Modeling affordances using Bayesian networks

IEEE International Conference on Intelligent Robots and Systems

Volumn , Issue , 2007, Pages 4102-4107

  Montesano, Luis ^a   Lopes, Manuel ^a   Bernardino, Alexandre ^a   Santos Victor, Jose ^a  

^a INSTITUTO SUPERIOR TÉCNICO   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER NETWORKS; DISTRIBUTED PARAMETER NETWORKS; INFERENCE ENGINES; INTELLIGENT NETWORKS; INTELLIGENT ROBOTS; INTELLIGENT SYSTEMS; KNOWLEDGE BASED SYSTEMS; ROBOTICS; SPEECH ANALYSIS;

INTERNATIONAL CONFERENCES; ROBOTIC SYSTEMS;

BAYESIAN NETWORKS;

EID: 51349153304     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2007.4399511     Document Type: Conference Paper

References (25)

1. J. J. Gibson, The Ecological Approach to Visual Perception. Boston: Houghton Mifflin, 1979.
2. S. Schaal, "Is imitation learning the route to humanoid robots," Trends in Cognitive Sciences, vol. 3(6), 1999.
3. L. Montesano, M. Lopes, A. Bernardino, and J. Santos-Victor, "Af-fordance, development and imitation," in International Conference on Development and Learning, 2007.
4. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.
5. D. K. N. Friedman, L. Getoor and A. Pfeffer, "Learning probabilistic relational models," in International Joint Conferences on Artificial Intelligence, 1999.
6. A. Chemero, "An outline of a theory of affordances," Ecological Psychology, vol. 15, no. 2, pp. 181-195, 2003.
7. J. Konczak, H. Meeuwsen, and M. Cress, "Changing affordances in stair climbing: The perception of maximum climbability in young and older adults," Journal of Experimental Psychology: Human Perception & Performance, vol. 19, pp. 691-7, 1992.
8. E. Symes, R. Ellis, and M. Tucker, "Visual object affordances: Object orientation," Acta Psychologica, vol. 124, no. 2, pp. 238-255, 2007.
9. M. Turvey, K. Shockley, and C. Carello, "Affordance, proper function, and the physical basis of perceived heaviness," Cognition, vol. 73, 1999.
10. E. Oztop, N. Bradley, and M. Arlib. "Infant grasp learning: a computational model," Experimental Brain Research, vol. 158, pp. 480-503, 2004.
11. M. Lopes and J. Santos-Victor, "A developmental roadmap for learning by imitation in robots," IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 37, no. 2, April 2007.
12. A. Slocum, D. Downey, and R. Beer, "Further experiments in the evolution of minimally cognitive behavior: from perceiving affordances to selective attention," in Conference on simulation of adaptive behavior, Paris, France, 2000.
13. P. Fitzpatrick, G. Metta, L. Natale, S. Rao, and G. Sandini., "Learning about objects through action: Initial steps towards artificial cognition," in IEEE International Conference on Robotics and Automation, Taipei, Taiwan, 2003.
14. H. Kozima, C. Nakagawa, and H. Yano, "Emergence of imitation mediated by objects," in Second International Workshop on Epigenetic Robotics, Edinburgh, Scotland, 2002.
15. I. Cos-Aguilera, L. Cañamero, and G. Hayes, "Using a SOFM to learn object affordances," in Workshop of Physical Agents (WAF), Girona, Spain, 2004.
16. G. Fritz, L. Paletta, R. Breithaupt, E. Rome, and G. Dorffner, "Learning predictive features in affordance based robotic perception systems," in IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China, 2006.
17. A. Stoytchev, "Behavior-gorunded representation of tool affordances." in International Conference on Robotics and Automation, Barcelona, Spain, 2005.
18. J. Pearl, Causality: Models, Reasoning and Inference. Cambridge University Press, 2000.
19. C. Huang and A. Darwiche, "Inference in belief networks: A procedural guide," International Journal of Approximate Reasoning, vol. 15, no. 3, pp. 225-263, 1996.
20. D. Heckerman, D. Geiger, and M. Chickering, "Learning bayesian networks: the combination of knowledge and statistical data," Machine Learning, 1995.
21. G. Cooper and E. Herskovits, "A bayesian method for the induction of probabilistic networks from data," Machine Learning, vol. 9, no. 4, pp. 309-347, 1992.
22. D. Madigan and I. York, "Bayesian graphical models for discrete data," Intl. Statistical Review, vol. 63, pp. 215-232, 1995.
23. D. Eaton and K. Murphy, "Exact bayesian structure learning from uncertain interventions," AI & Statistics, In Press.
24. D. Heckerman, "A tutorial on learning with bayesian networks," in In M. Jordan, editor, Learning in graphical models. MIT Press, 1998.
25. D. Pelleg and A. W. Moore, "X-means: Extending k-means with efficient estimation of the number of clusters," in ICML '00, San Francisco, CA, USA, 2000.