Abstract
The objective of this paper is to describe the development of a specific theory of interactions and learning among multiple robots performing certain tasks. One of the primary objectives of the research was to study the feasibility of a robot colony in achieving global objectives, when each individual robot is provided only with local goals and local information. In order to achieve this objective the paper introduces a novel cognitive architecture for the individual behavior of robots in a colony. Experimental investigation of the properties of the colony demonstrates its ability to achieve global goals, such as the gathering of objects, and to improve its performance as a result of learning, without explicit instructions for cooperation. Since this architecture is based on representation of the “likes” and “dislikes” of the robots, it is called the Tropism System Cognitive Architecture. This paper addresses learning in the framework of the cognitive architecture, specifically, phylogenetic and ontogenetic learning by the robots. The results show that learning is indeed possible with the Tropism Architecture, that the ability of a simulated robot colony to perform a gathering task improves with practice and that it can further improve with evolution over successive generations. Experimental results also show that the variability of the results decreases over successive generations.
Similar content being viewed by others
References
Agah, A. 1994. Sociorobotics: Learning for coordination in robot Colonies. Ph.D. Dissertation, Computer Science Department, University of Southern California, Los Angeles, California.
Agah, A. and Bekey, G.A. 1995. In a team of robots the loudest is not necessarily the best. 1995 IEEE International Conference on Systems, Man and Cybernetics, Vancouver, Canada, 3800–3805.
Arkin, R.C. 1987. Motor schema based navigation for a mobile robot: An approach to programming by behavior. In Proc. of the IEEE International Conference on Robotics and Automation, Raleigh, NC.
Arkin, R.C., Balch, T., and Nitz, E. 1993. Communication of behavioral state in multiagent retrieval tasks. In Proc. of the IEEE International Conference on Robotics and Automation, vol. 3, pp. 588–894.
Asada, M., Noda, S., Tawaratsumida, S., and Hosoda, K. 1995. Vision-based reinforcement learning for purposive behavior acquisition. In Proc. of the IEEE International Conference on Robotics and Automation, Nagoya, Japan, pp. 146–153.
Barto, A.G. 1992. Reinforcement learning and adaptive critic methods. In Handbook of Intelligent Control, D.A. White and D.A. Sofge (Eds.), Van Nostrand-Reinhold: New York, NY, pp. 469–491.
Beni, G. and Hackwood, S. 1990. The maximum entropy principle and sensing in swarm intelligence. In Toward a Practice of Autonomous Systems, F.J. Varela and P. Bourgine (Eds.), MIT Press: Cambridge, Massachusetts, pp. 153–160.
Brooks, R.A. 1986. A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation.
Brooks, R.A. 1989. A robot that walks: Emergent behaviors from a carefully evolved network. Neural Computation, 1:253–262.
Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., and Chretien, L. 1991. The dynamics of collective sorting robot-like ants and ant-like robots. In From Animals to Animats, edited by J.-A. Meyer and S.W. Wilson, MIT Press: Cambridge, Massachusetts, pp. 356–363.
Fukuda, T., Ueyama, T., and Arai, F. 1992. Control strategies for cellular robotic network. In Distributed Intelligence Systems, A.H. Levis and H.E. Stephanou (Eds.), Pergamon Press: Oxford, pp. 177–182.
Gasser, L. and Hill, R.W. 1990. Coordinated problem solvers. Annual Review of Computer Science, 4:203–253.
Goldberg, D.E. 1989. Genetic Algorithms in Search. Optimization, and Machine Learning. Addison-Wesley Publishing Company, Inc.: Reading, Massachusetts.
Griffin, D.R. 1992. Animal Minds. The University of Chicago Press: Chicago.
Hackman, J.R. 1990. Groups That Work (and Those That Don't). Jossey-Bass Inc., Publishers: San Francisco, California.
Ichikawa, S., Hara, F., and Hosokai, H. 1993. Cooperative route-searching behavior of multi-robot system using hello-call communication. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1149–1156.
Ishida, T. 1993. Towards organizational problem solving. In Proceedings of the IEEE International Conference on Robotics and Automation, 3:839–845.
Ishida, Y., Asama, H., Endo, I., Ozaki, K., and Matsumoto, A. 1992. Communication and cooperation in an autonomous and decentralized robot system. In Distributed Intelligence Systems, A.H. Levis and H.E. Stephanou (Eds.), Pergamon Press: Oxford, pp. 189–194.
Kaelbling, L.P. 1993. Learning to achieve goals. In Proceedings of the International Joint Conference on Artificial Intelligence (IJ-CAI'93), pp. 1094–1098.
Kawauchi, Y., Inaba, M., and Fukuda, T. 1992. Self-organizing intelligence for cellular robotic system CEBOT with genetic knowledge production algorithm. In Proceedings of the IEEE International Conference on Robotics and Automation, pp. 813–818.
Kube, C.R. and Zhang, H. 1994. Collective robotics: From social insects to robots. Adaptive Behavior, 2:189–218.
Maes, P. and Brooks, R.A. 1991. Learning to coordinate Behaviors. In Autonomous Mobile Robots: Control, Planning, and Architecture, S.S. Iyengar and A. Elfes (Eds.), IEEE Computer Society Press: Los Alamitos, California, pp. 224–230.
Majchrzak, A. and Gasser, L. 1992. Toward a conceptual framework for specifying manufacturing workgroups congruent with technological change. International Journal of Computer Integrated Manufacturing, 5:118–131.
Mataric, M.J. 1992. Minimizing complexity in controlling a mobile robot population. In Proceedings of the IEEE International Conference on Robotics and Automation, pp. 830–835.
Noreils, F.R. 1993. Toward a robot architecture integrating cooperation between mobile robots: Application to indoor environment. The International Journal of Robotics Research, 12:79–98.
Walter, W.G. 1950. An imitation of life. Scientific American, 182:42–45.
Walter, W.G. 1953. The Living Brain. W.W. Norton & Company, Inc.: New York.
Wang, J. and Beni, G. 1988. Pattern generation in cellular robotic systems. In Proceedings of the IEEE International Symposium on Intelligent Control, pp. 63–69.
Wiener, N. 1961. Cybernetics, or Control and Communication in the Animal and the Machine. Second Edition, MIT Press: Cambridge, Massachusetts.
Wilson, E.O. 1971. The Insect Societies. The Belknap Press: Cambridge, Massachusetts.
Wilson, E.O. 1980. Sociobiology: The Abridged Edition. The Belknap Press: Cambridge, Massachusetts.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Agah, A., Bekey, G.A. Phylogenetic and Ontogenetic Learning in a Colony of Interacting Robots. Autonomous Robots 4, 85–100 (1997). https://doi.org/10.1023/A:1008811203902
Issue Date:
DOI: https://doi.org/10.1023/A:1008811203902