Cognitive high level information fusion

Information Sciences

Volumn 177, Issue 10, 2007, Pages 2099-2118

  Perlovsky, Leonid I ^a  

^a HARVARD UNIVERSITY   (United States)

Author keywords

Communications; Computational linguistics; Concepts; Dynamic logic; Emotions; Evolving systems; Fusion; Mind; Sensors; Symbols

Indexed keywords

ABSTRACTING; CODES (SYMBOLS); COGNITIVE SYSTEMS; COMMUNICATION SYSTEMS; COMPUTATIONAL LINGUISTICS; DATA PROCESSING; DATA REDUCTION; MATHEMATICAL MODELS;

CONCEPTS; DYNAMIC LOGIC; EMOTIONS; EVOLVING SYSTEMS; MIND;

SENSOR DATA FUSION;

EID: 33847656263     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2006.12.026     Document Type: Article

References (72)

1. The American Heritage College Dictionary, third ed., Houghton Mifflin, Boston, MA, 2000.
2. Aristotle, IV BC, Metaphysics, tr. W.D. Ross, Complete Works of Aristotle, Ed. J. Barnes, Princeton, NJ, 1995.
3. Bellman R.E. Adaptive Control Processes (1961), Princeton University Press, Princeton, NJ
4. E.P. Blasch, S. Plano, Level 5: User Refinement to aid the Fusion Process, in: B. Dasarathy (Ed.), Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications, in: SPIE Proc., Vol. 5099. 2003, pp. 288-297.
5. A.-C. Boury-Brisset, Ontology-based approach for information fusion, in: Proceedings of the Sixth International Conference on Information Fusion, 2003.
6. Bossé E., Valin P., Boury-Brisset A., and Grenier D. Exploitation of a priori knowledge for information fusion. Inf. Fus. 7 2 (2006) 161-175
7. G.T. Capraro, R.A. Liuzzi, M.C. Wicks, Artificial Intelligence and Sensor Fusion, in: Proc. of Int. Conf. On Integration of Knowledge Intensive Multi-Agent Syst., Cambridge, MA, 2003, pp. 591-595.
8. Carpenter G.A., and Grossberg S. A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput Vis. Graphics Image Process 37 (1987) 54-115
9. N. Chomsky, Language and Mind, Harcourt Brace Javanovich, New York, NY, 1972.
10. Chomsky N. Principles and Parameters in Syntactic Theory. In: Hornstein N., and Lightfoot D. (Eds). Explanation in Linguistics, The Logical Problem of Language Acquisition (1981), Longman, London
11. A.R. Damasio, Descartes' Error: Emotion, Reason, and the Human Brain, Avon, New York, NY, 1995.
12. Dasarathy B. Sensor Fusion Potential Exploitation-Innovative Architectures and Illustrative Applications. IEEE Proc. 85 1 (1997) 24-38
13. Deacon T.W. The Symbolic Species: The Co-Evolution of Language and the Brain (1998), W.W. Norton & Company
14. R. Deming, L.I. Perlovsky, Sensor fusion problem for multiple UAVs. In: Proc. of Int. Conf. On Integration of Knowledge Intensive Multi-Agent Sys., April 2005, Waltham, MA, pp. 18-21.
15. V.A. Dmitriev, L.I. Perlovsky, Art Form as an Object of Cognitive Modeling: Towards Development of Vygotsky's Semiotics Model. In: Proc. of the Conf. on Intelligent Systems and Semiotics '96, 2, Gaithersburg, MD, 1996, pp. 385-389.
16. M.R. Endsley, Theoretical underpinnings of Situation Awareness: A Critical Review, in: Situation Awareness Analysis and Measurement, in: Endsley, Garland, Lawrence Erlbaum Associates Inc., Mahwah, NJ, 2000, pp. 3-32.
17. Freeman W.J. Mass action in the nervous system (1975), Academic Press, New York, NY
18. Grossberg S. Neural Networks and Natural Intelligence (1988), MIT Press, Cambridge, MA
