Language Evolution and Computation Bibliography

The origins of language have become a hotly debated topic in the last decade. Researchers from many fields have been trying to explain them using the tools that their specialisations provide. Also in Artificial Intelligence their has been interest in the topic, and computer science provides a very powerful tool for studying language as a complex dynamical system: multi-agent systems. In the thesis, we present four models based on the ``Language Game'' paradigm developed by Luc Steels for studying language. We support a gradualist view of the evolution of language, and we provide partial answers to three issues that need to be clarified in order to strengthen this position: (1) each stadium of the developing communication system must be viable and successful in its own right; (2) their must be a path from each intermediate communication system to the next; and (3) the users of the communication systems must be able to transition from one system to the next using only local data. Three models describe intermediate communication systems: a system in which the agents can exchange single words referencing one referent; a system in which the agents can exchange several words referencing one referent; and a system in which the agents can exchange complex, structured utterances referencing several referents. Experiments with these systems show that each system is a successful communication system in its own right. The fourth model attempts to show that the agents can make the transition from one communication system to the next successfully and on their own. Agents in this system have the cognitive capacities for two systems: (1) and (2), and have internal pressures that can push either of the communication systems as the one to use, based on different criteria. Two criteria push the agents from system (1) to system (2), yielding interesting results. The models and experiments in the thesis give positive results for each of the issues to be clarified, thereby supporting the gradual evolution hypothesis.

Language is a complex phenomenon. Utterances arise from complex interactions between semantics and grammar. Usually, semantics and grammar are studied separately from each other. This paper introduces a model that makes it possible to study the interaction between these two parts of language. The model comprises a population of agents that feature a relatively complex semantic module, and a lexicalisation module that can produce utterances from semantic representations. The language the agents use to communicate is developed through their interactions, without central control. The concrete focus of the model is on determination: whether or not the referents of an utterance are definite.

Human, syntactic language is one of the most intriguing behaviors and receives increasing attention from researchers in numerous fields. Here we present a model that goes an important step further than previous work because it explicitly connects low-level perception and categorization, hierarchical meaning construction and syntactic language. The model thus shows a solution to the `symbol grounding problem' (Harnad, 1990): the meaning of the symbolic system - logical symbols and syntactic rules - is grounded in its relation with a simplified but realistic world. We discuss the different components of this collaborative effort: (i) a realistic simulation of Newtonian dynamics of objects in a 2D plane; (ii) schemabased event-perception and categorization; (iii) a semantics based on predicate logic; and (iv) a categorial grammar for the production and interpretation of language. The integration of the different components poses on the one hand novel and important constraints; on the other hand, it allows for experiments that help to identify the relations between the different levels. We note some important similarities and differences with SHRDLU (Winograd, 1976) and the Talking Heads experiment (Steels et al., 2002), and give an agenda for future experiments.

We have been conducting large-scale public experiments with artificial robotic agents to explore what the necessary and sufficient prerequisites are for word-meaning pairs to evolve autonomously in a population of agents through a self-organized process. We focus not so much on the question of why language has evolved but rather on how. Our hypothesis is that when agents engage in particular interactive behaviors which in turn require specific cognitive structures, they automatically arrive at a language system. We study this topic by performing experiments based on artificial systems. One such experiment, known as the Talking Heads Experiment, employs a set of visually grounded autonomous robots into which agents can install themselves to play language games with each other.