BASC, a Bottom-up Approach to automated design of Spatial Coordination

The design of spatial coordination mechanisms for a group of agents is a challenging problem to which the top-down approach of traditional Artificial Intelligence often fails to find convincing solutions. Bottom-up approaches have given more promising results, but they often rely on a tedious manual hand-crafting that raises scalability issues. In this paper, we show that combining a bottom-up spatial coordination mechanisms with specially designed evolutionary methods to search the space of solutions is an efficient approach to such problems. More precisely, we show the benefits of our platform, BASC, through a quantitative comparison with previous work published by Balch and Hybinette and we conclude on the methodological issues raised by our work.

[1]  Tucker R. Balch,et al.  Social potentials for scalable multi-robot formations , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[2]  Ronald C. Arkin,et al.  Using Genetic Algorithms to Learn Reactive Control Parameters for Autonomous Robotic Navigation , 1994, Adapt. Behav..

[3]  H. Van Dyke Parunak,et al.  Distinguishing Environmental and Agent Dynamics: A Case Study in Abstraction and Alternate Modeling Technologies , 2000, ESAW.

[4]  Tucker R. Balch,et al.  Behavior-based formation control for multirobot teams , 1998, IEEE Trans. Robotics Autom..

[5]  Ronald C. Arkin,et al.  Motor Schema — Based Mobile Robot Navigation , 1989, Int. J. Robotics Res..

[6]  Lee Spector,et al.  Evolving teamwork and coordination with genetic programming , 1996 .

[7]  Maja J. Mataric,et al.  Multi-robot target acquisition using multiple objective behavior coordination , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[8]  Charles Gide,et al.  Cours d'économie politique , 1911 .

[9]  Maja J. Mataric,et al.  Interaction and intelligent behavior , 1994 .

[10]  Maja J. Mataric,et al.  Designing and Understanding Adaptive Group Behavior , 1995, Adapt. Behav..

[11]  Stefano Nolfi,et al.  Co-evolving predator and prey robots , 1998, Artificial Life.

[12]  Annie S. Wu,et al.  The Effects of Team Size on the Evolution of Distributed Micro air Vehicles , 2000, GECCO.

[13]  Ronald C. Arkin,et al.  Behavior-Based Robot Navigation for Extended Domains , 1992, Adapt. Behav..

[14]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[15]  Dario Floreano,et al.  Spatial Coordination through Social Potential Fields and Genetic Algorithms , 2002 .

[16]  Craig W. Reynolds.  An evolved, vision-based behavioral model of coordinated group motion , 1993 .

[17]  V. Pareto,et al.  Vilfredo Pareto. Cours d’Économie Politique , 1897 .

[18]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1998 .

[19]  Craig W. Reynolds.  Evolution of corridor following behavior in a noisy world , 1994 .