-
Miller, George A. (1956),The magical number seven plus
minus two: some limits on our capacity for processing
information. Psychological
Review 63, 81-97.
-
Bechara, Antoine, Antonio R. Damasio, Hanna Damasio and S. Anderson (1994).
Insensitivity to future
consequences following damage to human prefrontal cortex. Cognition 50, 712.
-
Stadler M.A, Roediger H.L 3rd, McDermott K.B. (1999),Norms for word lists that
create False memories. Memory & Cognition, 27, 494-500.
-
Brainerd C.J., Wright R., Reyna V.F., Mojardin A.H. (2001),
Conjoint recognition and
phantom recollection. Journal of Experimental Psychology: Learning, Memory and
cognition 27, 341-361.
-
Roediger H.L 3rd, McDermott K.B. (1995),
Creating False Memories:
Remembering Words Not Presented in Lists, Journal of Experimental Psychology:
Learning, Memory and cognition 21(4), 803-814.
-
Xiaoyuan Tu (1996),
Artificial Animals for Computer
Animation: Biomechanics, Perception, Locomotion and Behavior, Ph.D. thesis,
Department of Computer Science,University of Toronto.
-
James Williams,The
Principles of Psychology,(New York: Holt, 1890).
-
Broadbent D.E.,Perception and Communication,
(London: Pergamon, 1958).
-
J. H. Piater,Visual Feature
Learning, Ph.D. diss., Dept. of Comp. Sc., (Univ. of Mass., Amherst, 2001).
-
Pabitra Mitra, C.A Murthy, and S. K. Pal,
Unsupervised
Feature Selection Using Feature Similarity, IEEE
Transactions on Pattern Ananlysis and Machine Intelligence, 2002, Vol.24(3),
pp.301-312.
-
Baddeley A.D.,Working Memory,(Oxford
Univ. Press, 1986).
-
Baddeley A. D., G. Hitch,
Working Memory, Recent
advances in learning and motivation, G. Bower(ed.),(NY : Academic Press, 1974).
-
D.H. Ballard, M.M. Hayhoe, Feng Li, S. D. Whitehead,
Hand-Eye Coordination during Sequential
Tasks, Phil. Trans.
R. Soc. London B, Vol.337, (1992), pp.331-339.
-
Van de Laar P., Heskes T., Gielen S.,
Task-Dependent
Learning of Attention, Neural Networks, (1997), Vol.10(6), pp.981-992.
-
Goncalves L.M., Giraldi G.N., Oliveira A.A. and Grupen R.A.,
Learning Policies for Attentional
Control, IEEE
International Symposium on Computational Intelligence on Robotics and
Automation, (Monterey, CA, 1999), pp.288-293.
-
A.R McCallum,
Learning to use selective attention
and short-term memory in sequential tasks, in Proc. of the 4th Int. Conf.
on Simulation of Aadaptive Behaviour, (Cambridge, MA, 1996), pp.315-324.
-
T.Jaakkola, S.P. Singh, and M. Jordan,
Reinforcement
learning algorithm for partially observable markov
decision problems, In Advances in Neural Information Processing
Systems, (Cambridge, MA, 1995), pp.345-352.
-
L.J. Lin and T. Mitchell,
Reinforcement learning with hidden
states, in Proc. of the 2nd Int. Conf. on Simulation of Adaptive Behaviour,
(1992), pp.271-280.
-
L.P. Kaelbling, M.L. Littman, and A.R. Cassandra,
Planning and acting in partially observable
stochastic
domains, Technical Report
CS-95-11, Brown Univ., (Providence RI,1995).
-
M.L. Littman, A.R. Cassandra, and L.P. Kaelbling,
Learning policies for partially observable
environments: Scalingup,
in Proc. of the 12th Int. Conf. on Machine Learning,(1995), pp.362-370.
-
A. R. McCallum,
Hidden state and reinforcement
learning with instance-based stateidentification,
in IEEE Trans. Sys. Man and Cybenetics, Part B., Vol.26(3), (1996), pp.464-473.
-
Huber M. and Grupen R.A.,A Feedback Control Structure for On-line Learning
Tasks, in Robotics and Autonomous Systems, Vol.22(3-4), 1997, 303-315.
-
J.G. Thistle, W.M. Wonham,
Control of infinite behavior
of finite automata, SIAM Journal of Control and Optimization, Vol.32(4),
(1994), pp.1075-1097.
-
P.J.G. Ramadge and W.M. Wonham,
The control of discrete
event systems, in Proc. of IEEE, (1989), Vol.77(1), pp.81-97.
-
C.J.C.H Watkins,
Learning From Delayed
Rewards, PhD thesis,Cambridge Univ., (Cambridge, England, 1989).
-
L.P. Kaelbling, M.L. Littman, A.M. Moore,
Reinforcement
Learning: A Survey, Journal of Artificial Intelligence Research, (1996),
Vol.4, pp.237-285
-
R.S. Sutton and A.G. Barto,Reinforcement learning: an
introduction, (Cambridge, MA: MIT Press, 1998).
-
Andrew W. Moore,
The party-game algorithm for
variable resolution reinforcement learning in multidimensional
state-spaces, Advances in Neural Information Processing Systems, (San
Mateo, CA, 1994), pp.711-718.
-
Andrew W. Moore,
Prioritized Sweeping: Reinforcement
Learning with Less Data and Less Time, Machine Learning,(1993), Vol.13(1),
pp.103-130.
-
Richard S. Sutton,Integrated architectures for learning,planning,
and reacting based on approximating dynamic
programming,in Proceedings of the Seventh International Conference on
Machine Learning, (Austin, Texas, 1990), pp.216-224.
-
Richard S. Sutton,
Planning by incremental dynamic
programming, in Proceedings of the Eighth International
Workshop on Machine Learning, pp.353-357.
-
Remi Coulom,
Reinforcement Learning Using Neural
Networks, with Applications to Motor Control, Ph.D. diss., (Institut
National Polytechnique de Grenoble, 2002).
-
Gerald Tesauro (1995),
Temporal difference learning and
TD-Gammon, In Communications of the ACM, Vol.38(3), pp.58-68.
-
Jean M. Mandler,
How to build a baby II
conceptualprimitives, Psychological
Review,(1992), Vol.99(4), pp.587-604.
-
J. Piaget,
The Origins of Intelligence in
Childhood, International University Press, 1952.