These studies have shown that communication can be highly beneficial, allowing the communicating individuals to outperform the non communicating ones. This paper follows on from this line of work, and also borrows from language learning algorithms developed in computational linguistics . In Artificial Life IV, ed. The controversy lies in the nature of these transmissions and the source of innovations. The communication in these models resembles real communication only in an abstract sense.
Fewer studies exist that model genetic transmission of language capabilities.
Language capabilities are described with context free grammars , that make compositional and recursive structures very easy to obtain. Under these simplified conditions, the interaction between evolutionary dynamics and group dynamics can be studied. In making this change we allow a language to emerge that is learnable from a speci c set of weights. Another potential cause of learning failure that we have identi ed is the selection of training data from which new individuals learn. This paper reports on some simulations which evolve weight sets for both first order and second order recurrent networks to predict the CFL 0 n 1 n .