Abstract
We propose a novel approach that aims to realize autonomous developmental intelligence called Intelligence Dynamics. We emphasize two technical features of dynamics and embodiment in comparison with the symbolic approach of the conventional Artificial Intelligence. The essential conceptual idea of this approach is that an embodied agent interacts with the real world to learn and develop its intelligence as attractors of the dynamic interaction. We develop two computational models, one is for self-organizing multi-attractors, and the other provides a motivational system for open-ended learning agents. The former model is realized by recurrent neural networks with a small humanoid body in the real world, and the later is realized by hierarchical support vector machines with inverted pendulum agents in a virtual world. Although they are preliminary experiments, they take important first steps towards demonstrating the feasibility and value of open-ended learning agents with the concept of Intelligence Dynamics.
Article PDF
Similar content being viewed by others
References
Allen G.I., Tsukahara N. (1974) Cerebrocerebellar communication system. Physical Review 54: 957–1006
Asada M., MacDorman K.F., Ishiguro H., Kuniyoshi Y. (2001) Cognitive developmental robotics as a new paradigm for designing humanoid robots. Robotics and Autonomous Systems 37: 185–193. doi:10.1016/S0921-8890(01)00157-9
Barto, A. G., Singh, S., & Chentanez, N. (2004). Intrinsically motivated learning of hierarchical collection of skills. In Proceedings of the 3rd international conference on developmental learning (ICDL), San Diego, CA (pp. 112–119).
Bentivegna D.C., Atkeson C.G., Cheng G. (2004) Learning tasks from observation and practice. Robotics and Autonomous Systems 47(2–3): 163–169
Bentivegna, D. C., Ude, A., Atkeson, C. G., & Cheng, G. (2002). Humanoid robot learning and game playing using PC-based vision. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, Lausanne, Switzerland (pp. 2449–2454).
Bernstein N. (1967) The coordination and regulation of movements. Pergamon Press, Oxford
Borg I., Groenen P. (1997) Modern multidimensional scaling. Theory and applications. Springer, New York
Charniak E., McDermott D. (1985) Introduction to artificial intelligence. Reading, MA, Addison Wesley
Csikszentmihalyi M. (1990) Flow: The psychology of optimal experience. Harper and Row, New York
Donald M. (1991) Origin of the modern mind. Harvard University Press, Cambridge, MA
Doya K., Samejima K., Katagiri K., Kawato M. (2002) Multiple model-based reinforcement learning. Neural Computation 14(6): 1347–1369. doi:10.1162/089976602753712972
Fujita, M., Kuroki, Y., Ishida, T., & Doi, T. T. (2003). Autonomous behavior control architecture of entertainment humanoid robot SDR-4X. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, Las Vegas, NV (pp. 960–967).
Harnad S. (1990) The symbol grounding problem. Physica D. Nonlinear Phenomena 42: 335–346. doi:10.1016/0167-2789(90)90087-6
Haruno M., Wolpert D.M., Kawato M. (2001) MOSAIC model for sensorimotor learning and control. Neural Computation 13: 2201–2220. doi:10.1162/089976601750541778
Inamura T., Nakamura F., Toshima I. (2004) Embodied symbol emergence based on mimesis theory. The International Journal of Robotics Research 23(4): 363–377. doi:10.1177/0278364904042199
Ito M., Noda K., Hoshino Y., Tani J. (2006) Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model. Neural Networks 19(3): 323–337
Jordan M.I., Rumelhart D.E. (1992) Forward models: Supervised learning with a distal teacher. Cognitive Science 16: 307–354
Kaplan, F., & Oudeyer, P.-Y. (2003). Motivational principles for visual know-how development. In Proceedings of the 3rd international workshop on epigenetic robotics, Edinburgh, Scotland (pp. 73–80).
