Other evolutionary models have focused on the evolution of syntax (e.g. Batali, 1994; Kirby, 1999). Simulated organisms can evolve different syntactic categories starting from given sets of syntactic structures and constraints. In this type of models only the evolution of syntax is simulated. The associations that simulated organisms learn are self-referential symbol-symbol relationships. Therefore, these models are subject to the symbol grounding problem (Harnad, 1990) since they lack an intrinsic link between their symbols and the entities and relations existing in the organisms' environment. Indeed, internal symbols need some form of sensorimotor grounding. Due to the symbol grounding problem, the role of these models for understanding the evolution of cognition is reduced.
This talk will describe a series of models for the evolution of communication. It will focus on the distinction between signals, symbols, and words in language evolution. In particular, it will show how evolutionary computation techniques, such as Artificial Life, can be used to study the emergence of syntax and symbols from simple communication signals (Cangelosi, 1999). This type of language origin models can overcome the symbol grounding problem. In fact, simulated organisms use symbols whose semantic referents are constituted by categorical representations in the neural network's hidden layer. These semantic representations are activated by the actual presence of their referents in the organism's world.
Initially, computational models that evolve repertoires of isolated signals will be presented. For example, a recent study has simulated the evolution of signals for naming foods in a population of foragers (Cangelosi, & Parisi, 1998). This type of models study communication systems based on simple signal-object associations. Organisms learn and evolve simple stimulus associations between objects in the environment and signals. Communication signals only have referential relationships with the world's entities.
Then models that study the emergence of grounded symbols will be described. For example, in Cangelosi's (1999) work, simple syntactic rules are evolved, such as symbol combination and compositionality. The modeled behavioral task is influenced by Savage-Rumbaugh & Rumbaugh's (1978) ape language experiments. This second type of models mainly focus on the distinction between simple signal-object associations and complex symbol-symbol relationships. It permits a detailed analysis of the problem of symbol acquisition. For example, comparisons between symbol acquisition in animal models (e.g. chimpanzees) and in computational models (e.g. artificial neural networks) are made. They allow us to have an operational definition of the signal-symbol-word distinction in language evolution models.
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Cangelosi A., & Parisi D. (1998). The emergence of a "language" in an evolving population of neural networks. Connection Science, 10(2), 83-97
Harnad S. (1990). The Symbol Grounding Problem. Physica D 42: 335-346
Kirby S. (1999). Syntax out of learning: The cultural evolution of structured communication in a population of induction algorithms. In D. Floreano et al. (Eds.), Proceedings of ECAL99 European Conference on Artificial Life, Berlin: Springer-Verlag
Steels L. & Kaplan F. (1999). Collective learning and semiotic dynamics. In D. Floreano et al. (Eds.), Proceedings of ECAL99 European Conference on Artificial Life, Berlin: Springer-Verlag
Steels L. & Vogt P. (1997). Grounding adaptive language games in robotic agents. In P. Husband & I. Harvey (eds). Proceedings of the Fourth European Conference on Artificial Life, London: MIT Press