CPSC 420 Reading

Reading list for Undergraduate Artificial Intelligence

Instructions for writing paper reviews:

1. You should pick two papers from the list below (mostly available online -- just click on the title) and write reviews.
2. Some of the papers are very short (< 4 pages). You are welcome to pick those, but you may do so for only one of the two reviews. That is, at least one review should be for a longer paper.
3. If you want to read other papers related to AI which do not appear below, you should acquire my permission.
4. The reviews have to be in a particular format (TBA). Only typed hardcopies will be accepted (no handwriting please). Detailed submission instructions TBA.
5. The deadline for the submission of the reviews are indicated in the weekly schedule.
6. If you cannot decide which paper to read, please come see me during my office hour or send me email. (A good strategy would be to look at the Broader Perspectives category at the top of the list first.)

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.