Talk: “Design of a consensual domain”

Note: This talk happened already!

Design of a consensual domain: the artificial evolution of communicative behavior
Tom Froese
Symposium on ‘Language, Communication & Cognition’
May 25-26th 2006, University of Sussex, Brighton, UK

Almost since the beginning of research in artificial life there have been attempts at using synthetic models to gain insights into the origins of communication. In practice this usually takes the form of artificially evolving communication between embodied and situated agent s (Steels 2003). Within this framework two main paradigms can be distinguished: the computational approach to cognition characterizes communication in functional terms as a transfer of information between sender and receiver, while the organismic approach defines communication in operational terms as a form of behavioral coordination between structure-determined dynamical systems (Maturana & Varela 1987).

Recently the computational approach has come under criticism for ignoring the biological mechanisms which are responsible for the origin of communication in natural systems. While there are examples of communicative behavior between living organisms which can be said to describe a certain state of affairs, these descriptions require the existence of previous consensual agreement which can only be achieved on the grounds of pre-existing communicative abilities (Di Paolo 1997). This shortcoming of the computational approach is also reflected in models in which a communication channel is explicitly made availab le to the agents as part of the experimental setup (e.g. MacLennan & Burghardt 1994; Werner & Dyer 1991). In this manner communicative behavior does not first have to originate from noncommunicative behavior but merely has to be fine-tuned by artificial evolution. However, more recently some research has successfully used the dynamical systems approach to cognition in order to address this issue. For example, movement coordination through role allocation has been evolved between two simulated autonomous agents equipped only with wheels and proximity sensors (Quinn 2001). In addition, this methodology has resulted in a further system in which 3 actual robots form a team in order to work together on a movement formation task (Quinn et al. 2002).

These developments are important for the organismic paradigm because they present us with concrete examples where non-trivial, situated, and embodied communication between autonomous agents has evolved from non-communicative behavior. Because the signaling behavior in these examples is of the binary form ‘leader/follower’, we propose that the next step is to evolve a form of behavioral coordination, which exhibits more than one observable signal with respect to the changing internal and external context of the agent. It is an empirical question whether these multiple signals would evolve as noticeable changes in intensity of one type of coordinating behavior, or rather as two distinct behaviors.

In general, this focus on behavior, the structure of the organism, and of its environment would permit an analysis of how these theoretical entities are related and how they enable non-trivial communicative behavior to emerge. A possible experimental setup is outlined which is modeled on an ethological study of the mating behavior of Lebistes reticulates (Baerends, Brouwer & Waterbolk 1955).


Baerends, G.P., Brouwer, R., & Waterbolk, H.T.J. (1955), ‘Ethological studies on Lebistes reticulates (Peters). I. An analysis of the male courtship pattern’, Behaviour, 8, pp. 249-335

Di Paolo, E.A. (1997), ‘An investigation into the evolution of communication’, Adaptive Behavior, 6(2), pp. 285-324

MacLennan, B.J., & Burghardt, G.M. (1994), ‘Synthetic ecology and the evolution of cooperative communication’, Adaptive Behavior, 2(2), pp. 151-188

Maturana, H.R., & Varela, F.J. (1987), The Tree of Knowledge: The Biological Roots of Human Understanding, Boston, MA: Shambhala Publications

Quinn, M. (2001), ‘Evolving communication without dedicated communication channels’, Proc. of the 6th Euro. Conf. on Artificial Life, ECAL’01, Kelemen, J. and Sosik, P. (eds.), Springer, pp. 357-366

Quinn, M., Smith, L., Mayley, G., & Husbands, P. (2002), ‘Evolving Formation Movement for a Homogeneous Multi-Robot System: Teamwork and Role-Allocation with Real Robots’, Cognitive Science Research Paper, 515, Brighton, UK: University of Sussex

Steels, L. (2003), ‘The Evolution of Communication Systems by Adaptive Agents’, Adaptive Agents and Multi-Agent Systems, Alonso, E. et al. (eds.), LNAI 2636, Springer Verlag, pp. 125-140

Werner, G.M. & Dyer, M.G. (1991), ‘Evolution of Communication in Artificial Organisms’, Artificial Life II, Langton, C.G., Taylor, C., Farmer, J.D., & Rasmussen, S. (eds.), Addison-Wesley, pp. 659-687


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