Evolution of Communication

The aim of this project is to address questions on the emergence and evolution of communication in groups of social organisms by using evolutionary robotics to build societies of autonomous robots that evolve a communication system to solve a particular survival task collectively.

Communication is an integral part of the social behaviour of all organisms, ranging from bacteria to humans. Despite extensive research to uncover its mysteries, its evolutionary dynamics remain elusive. Since communication does not fossilise, studying its evolution is an immensely challenging task. We propose that by applying experimental evolution with groups of robots, we can overcome some of the difficulties faced by empirical studies, as well as other modelling techniques, such as game-theoretical or mathematical modelling. The evolutionary approach allows us to access all the data about the evolving communication system and the corresponding behaviour in a relatively complex mechanistic model.

In previous and current work we have focused on the following issues:

  • Conditions for the emergence of communication

    How does a communication system evolve and stabilise in a population? If the cost for signalling is high, why would an individual be interested in sharing information with the rest of the group? Our research has shown that different levels of genetic relatedness and levels of competition between/within groups highly influence the emerging communication system and its stabilisation.

  • The evolution of communication in competing populations

    Communication between conspecifics can significantly enhance cooperative behaviour. However, such signals can also have detrimental effects because they can be exploited by other competing organisms. Here, we investigate the evolution and effectiveness of intragroup communication strategies and their robustness to intergroup competition. 

  • The transition to symbolic communication

    What are the differences between human and non-human communication? What is meant by symbolic communication? In what way is animal communication less symbolic than language? We aim to explore these questions through a number of experiments representing the transition from non-symbolic to symbolic communication.


This project is conducted in collaboration with Laurent Keller (UNIL).



A. Asaei; M. Cernak; H. Bourlard; D. Ram : Sparse Pronunciation Codes for Perceptual Phonetic Information Assessment. 2017. Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS).


P. Dighe; A. Asaei; H. Bourlard : Sparse Modeling of Neural Network Posterior Probabilities for Exemplar-based Speech Recognition; Speech Communication. 2016. DOI : 10.1016/j.specom.2015.06.002.


R. Rasipuram; M. Magimai.-Doss : Probabilistic Lexical Modeling and Grapheme-based Automatic Speech Recognition. 2013.
S. Mitri; S. Wischmann; D. Floreano; L. Keller : Using robots to understand social behavior; Biological Reviews. 2013. DOI : 10.1111/j.1469-185X.2012.00236.x.


S. Wischmann; D. Floreano; L. Keller : Historical contingency affects signaling strategies and competitive abilities in evolving populations of simulated robots; PNAS. 2012. DOI : 10.1073/pnas.1104267109.


S. Mitri; D. Floreano; L. Keller : Relatedness influences signal reliability in evolving robots; Proceedings of the Royal Society B. 2011. DOI : 10.1098/rspb.2010.1407.


D. Floreano; L. Keller : Evolution of Adaptive Behaviour in Robots by Means of Darwinian Selection; PLOS Biology. 2010. DOI : 10.1371/journal.pbio.1000292.


S. Wischmann : Do animat models always need a biological target organism?; Adaptive Behavior. 2009. DOI : 10.1177/1059712309340861.
S. Mitri; D. Floreano; L. Keller : The Evolution of Information Suppression in Communicating Robots with Conflicting Interests; PNAS. 2009. DOI : 10.1073/pnas.0903152106.
D. Floreano; S. Mitri; J. Hubert : A Robotic Platform for Studying the Evolution of Communication; Evolution of Communication and Language in Embodied Agents; Berlin: Springer Verlag, 2009.
S. Mitri; D. Floreano; L. Keller : Evolutionary Conditions for the Emergence of Communication; Evolution of Communication and Language in Embodied Agents; Berlin: Springer Verlag, 2009.


S. Mitri; J. Hubert; M. Waibel : Social Behavior: From Cooperation to Language; Biological Theory. 2008. DOI : 10.1162/biot.2008.3.2.99.
D. Floreano; S. Mitri; A. Perez-Uribe; L. Keller : Evolution of Altruistic Robots; Computational Intelligence: Research Frontiers; Heidelberg: Springer Verlag, 2008. p. 232-248.


D. Floreano; S. Mitri; S. Magnenat; L. Keller : Evolutionary Conditions for the Emergence of Communication in Robots; Current Biology. 2007. DOI : 10.1016/j.cub.2007.01.058.


H. Misra; H. Bourlard : Spectral Entropy Feature in Full-Combination Multi-stream for Robust ASR. 2005.


M. F. BenZeghiba; H. Bourlard : On the Combination of Speech and Speaker Recognition. 2003.


J. Ajmera; I. A. McCowan; H. Bourlard : Speech/Music Discrimination using Entropy and Dynamism Features in a HMM Classification Framewor. 2001.
S. Moeller; H. Bourlard : Analytic Assessment of Telephone Transmission Impact on ASR Performance Using a Simulation Model. 2001.
T. A. Stephenson; M. Magimai.-Doss; H. Bourlard : Modeling Auxiliary Information in Bayesian Network Based ASR. 2001.


S. Dupont; H. Bourlard; C. Ris : Robust Speech Recognition based on Multi-Stream Features. 1997.