Interacting robot agents

Seite 6: Linguistic co-evolution

Der folgende Beitrag ist vor 2021 erschienen. Unsere Redaktion hat seither ein neues Leitbild und redaktionelle Standards. Weitere Informationen finden Sie hier.

In genetic evolution the selectionist criteria are not fixed but derive from an environment which is constantly changing due to co-evolution. For example, one species, acting as a prey, is evolving to become better in escaping its predator. But this causes the predator to evolve again towards becoming better in catching the prey. Whereas evolution in itself causes an equilibrium to be reached, co-evolution causes a self-enforcing spiral towards greater complexity.

Also in the case of language, co-evolution appears to play a crucial role in pushing a language towards greater complexity, at all levels. The ultimate pressure comes from the growing complexity of agent-agent and agent-environment interaction partly enabled by an increasingly more powerful linguistic ability. Thus language complexity feeds on itself and escalates. The lexicon puts pressure on phonology creation to create an adequate repertoire of phonemes. If there are not enough phonemes new ones will be generated through imitation games. The language game puts pressure first on the meaning creation modules, for example, to have enough distinctions. When there are more different types of objects, more distinctions are needed. It also puts pressure on the lexicon to lexicalise the meanings that need to be communicated. Thus the more meanings are used in language games the bigger the lexicon will have to be.

In the experiments conducted so far, multiple word sentences start to emerge because as feature sets become more elaborate more than one word is needed to code a given feature set into words. Syntax becomes a natural need when greater and greater pressure is exerted to express more and more sophisticated meaning within as few elements as possible. The possible origins of syntax are discussed in more detail later.

Different games are coupled because the output of one is used as input by the other. This introduces also additional selectionist pressures so that there is a two-way flow between two interdependent modules: When module 1 delivers input to module 2, module 2 will exercise additional selectionist constraints on module 1. For example, a feature used in discrimination is more appropriate in a language game if it also has been lexicalised. When one agent uses a word and thus certain features, the other agent may have to expand his feature repertoire accordingly before being able to decode the word. Thus there are two selectionist pressures on features: Are they adequate for discrimination and do they have or are they needed for lexicalisation.

Similarly a phoneme is not only appropriate as part of the phonological repertoire when it can be produced and understood, it must also be used by the lexicon.

Another paper illustrates this in more detail based on experiments for the co-evolution of words and meanings by a combination of discrimination games and language games. The agents engage in a series of language games and as part of each language game each agent performs one discrimination game.