Responding to affordances: learning and projecting a sensorimotor mapping

Proceedings - IEEE International Conference on Robotics and Automation

Volumn 4, Issue , 2000, Pages 3253-3259

  MacDorman, Karl F ^a  

^a OSAKA UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ONLINE LEARNING ALGORITHMS; SENSIMOTOR MAPPING;

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL METHODS; HEURISTIC METHODS; LEARNING ALGORITHMS; ROBOT LEARNING; SENSOR DATA FUSION; WAVELET TRANSFORMS;

MOBILE ROBOTS;

EID: 0033691409     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Bentley, J. L. (1975). Multidimensional binary search trees used for associative searching. Communications of the ACM, 18(9), 509-517.
2. Fodor, J.A. & Pylyshyn, Z.W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28(1-2), 3-71.
3. Fodor, J.A. (1994). J.A. Fodor. In S. Guttenplan (Ed.), A companion to the philosophy of mind. Oxford: Blackwell.
4. Gibson, J. J. (1979). The ecological approach to visua1 perception. Boston, MA: Houghton Mifflin.
5. Harnad, S. (1987). Category induction and representation. In S. Harnad (Ed.), Categorical perception: The groundwork of cognition. Cambridge: Cambridge University Press.
6. Harnad, S. (1990a). The symbol grounding problem. Physica D, 42(1-3), 335-346.
7. Harnad, S. (1993). Problems, problems: The frame problem as a symptom of the symbol grounding problem. Psycholoquy, 434). frame-problem.11.
8. MacDorman, K.F. (1992). First year report and thesis proposal. Computer Laboratory, Cambridge.
9. MacDorman, K. F. (1997). Symbol grounding: Learning categorical and sensorimotor predictions for coordination in autonomous robots. Technical Report No. 423. Computer Laboratory, Cambridge (e-mail librarian@cl.cam.ac.uk for a copy).
10. MacDorman, K. F. (1999). Grounding symbols through sensorimotor integration. Journal of the Robotics Society of Japan, 17(1), 20-24.
11. MacDorman, K. F. (1999). Partition Nets: An efficient on-line learning algorithm. ICAR 99: Ninth International Conference on Advanced Robotics, Tokyo.
12. Malcolm, C.M. (1995). The SOMASS system: A hybrid symbolic and behaviour-based system to plan and execute assemblies by robot. In J. Hallam, et al. (Eds.), Hybrid Problems, Hybrid Solutions, pp. 157-168. Oxford: ISO Press.
13. Moore, A. W. (1991). Variable resolution dynamic programming: Efficiently learning action maps in multivariate real-valued state-spaces. In L. Birnbaum & G. Collins (Eds.), Machine Learning: Proceedings of the Eighth International Workshop. San Mateo, CA: Morgan Kaufmann.
14. Moore, A. W. & Atkeson, C. G. (1995). The partigame algorithm for variable resolution reinforcement learning in multidimensional state-spaces. Machine Learning, 21(3), 199-233.
15. Nakawaki, D., Cisek, R., MacDorman, K. F., Joo, S., & Miyazaki, F. (1998). Coaching information determined from dynamic modeling based on a total energy analysis. 16th Annual Conference of the Robotics Society of Japan (vol. 1), September 18-20, 1998, Hokkaido University, pp. 45-6.
16. Newell, A. & Simon, H.A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.
17. Newell, A. (1980). Physical symbol systems. Cognitive Science, 4, 135-183.
18. Omohundro, S. M. (1991). Bumptrees for efficient function, constraint, and classification learning. In R. P. Lippmann, J. E. Moody, and D. S. Touretzky, Advances an Neural Information Processing Systems 3. San Mateo, CA: Morgan Kaufmann.
19. Rummelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representation by error propagation. In D. E. Rummelhart and J. L. McClelland (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition, Vol. 1, Ch.8. Cambridge, MA: MIT Press.
20. Schyns, P.G., Goldstone, R.L., & Thibaut, J.-P. (1998). The development of features in object concepts. Behavioral and Brain Sciences, 21(1), 1-17.
21. Sidgwick, R. (1998). Algorithms in C (3rd ed.). Reading, MA: Addison-Wesley.
22. Sproull, R. F. (1991). Refinements to nearest-neihbor searching in KD trees. Algorithmica, 6:47, 579-589.