Kohonen T. (1997) Self-organizing maps. Springer-Verlag, New York
Kuniyoshi Y., Ohmura Y., Terada K., Nagakubo A., Eitoku S., Yamamoto T. (2004) Embodied basis of invariant features in execution and perception of whole body dynamic actions—knacks and focuses of roll-and-rise motion. Robotics and Autonomous Systems 48(4): 189–201. doi:10.1016/j.robot.2004.07.004
Ma J., Theiler J., Perkins S. (2003) Accurate on-line support vector regression. Neural Computation 15(11): 2683–2703. doi:10.1162/089976603322385117
Minamino, K. (2008). Intelligence model organized by rich experience (in Japanese). In Intelligence dynamics (Vol. 3). Japan: Springer.
Newell A., Simon H.A. (1976) Computer science as empirical enquiry: Symbols and search. Communications of the ACM 19(3): 113–126. doi:10.1145/360018.360022
Noda, K., Ito, M., Hoshino, Y., & Tani, J. (2006). Dynamic generation and switching of object handling behaviors by a humanoid robot using a recurrent neural network model. In Proceedings of simulation of adaptive behavior (SAB’06), Rome, Italy. Lecture Notes in Artificial Intelligence (Vol. 4095, pp. 185–196).
Pfeifer R., Scheier C. (1999) Understanding intelligence. MIT Press, Cambridge, MA
Reed E.S. (1997) From soul to mind: The emergence of psychology, from Erasmus Darwin to William James. Yale University Press, New Haven, CT
Rizzolattie G., Fadiga L., Gallese V., Fogassi L. (1996) Premotor cortex and the recognition of motor actions. Brain Research. Cognitive Brain Research 3: 131–141. doi:10.1016/0926-6410(95)00038-0
Rumelhart D.E., Hinton G.E., Williams R.J. (1986) Learning internal representations by error propagation. In: Rumelhart D.E., McClelland J.L. (eds) Parallel distributed processing. MIT Press, Cambridge, MA
Russell R., Norvig P. (2002) Artificial intelligence: A modern approach. Prentice Hall, Englewood Cliffs, NJ
Sabe, K. (2005). A proposal of intelligence model: MINDY (in Japanese). In Intelligence dynamics (Vol. 2). Japan: Springer.
Sabe, K., Hidai, K., Kawamoto, K., & Suzuki, H. (2006). A proposal for intelligence model, MINDY for open ended learning system. In Proceedings of the international workshop on intelligence dynamics at IEEE/RSJ Humanoids, Geneva, Italy.
Scholkopf B., Smola A.J. (2001) Learning with kernels: Support vector machines, regularization, optimization and beyond. MIT Press, Cambridge, MA
Sutton R.S., Bart A.G. (1998) Reinforcement learning. MIT Press, Cambridge, MA
Tani J. (2001) Learning to generate articulated behavior through the bottom-up and the top-down interaction process. Neural Networks 16(1): 11–23. doi:10.1016/S0893-6080(02)00214-9
Vijayakumar, S., & Schaal, S. (2000). LWPR: An O(n) algorithm for incremental real time learning in high dimensional space. In Proceedings of the seventeenth international conference on machine learning (ICML2000), Stanford, CA (pp. 1079–1086).
Acknowledgements
The author would like thank the former members of Sony Intelligence Dynamics Inc. (SIDL) for their research efforts on the concept and experiments described here. Especially the author would like to thank Dr. Toshi. T. Doi, the former president of SIDL, for directing Intelligence Dynamics research, and to Hideki Shimomura, Kohtaro Sabe, and Masato Ito for their discussions on this article. The author would also like to thank Akira Iga, the former president of Information Technologies laboratories, Sony, for his assist to continue research on Intelligence Dynamics.
Open Access
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
About this article
Cite this article
Fujita, M. Intelligence Dynamics: a concept and preliminary experiments for open-ended learning agents. Auton Agent Multi-Agent Syst 19, 248–271 (2009). https://doi.org/10.1007/s10458-009-9076-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10458-009-9076-y