From pixels to policies: A bootstrapping agent

2008 IEEE 7th International Conference on Development and Learning, ICDL

Volumn , Issue , 2008, Pages 103-108

  Stober, Jeremy ^a   Kuipers, Benjamin ^a  

^a University of Texas at Austin   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTONOMOUS LEARNING; BINARY VARIABLES; CONTINUOUS TRACKING; DISCRETE STATE; EFFECTIVE LEARNING; EMBODIED AGENT; PIXEL LEVEL; SCIENTIFIC DISCOVERY; SENSORY INTERFACES; SIMPLIFIED MODELS; TEMPORAL DIFFERENCE LEARNING; THREE-VALUED; TIME VARYING; TRACKING OBJECTS;

EDUCATION;

PIXELS;

EID: 67650122131     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DEVLRN.2008.4640813     Document Type: Conference Paper

References (25)

1. D. Pierce and B. Kuipers, "Map learning with uninterpreted sensors and effectors," Artificial Intelligence, vol. 92, no. 1-2, pp. 169-227, 1997.
2. L. Olsson, C. Nehaniv, and D. Polani, "From unknown sensors and actuators to actions grounded in sensorimotor perceptions," Connection Science, vol. 18, no. 2, pp. 121-144, 2006.
3. Z. Pylyshyn, "The Role of Location Indexes in Spatial Perception:A Sketch of the FINST Spatial Index Model," Cognition, vol. 32, pp. 65-97, 1989.
4. S. Coradeschi and A. Saffiotti, "An introduction to the anchoring problem," Robotics and Autonomous Systems, vol. 43, no. 2-3, pp. 85-96, 2003.
5. J. Modayil and B. Kuipers, "Autonomous development of a grounded object ontology by a learning robot," in Proceedings of the Twenty-Second National Conference on Artificial Intelligence (AAAI-07), 2007.
6. P. Langley, H. Simon, G. Bradshaw, and J. Zytkow, Scientific Discovery: Computational Explorations of the Creative Processes. MIT Press, 1987.
7. B. Kuipers, Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge. MIT Press, 1994.
8. A. Yilmaz, O. Javed, and M. Shah, "Object tracking: A survey," ACM Computing Surveys (CSUR), vol. 38, no. 4, 2006.
9. R. Sutton and A. Barto, Reinforcement Learning: An Introduction. MIT Press, 1998.
10. L. Kaelbling, M. Littman, and A. Moore, "Reinforcement Learning: A Survey," Journal of Artificial IntelligenceResearch, vol. 4, pp. 237-285, 1996.
11. W. Snyder and H. Qi, Machine Vision. Cambridge University Press, 2004.
12. F. Linaker and L. Niklasson, "Sensory Flow Segmentation using a Resource Allocating Vector Quantizer," Advances in Pattern Recognition: Joint IAPR International Workshops SSPR2000 and SPR2000, pp. 853-862, 2000.
13. B. Fritzke, "A growing neural gas network learns topologies," Advances in Neural Information Processing Systems, vol. 7, pp. 625-632, 1995.
14. J. Platt, "A resource-allocating network for function interpolation," Neural Computation, vol. 3, no. 2, pp. 213-225, 1991.
15. L. Li, T. Walsh, and M. Littman, "Towards a unified theory of state abstraction for MDPs," Proceedings of the ninth international symposium on AI and mathematics, 2006.
16. J. Albus et al., "A new approach to manipulator control: The cerebellar model articulation controller (CMAC)," Journal of Dynamic Systems, Measurement and Control, vol. 97, no. 3, pp. 220-227, 1975.
17. R. S. Sutton, "Generalization in reinforcement learning: Successful examples using sparse coarse coding," Advances in Neural Information Processing Systems, vol. 8, pp. 1038-1044, 1996.
18. D. Michie and R. A. Chambers, "BOXES: An Experiment in Adaptive Control," in Machine Intelligence 2. Edinburgh University Press, 1968, pp. 137-152.
19. A. Barto, R. Sutton, and C. Anderson, "Neuronlike adaptive elements that can solve difficult learning control problems," IEEE Computer Society Neural Networks Technology Series, pp. 81-93, 1990.
20. A. McCallum, "Reinforcement Learning with Selective Perception and Hidden State," Ph.D. dissertation, University of Rochester, 1996.
21. U. Fayyad and K. Irani, "Multi-interval discretization of continuousvalued attributes for classification learning," Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, vol. 2, 1993.
22. J. Mugan and B. Kuipers, "Learning distinctions and rules in a continuous world through active exploration," in 7th International Conference on Epigenetic Robotics (Epirob-07), 2007.
23. P. Langley, G. Bradshaw, and H. Simon, "Rediscovering chemistry with the BACON system," Machine Learning: A Multistrategy Approach, 1994.
24. A. Moore and C. Atkeson, "The parti-game algorithm for variable resolution reinforcement learning in multidimensional state-spaces," Machine Learning, vol. 21, no. 3, pp. 199-233, 1995.
25. D. Chapman and L. Kaelbling, "Input generalization in delayed reinforcement learning: An algorithm and performance comparisons," Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, pp. 726-731, 1991.