Evolution of Symbolic Grammar Systems

Evolution of symbolic language and grammar is studied in a network model. Language is expressed by words, i.e. strings of symbols, which are generated by agents with their own symbolic grammar system. By deriving and accepting words, the agents communicate with each other. An agent which can derive less frequent and less acceptable words and accept words in less computational time will have higher scores. Grammars of agents can evolve by mutationally processes, where higher scored agents have more chances to breed their o springs with improved grammar system. Complexity and diversity of words increase in time. It is found that the module type evolution and the emergence of loop structure enhance the evolution. Furthermore, ensemble structure (net-grammar) emerges from interaction among individual grammar systems. A net-grammar restricts structures of individual grammar and determines their evolutionary pathway.