19. Grossberg S. Linking mind to brain: the mathematics of biological intelligence. Notices Am. Math. Soc. (2000) 471361-471372
20. Grossberg S., and Levine D.S. Neural dynamics of attentionally modulated Pavlovian conditioning: blocking, inter-stimulus interval, and secondary reinforcement. Psychobiology 15 3 (1987) 195-240
21. Grossberg S., and Schmajuk N.A. Neural dynamics of attentionally modulated Pavlovian conditioning: Conditioned reinforcement, inhibition, and opponent processing. Psychobiology 15 3 (1987) 195-240
22. D.L. Hall, R.J. Linn, A taxonomy of Algorithms for Multisensor Data Fusion, in: Proc. Tri-Service Data-Fusion Symp., 1990, pp. 13-29.
23. D.L. Hall, S.A.H. McMullen, Mathematical Techniques in Multisensor Data Fusion, second Eds., Artech House, 2004, .
24. Jackendoff R. Foundations of Language: Brain, Meaning, Grammar, Evolution (2002), Oxford University Press, New York, NY
25. C.G. Jung, Archetypes of the Collective Unconscious, in: the Collected Works, v. 9, II, Bollingen Series XX, 1969, Princeton University Press, Princeton, NJ, 1934.
26. I. Kant, Critique of Pure Reason, 1781. Tr. J.M.D. Meiklejohn, Willey Book, New York, NY, 1943.
27. I. Kant, Critique of Judgment, 1790, Tr. J.H. Bernard, second ed., Macmillan and Co., London, 1914.
28. I. Kant, Critique of Practical Reason, 1788. Tr. J.H Bernard, Hafner Press-Macmillan, New York, 1986.
29. Kecman V. Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems) (2001), The MIT Press, Cambridge, MA
30. In: Koch C., and Segev I. (Eds). Methods in Neuronal Modeling: From Ions to Networks (1998), MIT Press, Cambridge, MA
31. Hebb D. Organization of Behavior (1949), J. Wiley and Sons, New York, NY
32. R. Linnehan, C. Mutz, L.I. Perlovsky, B. Weijers, J. Schindler, R. Brockett, Detection of Patterns Below Clutter in Images, in: Proc. of Int. Conf. on Integration of Knowledge Intensive Multi-Agent Systems, Cambridge, MA, October 1-3, 2003.
33. B. McGuinness, L. Foy, A subjective measure of SA: The Crew Awareness Rating Scale (CARS), in: Proc. of First Human Performance, Situation Awareness, and Automation Conf., Savannah, GA, 2000.
34. A. Mehler, A multiresolutional approach to fuzzy text meaning. Intelligent systems: a semiotic perspective, in: Proc. Int. Multidisciplinary Conf. 1., 1996, pp. 261-273.
35. A. Mehler, Hierarchical Orderings of Textual Units, in: Proc. of the 19th Int. Conf. on Computational Linguistics, COLING 2002, Morgan Kaufmann, Taipei, San Francisco, 2002, pp. 646-652.
36. Mehler A. Components of a Model of Context-Sensitive Hypertexts. J. Universal Comput. Sci. (JUCS) 8 10 (2002) 924-943
37. A. Mehler, Aspects of Informational Uncertainty in Web Mining, in: Proc. of Japanese-German Workshop on Natural Language Processing (JGWS'05), Schloss Eckberg, Dresden, Germany, 2005.
38. Meystel A.M., and Albus J.S. Intelligent Systems: Architecture, Design, and Control (2001), Wiley, New York
39. Minsky M.L. Semantic Information Processing (1968), The MIT Press, Cambridge, MA
40. Newell A. Intellectual issues in the history of artificial intelligence. In: Machlup F., and Mansfield U. (Eds). The study of information (1983), J. Wiley, New York, NY
41. L.I. Perlovsky, Gödel Theorem and Semiotics, in: Proc. of the Conf. on Intelligent Systems and Semiotics '96. Vol. 2. Gaithersburg, MD, 1996, pp. 14-18.
