CPSC 420 Reading

Reading list for Undergraduate Artificial Intelligence

Broader Prespectives

• Peter Cariani. Symbols and dynamics in the brain. Biosystems, 60:59-83, 2001.

• Anthony J. Bell. Levels and loops: The future of artificial intelligence and neuroscience. Philosophical Transactions of the Royal Society of London, 354:2013-2020, 1999.

• Tom Ziemke. Rethinking grounding. In A. Riegler, A. von Stein, and M. Peschl, editors, Understanding Representation in the Cognitive Sciences: Does Representation Need Reality?, pages 177-199. Kluwer Academic/Plenum Press, New York, 1999.

• Richard Langlois and Rierre Garrouste. Cognition, redundancy, and learning in organizations. Economics of Innovation and New Technology, 4:287-299, 1997.

• Rolf Pfeifer and Christian Scheier. Sensory-motor coordination: The metaphor and beyond. Robotics and Autonomous Systems, 20:157-178, 1997.

Robotics

• Sebastian Thrun. Probabilistic algorithms in robotics. AI Magazine, 21:93-109, 2000.

• Ralf Salomon. Achieving robust behavior by using proprioceptive activity patterns. Biosystems, 47:193-206, 1998.

• Rolf Pfeifer and Christian Scheier. Sensory-motor coordination: The metaphor and beyond. Robotics and Autonomous Systems, 20:157-178, 1997.

• Tim Smithers. Autonomy in robots and other agents. Brain and Cognition, 34:88-106, 1997.

Symbol Grounding and Natural Semantics

• Yoonsuck Choe and Noah H. Smith. Motion-based autonomous grounding: Inferring external world properties from internal sensory states alone. In Yolanda Gil and Raymond Mooney, editors, Proceedings of the 21st National Conference on Artificial Intelligence, 2006. In press.

• Peter Cariani. Symbols and dynamics in the brain. Biosystems, 60:59-83, 2001.

• Paul R. Cohen and Carole R. Beal. Natural semantics for a mobile robot. Technical Report 00-59, University of Massachusettes, Department of Computer Science, 2000.

• Karl F. MacDorman, Koji Tatani, Yoji Miyazaki, and Masanao Koeda. Proto-symbol emergence. In Proceedings of the 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1619-1625, 2000.

• Tom Ziemke. Rethinking grounding. In A. Riegler, A. von Stein, and M. Peschl, editors, Understanding Representation in the Cognitive Sciences: Does Representation Need Reality?, pages 177-199. Kluwer Academic/Plenum Press, New York, 1999.

• Stevan Harnad. The symbol grounding problem. Physica D, 42:335-346, 1990.

Bayesian Perception

• Clark Glymour. Learning, prediction and causal bayes nets. Trends in Cognitive Sciences, 7:43-48, 2003.

• D. C. Knill, D. Kersten, and A. Yuille. Introduction: A bayesian formulation of visual perception. In D. C. Knill and W. Richards, editors, Perception as Bayesian Inference, chapter 0, pages 1-22. Cambridge University Press, 1996.

Imitation and Mirror Neurons

• R. P. N. Rao, A. P. Shon, and A. N. Meltzoff. A bayesian model of imitation in infants and robots. Cambridge University Press, Cambridge, UK, 2004. In press.

• Giacomo Rizzolatti, Leonardo Fogassi, and Vittorio Gallese. Neurophysiological mechanisms underlying the understanding and imitation of action. Nature Reviews Neuroscience, 2:661-670, 2001.

• Simon Dennis. Behaviour with an implicit teacher in connectionist networks. Available online at http://citeseer.nj.nec.com/158522.html.

Active Perception

• D. Philipona, J. K. O'Regan, and J.-P. Nadal. Is there something out there? Inferring space from sensorimotor dependencies. Neural Computation, 15:2029-2050, 2003.

• Gösta H. Granlund. Does vision inveitably have to be active?. In Proceedings of the 11th Scandinavian Conference on Image Analysis, pages 11-19, 1999.

• Ralf Möller. Perception through anticipation: An approach to behavior-based perception. In Proceedings of New Trends in Cognitive Science, pages 184-190, 1997.