Self-Assembling Hypernetworks for Cognitive Learning of Linguistic Memory
Hypernetworks are a generalized graph structure
representing higher-order interactions between variables. We present a
method for self-organizing hypernetworks to learn an associative
memory of sentences and to recall the sentences from this memory.
This learning method is inspired by the “mental chemistry" model of
cognition and the “molecular self-assembly" technology in
biochemistry. Simulation experiments are performed on a corpus of
natural-language dialogues of approximately 300K sentences
collected from TV drama captions. We report on the sentence
completion performance as a function of the order of word-interaction
and the size of the learning corpus, and discuss the plausibility of this
architecture as a cognitive model of language learning and memory.
[1] Crowder, R. G., Principles of Learning and Memory, Lawrence Erlbaum,
1976.
[2] Feldman, J., From Molecules to Metaphor: A Neural Theory of Language,
MIT Press, 2006.
[3] Fuster, J., Cortex and Mind: Unifying Cognition, Oxford University Press,
2003.
[4] Grillner, S. and Graybiel, A. M. (Eds.) Microcircuits: The Interface
between Neurons and Global Brain Function, MIT Press, 2006.
[5] Kauffman, S., Origins of Order: Self Organization and Selection in
Evolution, Oxford University Press, 1993.
[6] Langley, P., "Cognitive architectures and general intelligent systems," AI
Magazine, pp. 33-44, Summer 2006.
[7] O-Reilly, R. C., "Biologically based computational models of high-level
cognition," Science, 314: 91-94, October 2006.
[8] Tse, D. et al., "Schemas and memory consolidation," Science 316: 76-82,
April 2007.
[9] Zhang, B.-T., "Cognitive learning and the multimodal memory game:
toward human-level machine learning," IEEE World Congress on
Computational Intelligence (WCCI-2008), 2007 (submitted).
[10] Zhang, B.-T. and Kim, J.-K., "DNA hypernetworks for information
storage and retrieval," Proc. 2006 Int. Annual Meeting on DNA
Computing (DNA12), LNCS 4287: 298-307, 2006.
[1] Crowder, R. G., Principles of Learning and Memory, Lawrence Erlbaum,
1976.
[2] Feldman, J., From Molecules to Metaphor: A Neural Theory of Language,
MIT Press, 2006.
[3] Fuster, J., Cortex and Mind: Unifying Cognition, Oxford University Press,
2003.
[4] Grillner, S. and Graybiel, A. M. (Eds.) Microcircuits: The Interface
between Neurons and Global Brain Function, MIT Press, 2006.
[5] Kauffman, S., Origins of Order: Self Organization and Selection in
Evolution, Oxford University Press, 1993.
[6] Langley, P., "Cognitive architectures and general intelligent systems," AI
Magazine, pp. 33-44, Summer 2006.
[7] O-Reilly, R. C., "Biologically based computational models of high-level
cognition," Science, 314: 91-94, October 2006.
[8] Tse, D. et al., "Schemas and memory consolidation," Science 316: 76-82,
April 2007.
[9] Zhang, B.-T., "Cognitive learning and the multimodal memory game:
toward human-level machine learning," IEEE World Congress on
Computational Intelligence (WCCI-2008), 2007 (submitted).
[10] Zhang, B.-T. and Kim, J.-K., "DNA hypernetworks for information
storage and retrieval," Proc. 2006 Int. Annual Meeting on DNA
Computing (DNA12), LNCS 4287: 298-307, 2006.
@article{"International Journal of Business, Human and Social Sciences:53465", author = "Byoung-Tak Zhang and Chan-Hoon Park", title = "Self-Assembling Hypernetworks for Cognitive Learning of Linguistic Memory", abstract = "Hypernetworks are a generalized graph structure
representing higher-order interactions between variables. We present a
method for self-organizing hypernetworks to learn an associative
memory of sentences and to recall the sentences from this memory.
This learning method is inspired by the “mental chemistry" model of
cognition and the “molecular self-assembly" technology in
biochemistry. Simulation experiments are performed on a corpus of
natural-language dialogues of approximately 300K sentences
collected from TV drama captions. We report on the sentence
completion performance as a function of the order of word-interaction
and the size of the learning corpus, and discuss the plausibility of this
architecture as a cognitive model of language learning and memory.", keywords = "Linguistic recall memory, sentence completion task,self-organizing hypernetworks, cognitive learning and memory.", volume = "2", number = "1", pages = "13-5", }