In this work, I present a review on Sparse Distributed
Memory for Small Cues (SDMSCue), a variant of Sparse Distributed
Memory (SDM) that is capable of handling small cues. I then conduct
and show some cognitive experiments on SDMSCue to test its
cognitive soundness compared to SDM. Small cues refer to input
cues that are presented to memory for reading associations; but have
many missing parts or fields from them. The original SDM failed to
handle such a problem. SDMSCue handles and overcomes this
pitfall. The main idea in SDMSCue; is the repeated projection of the
semantic space on smaller subspaces; that are selected based on the
input cue length and pattern. This process allows for Read/Write
operations using an input cue that is missing a large portion.
SDMSCue is augmented with the use of genetic algorithms for
memory allocation and initialization. I claim that SDM functionality
is a subset of SDMSCue functionality.
[1] Anwar, Ashraf, ÔÇÿSparse Distributed Memory with Evolutionary
Mechanisms-, Proceedings of Genetic and Evolutionary Computation
Conference Workshop (GECCO), 1999, pp. 339-40.
[2] Anwar, Ashraf, Dasgupta, Dipankar, and Franklin, Stan, Using Genetic
Algorithms for Sparse Distributed Memory Initialization, Proceedings of
Congress on Evolutionary Computation (CEC), 1999.
[3] Anwar, Ashraf, and Franklin, Stan, Sparse Distributed Memory for
"Conscious" Software Agents, Cognitive Systems Research Journal,
UK: Elsevier, December 2003, v 4 n 4, pp. 339-54.
[4] Anwar, Ashraf, and Franklin, Stan, A Sparse Distributed Memory
Capable of Handling Small Cues, SDMSCue, IFIP International
Federation for Information Processing, USA: Springer
Science+Business Media Inc., Formerly Kluwer Boston Inc, 2005, vol.
172, pp. 23, ISSN: 1571-5736 (Paper) 1861-2288 (Online).
[5] Doyle, John, A Model for Deliberation, Action, and Introspection (AI
TR), USA: MIT, AI Lab, 1980.
[6] Evans, Richard, and Surkan, Alvin, Relating Number of Processing
Elements in a Sparse Distributed Memory Model to Learning Rate and
Generalization, APL Quote Quad, Aug 1991, v 21, n 4, pp. 166.
[7] Feldman, Robert S., Understanding Psychology, USA: McGraw Hill,
2005.
[8] Franklin, Stan, Artificial Minds, USA: MIT Press, 1995.
[9] Franklin, Stan, Autonomous Agents as Embodied AI, Cybernetics and
Systems Journal, special issue on Epistemological Issues in Embedded
AI, 1997.
[10] Franklin, Stan, and Graesser, Art, A Software Agent Model of
Consciousness, Consciousness and Cognition Journal, 1999, v 8, pp.
285-305.
[11] Franklin, Stan, Kelemen, Arpad, and McCauley, Lee, IDA: A Cognitive
Agent Architecture, IEEE Transactions on Systems, Man, and
Cybernetics, USA: IEEE, NJ, 1998.
[12] Glenberg, Arthur M., What Memory is for?, Behavioral and Brain
Sciences Journal, USA: Cambridge University Press, 1997.
[13] Hely, T., The Sparse Distributed Memory: A Neurobiologically
Plausible Memory Model?, Master's Thesis, UK: Edinburgh University,
Department of Artificial Intelligence, 1994.
[14] Ingrand, F. F., and Georgeff, M. P., Managing Deliberation and
Reasoning in Real-Time AI Systems, Proceedings of the Workshop on
Innovative Approaches to Planning, Scheduling and Control, pp. 284-91,
1990.
[15] Kanerva, Pentti, and Raugh, Michael, Sparse Distributed Memory,
RIACS, Annual Report, USA: NASA Ames Research Center, Moffett
Field, CA, 1987.
[16] Kanerva, Pentti, Sparse Distributed Memory, USA: MIT Press, 1988.
[17] Kanerva, Pentti, The Organization of an Autonomous Learning System,
USA: RIACS-TR-88, NASA Ames Research Center, Moffett Field, CA,
1988.
[18] Kanji, Gopal K., 100 Statistical Tests, USA: Sage Publications, 1999.
[19] Loftus, Geoffrey, and Loftus, Elizabeth, Human Memory, the Processing
of Information, USA: Lawrence Erlbaum Associates, 1976.
[20] Rao, Rajesh P. N., and Fuentes, Olac, Learning Navigational Behaviors
using a Predictive Sparse Distributed Memory, Proceedings of the 4th
international Conference on Simulation of Adaptive Behavior, From
Animals to Animats IV, 1996, pp. 382.
[21] Rao, Rajesh P. N., and Fuentes, Olac, Hierarchical Learning of
Navigation Behaviors in an Autonomous Robot using a Predictive
Sparse Distributed Memory, Machine Learning Journal, April 1998, v
31, n 1/3, pp. 87-113.
[22] Scott, E., Fuller, C., and O'Brien, W., Sparse Distributed Associative
Memory for the Identification of Aerospace Acoustic Sources, AIAA
Journal, September 1993, v 31, n 9, pp. 1583.
[23] Vogt, W. Paul, Dictionary of Statistics and Methodology, 2nd Edition,
USA: Sage Publications, 1998.
[1] Anwar, Ashraf, ÔÇÿSparse Distributed Memory with Evolutionary
Mechanisms-, Proceedings of Genetic and Evolutionary Computation
Conference Workshop (GECCO), 1999, pp. 339-40.
