thus could be adaptive, while accurate communication could decrease a diversity of agent behavior, which might decrease fitness. This paper also discusses a correlative relationship between direct misperception and indirect misperception. We believe that the study on adaptive property of misperception based on an innovative frame of reference and a powerful methodology in the field of complex system or artificial life would shed light on fundamental issues in cognitive science, memetics and engineering.

This paper reports on the current state of our efforts to shed light on the origin and evolution of linguistic diversity by using synthetic modeling and artificial life techniques. We construct a simple abstract model of a communication system that has been designed with regard to referential signaling in nonhuman animals. The evolutionary dynamics of vocabulary sharing is analyzed based on these experiments. The results show that mutation rates, population size, and resource restrictions define the classes of vocabulary sharing. We also see a dynamic equilibrium, where two states, a state with one dominant shared word and a state with several dominant shared words, take turns appearing. We incorporate the idea of the abstract model into a more concrete situation and present an agent-based model to verify the results of the abstract model and to examine the possibility of using linguistic diversity in the field of distributed AI and robotics. It has been shown that the evolution of linguistic diversity in vocabulary sharing will support cooperative behavior in a population of agents.

This article reports on the current state of our efforts to shed light on the origin and evolution of linguistic diversity using synthetic modeling and artificial life techniques. We construct a simple abstract model of a communication system that has been designed with regard to referential signaling in nonhuman animals. We analyze the evolutionary dynamics of vocabulary sharing based on these experiments. The results show that mutation rates, population size, and resource restrictions define the classes of vocabulary sharing. We also see a dynamic equilibrium, where two states, a state with one dominant shared word and a state with several dominant shared words, take turns appearing. We incorporate the idea of the abstract model into a more concrete situation and present an agent-based model to verify the results of the abstract model and to examine the possibility of using linguistic diversity in the field of distributed AI and robotics. It has been shown that the evolution of linguistic diversity in vocabulary sharing will support cooperative behavior in a population of agents.

This paper investigates the evolution of communication among autonomous robots in the real world. A simple model has been constructed as a first step, in which a population of artificial organisms inhabits a lattice plane. Each organism communicates information with neighbors by uttering words. A common language typically evolves. We have analyzed evolutionary dynamics in this system, and have begun to implement it with a population of small mobile robots.

Natural language, communication or related mental phenomena must surely be a prominent candidate for an evolutionary explanation. This paper discusses a primitive model of language generation by evolution and learning among a population of artificial organisms whose brains are realized by a model of associative memory with a neural network structure. The goal of our study is to acquire general knowledge of the theory that relates the mechanisms to the evolutionary process such as language generation, and to develop the evolutionary systems which have facilities for still more intelligent information processing.