Language Evolution and Computation Bibliography

This paper suggests a model of the process through which a set of symbols, initially without any intrinsic meaning, acquires endogenously a conventional and socially shared meaning. This model has two related aspects. The first is the cognitive aspect, represented by the process through which each agent processes the information gathered during the interactions with other agents. In this paper, the agents are endowed with the cognitive skills necessary to categorize the input in a lexicographic way, a categorization process that is implemented by the means of data mining techniques. The second aspect is the social one, represented by the process of reiterate interactions among the agents who compose a population. The framework of this social process is that of evolutionary game theory, with a population of agents who are randomly matched in each period in order to play a game that, in this paper, is a kind of signaling game. The simulations show that the emergence of a socially shared meaning associated to a combination of symbols is, under the assumptions of this model, a statistically inevitable occurrence.

In open systems of artificial agents, the meaning of communication in part emerges from ongoing interaction processes. In this paper, we present the empirical semantics approach to inductive derivation of communication semantics that can be used to derive this emergent semantics of communication from observations. The approach comes in two complementary variants: One uses social systems theory, focusing on system expectation structures and global utility maximisation, and the other is based on symbolic interactionism, focusing on the viewpoint and utility maximisation of the individual agent. Both these frameworks make use of the insight that the most general meaning of agent utterances lies in their expectable consequences in terms of observable events, and thus they strongly demarcate themselves from traditional approaches to the semantics and pragmatics of agent communication languages.

Recently, there has been a revival of interest in multi-agent simulation techniques for exploring the nature of language change. However, a lack of appropriate validation of simulation experiments against real language data often calls into question the general applicability of these methods in modeling realistic language change. We try to address this issue here by making an attempt to model the phenomenon of schwa deletion in Hindi through a multi-agent simulation framework. The pattern of Hindi schwa deletion and its diachronic nature are well studied, not only out of general linguistic inquiry, but also to facilitate Hindi grapheme-to-phoneme conversion, which is a preprocessing step to text-to-speech synthesis. We show that under certain conditions, the schwa deletion pattern observed in modern Hindi emerges in the system from an initial state of no deletion. The simulation framework described in this work can be extended to model other phonological changes as well.

The main contribution of this paper is threefold. First, it presents a new software system for empirical investigations of evolving agent societies in SugarScape like environments. Second, it introduces a conceptual framework for modeling cooperation in an artificial society. In this framework the environmental pressure to cooperate is controllable by a single parameter, thus allowing systematic investigations of system behavior under varying circumstances. Third, it reports upon results from experiments that implemented and tested environments based upon this new model of cooperation. The results show that the pressure to cooperate leads to the evolution of communication skills facilitating cooperation. Furthermore, higher levels of cooperation pressure lead to the emergence of increased communication.

This paper investigates how different modes of social interactions influence the bootstrapping and evolution of lexicons. This is done by comparing three language game models that differ in the type of social interactions they use. The simulations show that the language games which use either joint attention or corrective feedback as a source of contextual input are better capable of bootstrapping a lexicon than the game without such directed interactions. The simulation of the latter game, however, does show that it is possible to develop a lexicon without using directed input when the lexicon is transmitted from generation to generation.

In this paper we present some multiagent simulations in which the individuals try to reach a uniform vocabulary to name spatial movements. Each agent has initially a random vocabulary that can be modified by means of interactions with the other agents. As the objective is to name movements, the topic of conversation is chosen by moving. Each agent can remember a finite number of words per movement, with certain strength. We show the importance of the forgetting process and memory size in these simulations, discuss the effect of the number of agents on the time to agree and present a few experiments where the evolution of vocabularies takes place in a divided range.

This is an overview of recent computational work done in the simulated evolution of language. It is prefaced by an overview of the broader issues in linguistics that computational models may help to clarify. Is language innate - genetically specified in the human organism in some way, a product of natural selection? Or can the properties of language be accounted for by general cognitive capabilities that did not develop as a consequence of language-specific selective pressures? After a consideration of the intellectual background surrounding these issues, we will examine how recent computational work sheds light on them.

This paper addresses Narrative Intelligence from a bottom up, Artificial Life perspective. First, different levels of narrative intelligence are discussed in the context of human and robotic story-tellers. Then, we introduce a computational framework which is based on minimal definitions of stories, story-telling and autobiographic agents. An experimental test-bed is described which is applied to the study of story-telling, using robotic agents as examples of situated, autonomous minimal agents. Experimental data are provided which support the working hypothesis that story-telling can be advantageous, i.e. increases the survival of an autonomous, autobiographic, minimal agent. We conclude this paper by discussing implications of this approach for story-telling in humans and artifacts.

In this paper, we present several experiments of belief propagation in multi-agent communities. Each agent in the simulation has an initial random vocabulary (4 words) corresponding to each possible movement (north, south, east and west). Agents move and communicate the associated word to the surrounding agents, which can be convinced by the 'speaking agent', and change their corresponding word by 'imitation'. Vocabulary uniformity is achieved, but strong interactions and competition can occur between dominant words. Several moving and trusting strategies as well as agent roles are analyzed.