The role of associative processing in cognitive computing

Cognitive Computation

Volumn 1, Issue 1, 2009, Pages 42-49

  Haikonen, Pentti O A ^a  

^a NOKIA RESEARCH CENTER   (United States)

Author keywords

Associative processing; Machine cognition; Meaning; Ontology; Symbolic neural networks

Indexed keywords

ARTIFICIAL INTELLIGENCE AND NEURAL NETWORKS; ARTIFICIAL NEURAL NETWORK; HYBRID APPROACH; INFORMATION PROCESSING; MACHINE COGNITION; MEANING; PATTERN RECOGNITION AND CLASSIFICATION; REAL INTELLIGENCE; RULE BASED; SUB-SYMBOLIC;

ASSOCIATIVE PROCESSING; ONTOLOGY; PATTERN RECOGNITION;

NEURAL NETWORKS;

EID: 77549088295     PISSN: 18669956     EISSN: 18669964     Source Type: Journal    
DOI: 10.1007/s12559-009-9006-y     Document Type: Article

References (33)

1. Hebb DO. The organization of behavior. New York: Wiley; 1949.
2. Jain AK, Mao J, Mohiuddin KM. Artificial neural networks: a tutorial. Computer. 1996; 29(3): 31-44.
3. Mckenna TM. The role of interdisciplinary research involving neuroscience in the development of intelligent systems. In: Honavar V, Uhr L, editors. Artificial intelligence and neural networks: steps toward principled integration. USA: Academic Press; 1994. p. 75-92.
4. Searle JR. Minds, brains & science. London, England: Penguin Books Ltd; 1984.
5. Searle JR. The mystery of consciousness. London: Granta Books; 1997.
6. McGarry K, Wermter S, MacIntyre J. Hybrid neural systems: from single coupling to fully integrated neural networks. Neural Comput Surv. 1999; 2: 62-93.
7. Smolensky P, Legendre G, Miyata Y. Integrating connectionist and symbolic computation for the theory of language. In: Honavar V, Uhr L, editors. Artificial intelligence and neural networks: steps toward principled integration. USA: Academic Press; 1994. p. 509-30.
8. Kelley TD. Symbolic and sub-symbolic representations in computational models of human cognition. Theory Psychol. 2003; 13(6): 847-60.
9. Haikonen PO. An artificial cognitive neural system based on a novel neuron structure and a reentrant modular architecture with implications to machine consciousness. Dissertation for the degree of Doctor of Technology, Helsinki University of Technology, Applied Electronics Laboratory, Series B: Research Reports B4; 1999.
10. Haikonen PO. Robot brains, circuits and systems for conscious machines. UK: Wiley; 2007.
11. Haikonen PO. Towards streams of consciousness; implementing inner speech. In: The proceedings of AISB'06: adaptation in artificial and biological systems, vol 1. Bristol, UK: University of Bristol; 2006. p. 144-9.
12. Gruber TR. A translation approach to portable ontology specifications. Knowl Acquis. 1993; 5(2): 199-220 (http://tomgruber. org/writing/ontolingua-kaj-1993. htm).
13. Amigoni F, Neri MA. An application of ontology technologies to robotic agents. In: Proceedings of the 2005 IEEE/WIC/ACM international conference on intelligent agent technology. Compiegne, France, 19-22 September. IEEE Computer Society; 2005. p. 751-4.
14. Chella A, Cossentino M, Pirrone R, Ruisi A. Modeling ontologies for robotic environments. In: Proceedings of SEKE'2002, fourteenth international conference on software engineering and knowledge engineering. Ischia, Italy; 2002. p. 77-82.
15. Sato E, Hoshi S, Yamaguchi T. Humane robot system using ontological network. In: Proceedings of 2004 IEEE/RSJ international conference on intelligent robots and systems. 28 September-2 October. Sendai, Japan; 2004. p. 4038-43.
16. Costa PCG, Laskey KB, AlGhamdi G. Bayesian ontologies in AI systems. Paper presented at the fourth bayesian modelling applications workshop, held at the twenty-second conference on uncertainty in artificial intelligence (UAI 2006). 13 July. Cambridge, MA, USA; 2006.
17. Clancey WJ. The frame of reference problem in cognitive modeling. In: Proceedings of 11th annual conference of the cognitive science society. Ann Arbor: Lawrence Erlbaum Associates; 1989. p. 107-14.
18. Hinton GE, McClelland JL, Rumelhart DE. Distributed representations. In: Boden MA, editor. The philosophy of artificial intelligence. New York: Oxford University Press; 1990. p. 248-80.
19. Anderson JA. An introduction to neural networks. USA: MIT Press; 1995.
20. Pavlov IP. Conditional reflexes. New York: Dover Publications; 1927/1960.
21. Grossberg S. Pavlovian pattern learning by nonlinear neural networks. Proc Natl Acad Sci USA. 1971; 4: 828-31.
22. Vogel EH, Castro ME, Saavedra MA. Quantitative models of Pavlovian conditioning. Brain Res Bull. 2004; 63: 173-202.
23. Anderson JA. A simple neural network generating an interactive memory. Math Biosci. 1972; 14: 197-220.
24. Haque AL, Cheung JY. Preprocessing of the input vectors for the linear associator neural networks. In: The proceedings of IEEE international conference on neural networks, vol. 2; 1994. p. 930-3.
25. Hopfield JJ. Neural networks and physical systems with emergent collective computational properties. Proc Natl Acad Sci USA. 1982; 79: 2554-8.
26. Eliasmith C. Attractor network. Scholarpedia. 2007; 2(10): 1380.
27. Dodd W, Gutierrez R. The role of episodic memory and emotion in a cognitive robot. In: Proceedings of the IEEE international workshop on robot and human interactive communication (RO-MAN), Nashville, Tennessee, 13-15 August 2005; p. 692-7.
28. Chella A. Towards robot conscious perception. In: Chella A, Manzotti R, editors. Artificial consciousness. UK: Imprint Academic; 2007.
29. Chella A. Perception loop and machine consciousness. APA Newsletter on Philosophy and Computers. 2008; 2: 7-9.
30. Haikonen PO. The cognitive approach to conscious machines. UK: Imprint Academic; 2003.
31. Hesslow G, Jirenhed DA. The inner world of a simple robot. J Conscious Stud. 2007; 14: 85-96.
32. Miller GA. The magical number seven plus or minus two: some limits on our capacity for processing information. Psychol Rev. 1956; 63: 81-97.
33. Zwaan RA, Radvansky GA. Situation models in language comprehension and memory. Psychol Bull. 1998; 2: 162-85.