Meaning creation and communication in a community of agents
J.F. Fontanari, L.I. Perlovsky · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
The emergence of communication is studied in a scenario where agents endowed with distinct object-meaning mappings learn from scratch signal-meaning associations (i.e., communication codes) that allow them to identify the objects in their environment. Meanings are created through the Modeling Field Theory categorization mechanism, and learning is based on two variants of the obverter procedure, in which the agents may or may not receive feedback about the success of the communication episodes. We show that in the unsupervised learning scheme the agents fail to develop ideal communication codes, whereas success is guaranteed in the supervised scheme provided the size of the repertoire of signals is sufficiently large, though only a few signal are actually used in the code. Thus the mere ability to produce and observe different signals bears on the quality of the evolved communication codes.