A BEHAVIORAL SIMULATION MODEL FOR THE STUDY OF EMERGENT SOCIAL STRUCTURES

In this paper is reported a research on a system aiming to simulate and model the life of artificial creature populations in order to study the emergence of social structures whithin these populations. The features of this system, called EthoModeling Framework (EMF), are fully described. We show how to create an environment and how the agents communicate through it. We describe the structure of the agents and their capacitites in terms of behavior and learning. The notions of task, primitive and behavior reinforcement are presented and discussed with respect to other behavior-based approaches. Then, we introduce the MANTA project, implemented under EMF, whose purpose is to study the emergence of a division of labor inside a modeled anthill. A first example is presented as a case study on the generation of a division of tasks within a population of deliberately simplified ants. A second example allows us to show that an emergent social structure is able to improve the efficiency of an emergent functionality already studied in other works, e.g. collective sorting

[1]  John E. W. Mayhew,et al.  Computer simulation of an animal environment , 1991 .

[2]  Rodney A. Brooks,et al.  Elephants don't play chess , 1990, Robotics Auton. Syst..

[3]  Pattie Maes,et al.  A bottom-up mechanism for behavior selection in an artificial creature , 1991 .

[4]  Luc Steels,et al.  Cooperation between distributed agents through self-organisation , 1990, EEE International Workshop on Intelligent Robots and Systems, Towards a New Frontier of Applications.

[5]  Mitchel Resnick,et al.  Beyond the Centralized Mindset , 1996 .

[6]  Paulien Hogeweg,et al.  Evolution as pattern processing: TODO as substrate for evolution , 1991 .

[7]  Pattie Maes,et al.  Situated agents can have goals , 1990, Robotics Auton. Syst..

[8]  P. Hogeweg,et al.  Socioinformatic processes: MIRROR modelling methodology , 1985 .

[9]  Luc Steels,et al.  Towards a theory of emergent functionality , 1991 .

[10]  J. Deneubourg,et al.  Collective patterns and decision-making , 1989 .

[11]  D. Fresneau,et al.  Social regulation in ponerine ants , 1987 .

[12]  Uwe Schnepf,et al.  Robot ethology: a proposal for the research into intelligent autonomous systems , 1991 .

[13]  Jean-Louis Deneubourg,et al.  The dynamics of collective sorting robot-like ants and ant-like robots , 1991 .

[14]  Guy Theraulaz,et al.  Task differentiation in Polistes wasp colonies: a model for self-organizing groups of robots , 1991 .

[15]  Jean-Louis Deneubourg,et al.  Random behaviour, amplification processes and number of participants: how they contribute to the foraging properties of ants , 1986 .

[16]  Dominique Fresneau,et al.  An automated photographic technique for behavioural investigations of social insects , 1986, Behavioural Processes.

[17]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[18]  J.-P. Briot,et al.  From objects to actors: study of a limited symbiosis in smalltalk-80 , 1988, OOPSLA/ECOOP '88.

[19]  P. Hogeweg,et al.  The ontogeny of the interaction structure in bumble bee colonies: A MIRROR model , 1983, Behavioral Ecology and Sociobiology.

[20]  J. Deneubourg,et al.  Self-organization mechanisms in ant societies. II: Learning in foraging and division of labor , 1987 .

[21]  Long-Ji Lin,et al.  Self-improving reactive agents: case studies of reinforcement learning frameworks , 1991 .