[2] Anwar, Ashraf, Dasgupta, Dipankar, and Franklin, Stan, Using Genetic
Algorithms for Sparse Distributed Memory Initialization, Proceedings of
Congress on Evolutionary Computation (CEC), 1999.
[3] Anwar, Ashraf, and Franklin, Stan, Sparse Distributed Memory for
"Conscious" Software Agents, Cognitive Systems Research Journal,
UK: Elsevier, December 2003, v 4 n 4, pp. 339-54.
[4] Anwar, Ashraf, and Franklin, Stan, A Sparse Distributed Memory
Capable of Handling Small Cues, SDMSCue, IFIP International
Federation for Information Processing, USA: Springer
Science+Business Media Inc., Formerly Kluwer Boston Inc, 2005, vol.
172, pp. 23, ISSN: 1571-5736 (Paper) 1861-2288 (Online).
[5] Doyle, John, A Model for Deliberation, Action, and Introspection (AI
TR), USA: MIT, AI Lab, 1980.
[6] Evans, Richard, and Surkan, Alvin, Relating Number of Processing
Elements in a Sparse Distributed Memory Model to Learning Rate and
Generalization, APL Quote Quad, Aug 1991, v 21, n 4, pp. 166.
[7] Feldman, Robert S., Understanding Psychology, USA: McGraw Hill,
2005.
[8] Franklin, Stan, Artificial Minds, USA: MIT Press, 1995.
[9] Franklin, Stan, Autonomous Agents as Embodied AI, Cybernetics and
Systems Journal, special issue on Epistemological Issues in Embedded
AI, 1997.
[10] Franklin, Stan, and Graesser, Art, A Software Agent Model of
Consciousness, Consciousness and Cognition Journal, 1999, v 8, pp.
285-305.
[11] Franklin, Stan, Kelemen, Arpad, and McCauley, Lee, IDA: A Cognitive
Agent Architecture, IEEE Transactions on Systems, Man, and
Cybernetics, USA: IEEE, NJ, 1998.
[12] Glenberg, Arthur M., What Memory is for?, Behavioral and Brain
Sciences Journal, USA: Cambridge University Press, 1997.
[13] Hely, T., The Sparse Distributed Memory: A Neurobiologically
Plausible Memory Model?, Master's Thesis, UK: Edinburgh University,
Department of Artificial Intelligence, 1994.
[14] Ingrand, F. F., and Georgeff, M. P., Managing Deliberation and
Reasoning in Real-Time AI Systems, Proceedings of the Workshop on
Innovative Approaches to Planning, Scheduling and Control, pp. 284-91,
1990.
[15] Kanerva, Pentti, and Raugh, Michael, Sparse Distributed Memory,
RIACS, Annual Report, USA: NASA Ames Research Center, Moffett
Field, CA, 1987.
[16] Kanerva, Pentti, Sparse Distributed Memory, USA: MIT Press, 1988.
[17] Kanerva, Pentti, The Organization of an Autonomous Learning System,
USA: RIACS-TR-88, NASA Ames Research Center, Moffett Field, CA,
1988.
[18] Kanji, Gopal K., 100 Statistical Tests, USA: Sage Publications, 1999.
[19] Loftus, Geoffrey, and Loftus, Elizabeth, Human Memory, the Processing
of Information, USA: Lawrence Erlbaum Associates, 1976.
[20] Rao, Rajesh P. N., and Fuentes, Olac, Learning Navigational Behaviors
using a Predictive Sparse Distributed Memory, Proceedings of the 4th
international Conference on Simulation of Adaptive Behavior, From
Animals to Animats IV, 1996, pp. 382.
[21] Rao, Rajesh P. N., and Fuentes, Olac, Hierarchical Learning of
Navigation Behaviors in an Autonomous Robot using a Predictive
Sparse Distributed Memory, Machine Learning Journal, April 1998, v
31, n 1/3, pp. 87-113.
[22] Scott, E., Fuller, C., and O'Brien, W., Sparse Distributed Associative
Memory for the Identification of Aerospace Acoustic Sources, AIAA
Journal, September 1993, v 31, n 9, pp. 1583.
[23] Vogt, W. Paul, Dictionary of Statistics and Methodology, 2nd Edition,
USA: Sage Publications, 1998.
@article{"International Journal of Information, Control and Computer Sciences:57393", author = "Ashraf Anwar", title = "Review and Experiments on SDMSCue", abstract = "In this work, I present a review on Sparse Distributed
Memory for Small Cues (SDMSCue), a variant of Sparse Distributed
Memory (SDM) that is capable of handling small cues. I then conduct
and show some cognitive experiments on SDMSCue to test its
cognitive soundness compared to SDM. Small cues refer to input
cues that are presented to memory for reading associations; but have
many missing parts or fields from them. The original SDM failed to
handle such a problem. SDMSCue handles and overcomes this
pitfall. The main idea in SDMSCue; is the repeated projection of the
semantic space on smaller subspaces; that are selected based on the
input cue length and pattern. This process allows for Read/Write
operations using an input cue that is missing a large portion.
SDMSCue is augmented with the use of genetic algorithms for
memory allocation and initialization. I claim that SDM functionality
is a subset of SDMSCue functionality.", keywords = "Artificial intelligence, recall, recognition, SDM,SDMSCue.", volume = "1", number = "10", pages = "3138-7", }
