The paper discusses efforts to understand the self-organisation and evolution of language from a cognitive modeling point of view. It focuses in particular on efforts that use connectionist components to synthesise some of the major stages in the emergence of language and possible transitions between stages. The paper does not introduce new technical results but discusses a number of dimensions for mapping out the research landscape. 1 1

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Human language is unique among the communication systems of the natural world. The vocabulary of human language is unique in being both culturally-transmitted and symbolic. In this paper I present an investigation into the factors involved in the evolution of such vocabulary systems. I investigate both the cultural evolution of vocabulary systems and the biological evolution of learning rules for vocabulary acquisition.

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.

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Abstract. We present a mathematical model of cross-situational learning, in which we quantify the learnability of words and vocabularies. We find that high levels of uncertainty are not an impediment to learning single words or whole vocabulary systems, as long as the level of uncertainty is somewhat lower than the total number of meanings in the system. We further note that even large vocabularies are learnable through cross-situational learning. 1

This paper presents an algorithm for learning the meaning of messages communicated between agents that interact while acting optimally towards a cooperative goal. Our reinforcement-learning method is based on Bayesian filtering and has been adapted for a decentralized control process. Empirical results shed light on the complexity of the learning problem, and on factors affecting the speed of convergence. Designing intelligent agents able to adapt their mutual interpretation of messages exchanged, in order to improve overall task-oriented performance, introduces an essential cognitive capability that can upgrade the current state of the art in multi-agent and human-machine systems to the next level. Learning to communicate while acting will add to the robustness and flexibility of these systems and hence to a more efficient and productive performance.

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

Learning to communicate is an emerging challenge in AI research. It is known that agents interacting in decentralized, stochastic environments can benefit from exchanging information. Multiagent planning generally assumes that agents share a common means of communication; however, in building robust distributed systems it is important to address potential miscoordination resulting from misinterpretation of messages exchanged. This paper lays foundations for studying this problem, examining its properties analytically and empirically in a decision-theoretic context. We establish a formal framework for the problem, and identify a collection of necessary and sufficient properties for decision problems that allow agents to employ probabilistic updating schemes in order to learn how to interpret what others are communicating. Solving the problem optimally is often intractable, but our approach enables agents using different languages to converge upon coordination over time. Our experimental work establishes how these methods perform when applied to problems of varying complexity. 1

There is a growing body of research on multi-agent systems bootstrapping a communication system. Most studies are based on simulation, but recently there has been an increased interest in the properties and formal analysis of these systems. Although