This paper illustrates how external (or social) symbol grounding can be studied in simulations with large populations. We discuss how we can simulate language evolution in a relatively complex environment which has been developed in the context of the New Ties project. This project has the objective of evolving a cultural society and, in doing so, the agents have to evolve a communication system that is grounded in their inter-actions with their virtual environment and with other individuals. A preliminary experiment is presented in which we investigate the effect of a number of learning mechanisms. The results show that the social sym-bol grounding problem is a particularly hard one; however, we provide an ideal platform to study this problem.

Abstract. 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. 1

Abstract. In this article, we study the emergence of associations between words and concepts using the self-organizing map. In particular, we explore the meaning negotiations among communicating agents. The self-organizing map is used as a model of an agent’s conceptual memory. The concepts are not explicitly given but they are learned by the agent in an unsupervised manner. Concepts are viewed as areas formed in a self-organizing map based on unsupervised learning. The language acquisition process is modeled in a population of simulated agents by using a series of language games, specifically observational games. The results of the simulation experiments verify that the agents learn to communicate successfully and a shared lexicon emerges. 1

This paper investigates the effect of predefining semantics in modelling the evolution of compositional languages versus allowing agents to develop these semantics in parallel with the development of language. The study is done using a multi-agent model of language evolution that is based on the Talking Heads experiment. The experiments show that when allowing a co-evolution of semantics with language, compositional languages develop faster than when the semantics are predefined, but compositionality appears more stable in the latter case. The paper concludes that conclusions drawn from simulations with predefined meanings, which most studies use, may need revision. 1

Contemporary research in artificial environments has marked the need for autonomy in artificial agents. Autonomy has many interpretations in terms of the field within which it is being used and analyzed, but the majority of the researchers in artificial environments are arguing in favor of a strong and life-like notion of autonomy. Departing from this point the main aim of this paper is to examine the possibility of the emergence of autonomy in contemporary artificial agents. The theoretical findings of research in the areas of living and cognitive systems, suggests that the study of autonomous agents should adopt a systemic and emergent perspective for the analysis of the evolutionary development of the notions/properties of autonomy, functionality, intentionality and meaning, as the fundamental and characteristic properties of a natural agent. An analytic indication of the functional emergence of these concepts and properties is provided, based on the characteristics of the more general systemic framework of second-order cybernetic and of the interactivist framework. The notion of emergence is a key concept in such an analysis which in turn provides the ground for the theoretical evaluation of the autonomy of contemporary artificial agents with respect to the functional emergence of their capacities. The fundamental problems for the emergence of genuine autonomy in artificial agents are critically discussed and some design guidelines are provided.

There is an ongoing debate as to whether the words in early pre-syntactic forms of human language had simple atomic meanings like modern words (Bickerton, 1990, 1996), or whether they were holophrastic (Wray, 1998, 2000). Simulations were conducted using an iterated learning model in which the agents were able to associate words with meanings, but in which they were not able to use syntactic rules to combine words into phrases or sentences. In some of these simulations words emerged which had neither holophrastic nor atomic meanings, demonstrating the possibility of protolanguages intermediate between these two extremes. Further simulations show how increases in cognitive or articulatory capacity would have produced changes in the type of words that were dominant in protolanguages. It is likely that at some point in time humans spoke a protolanguage in which most words had neither holophrastic nor atomic meanings.

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Abstract. Autonomy is a fundamental property for an intelligent virtual agent. The problem in the design of an autonomous IVA is that the respective models approach the interactive, environmental and representational aspects of the agent as separate to each other, while the situation in biological agents is quite different. A theoretical framework indicating the fundamental properties and characteristics of an autonomous biological agent is briefly presented and the interactivist model of representations combined with the concept of a semiotic process are used as a way to provide a detailed architecture of an autonomous agent and its fundamental characteristics. A part of the architecture is implemented as a case study and the results are critically discussed showing that such architecture may provide grounded representational structures, while issues of scaling are more difficult to be tackled.

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The Iterated Classification Game (ICG) combines the Classification Game with the Iterated Learning Model (ILM) to create a more realistic model of the cultural transmission of language through generations. It includes both learning from parents and learning from peers. Further, it eliminates some of the chief criticisms of the ILM: that it does not study grounded languages, that it does not include peer learning, and that it builds in a bias for compositional languages. We show that, over the span of a few generations, a stable linguistic system emerges that can be acquired very quickly by each generation, is compositional, and helps the agents to solve the classification problem with which they are faced. The ICG also leads to a different interpretation of the language acquisition process. It suggests that the role of parents is to initialize the linguistic system of the child in such a way that subsequent interaction with peers results in rapid convergence to the correct language. 1