Abstract
The purpose of this communication is to demonstrate that in a population composed of agents that are capable of simple cognitive activities spontaneously emerges coordinated communication between agents. Each agent is characterized by meaning vectors (internal states) represented by n-dimensional binary vectors. Cognitive activities of agents are performed by simple formal device represented by mappings that map binary vectors onto symbol strings (signals) and conversely. An elementary communication act consists in (1) a random selection of two agents, where one of them is declared as speaker and the other one as listener, (2) the speaker codes its randomly selected meaning vector into a sequence of symbols and sends it to the listener as a message, and finally, (3) the listener decodes this received message into a meaning vector. A Darwinian evolution of population is simulated by simple version of genetic algorithms, where agent mappings are considered as chromosomes and their fitness is evaluated on the basis of distances between speaker meaning vector and listener meaning vector constructed from the received messages. If these distances are small, then for both agents, speaker and listener, fitness is increased. It is demonstrated that in the course of evolution agents gradually improve decoding of the received messages (they are closer and closer to meaning vectors of speakers) and all agents gradually start to use tightly related cognitive devices, i.e. all agents start to use the same vocabulary for common communication. Moreover, if agent meaning vectors contain regularities, then these regularities are manifested also in messages created by agents — speaker, i.e. similar parts of meaning vectors are coded by similar symbol substrings. This observation is considered as a manifestation of an emergence of a grammar system in the common coordinated communication.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
F. de Saussure: Course in General Linguistics. Duckworth, Translated by R. Harris, London 1983 (original French edition was published in 1916).
N. Chomsky: Syntactic Structures. Mounton & Co., The Hague 1957.
N. Chomsky: Knowledge of Language: Its Nature, Origin, and Use. Praeger, New York 1987.
S. Pinker and P. Bloom: Natural language and natural selection. Behavioral and Brain Sciences, 13(1990), 707. This paper is available on.
C. Darwin: The Origin of Species. Penguin, London (1985) (original edition was published in 1859).
Approaches to the Evolution of Language: social and cognitive bases. Edited by J. R. Hurford, M. Studdert-Kennedy and C. Knight. Cambridge University Press, Cambridge 1998. An information about the book is available on http://www.ling.ed. ac.uk/~jim/evoconf.html.
J. R. Hurford: The Evolution of Language and Languages”. In The Evolution of Culture, edited by R. Dunbar, C. Knight and C. Power, Edinburgh University Press, Edinburgh 1999. This paper is available on.
J.H. Holland: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975).
D.E. Goldberg: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley(1989).
J. Batali: Computational Simulations of the Emergence of Grammar. In [6]. This paper is available on http://cogsci.ucsd.edu/~batali/papers/ grammar.html.
J. Batali: The Negotiation and Asquistion of Recursive Grammars as a Result of Competition Among Exemplar, to be published. This paper is available on http://cogsci. ucsd.edu/~batali/rephrase.ps.gz.
J. Batali: Innate Biases and Critical Periods: Combining Evolution and Learning in the Acquisition of Syntax. In Proceedings of the Fourth Artificial Life Workshop, edited by R. Brooks and P. Maes. The MIT Press, 1994, pp.160-171. This paper is available on http://cogsci.ucsd.edu/~batali/papers/alife-syntax.ps.
L. Steels: Synthesising the origins of language and meaning using co-evolution, self-organisation and level formation. In [6]. This paper is available on http://arti.vub.ac. be/steels/edin.ps.
L. Steels: A self-organizing spatial vocabulary. Artificial Life Journal 2(1966). This paper is available on http://arti.vub.ac.be/steels/space.ps.
B.J. MacLennan and G. M. Burghardt: Synthetic Ethology and the Evolution of Cooperative Communication. Adaptive Behavior 2(1994), 161–187. This paper is available on http://www.cs.utk.edu/~mclennan/anon-ftp/SEECC.ps.Z.
G. W. Werner and M.G. Dyer: Evolution of Communication in Artificial Intelligence. In Artificial Intelligence II. Edited by C. G. Langton, J. D. Farmer, and S. Rasmusen, Addison Wesley 1991.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag London
About this paper
Cite this paper
Kvasnicka, V. (2000). An Emergence of Coordinated Communication in Populations of Agents with Evolution Simulated by Genetic Algorithm. In: Suzuki, Y., Ovaska, S., Furuhashi, T., Roy, R., Dote, Y. (eds) Soft Computing in Industrial Applications. Springer, London. https://doi.org/10.1007/978-1-4471-0509-1_21
Download citation
DOI: https://doi.org/10.1007/978-1-4471-0509-1_21
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1155-9
Online ISBN: 978-1-4471-0509-1
eBook Packages: Springer Book Archive