42. Perlovsky L.I. Mathematical Concepts of Intellect. Proc. World Congress on Neural Networks (1996), Lawrence Erlbaum Associates, NJ, San Diego, CA
43. Perlovsky L.I. Cramer-Rao Bound for Tracking in Clutter and Tracking Multiple Objects. Pattern Recognition Lett. 18 3 (1997) 283-288
44. Perlovsky L.I. Physical Concepts of Intellect. Proc. Russian Academy of Sciences 354 3 (1997) 320-323
45. L.I. Perlovsky, Cyberaesthetics: aesthetics, learning, and control, STIS'98 Gaithersburg, MD, 1998.
46. Perlovsky L.I. Conundrum of Combinatorial Complexity. IEEE Trans. PAMI 20 6 (1998) 666-670
47. L.I. Perlovsky, Emotions, Learning, and Control, in: Proc. Int Symp. Intell. Control, Intelligent Systems & Semiotics, Cambridge MA, 1999, pp. 131-137.
48. L.I. Perlovsky, Physical Theory of Information Processing in the Mind: concepts and emotions, in: Proc. of SEE Conf. on The Integration of Information Processing, Toronto, 2001.
49. Perlovsky L.I. Neural Networks and Intellect: using model-based concepts (2001), Oxford University Press, New York, NY
50. L.I. Perlovsky, Fusion of Language and Thought Processes for CTS. Keynote Presentation at Collaborative Technologies and Systems Conference 2003, Orlando, Florida, 2003.
51. Perlovsky L.I. Evolving Agents: Communication and Cognition. In: Gorodetsky V., Liu J., and Skormin V.A. (Eds). Autonomous Intelligent Systems: Agents and Data Mining (2005), Springer-Verlag GmbH 37-49
52. Perlovsky L.I. Modeling Field Theory of Higher Cognitive Functions. In: Loula A., Gudwin R., and Queiroz J. (Eds). Artificial Cognition Systems (2006), Idea Group, Hershey, PA 64-105
53. Perlovsky L.I. Symbols: Integrated Cognition and Language. In: Loula A., Gudwin R., and Queiroz J. (Eds). Semiotics and Intelligent Systems Development (2006), Idea Group, Hershey, PA 121-151
54. Perlovsky L.I. Fuzzy Dynamic Logic. New Math. Natural Comput. 2 1 (2006) 43-55
55. Perlovsky L.I. Toward Physics of the Mind: Concepts, Emotions, Consciousness, and Symbols. Phys. Life Rev. 3 1 (2006) 22-55
56. L.I. Perlovsky, The Knowledge Instinct, Basic Books, New York, NY, in press.
57. Perlovsky L.I., and McManus M.M. Maximum Likelihood Neural Networks for Sensor Fusion and Adaptive Classification. Neural Networks 4 1 (1991) 89-102
58. L.I. Perlovsky, C. Mutz, B. Weijers, R. Linnehan, J. Schindler, R. Brockett, Detection of Patterns Below Clutter in Images, in: Int. Conf. On Integration of Knowledge Intensive Multi-Agent Systems, Cambridge, MA, October 1-3, 2003.
59. Perlovsky L.I., Schoendorf W.H., Tye D.M., and Chang W. Concurrent Classification and Tracking Using Maximum Likelihood Adaptive Neural System. J. Underwater Acoust. 45 2 (1995) 399-414
60. Perlovsky L.I., Schoendorf W.H., Garvin L.C., and Chang W. Development of Concurrent Classification and Tracking for Active Sonar. J. Underwater Acoust. 47 2 (1997) 375-388
61. J. Piaget, The Psychology of the Child, Tr. H. Weaver, Basic Books, 2000.
62. Pinker S. The Language Instinct: How the Mind Creates Language (2000), Harper Perennial
63. Pinker S. Words and Rules: The Ingredients of Language (2000), Harper Perennial
64. Pribram K. Languages of the Brain (1971), Prentice-Hall, Englewood Cliffs, NJ
65. B.B. Rieger, Empirical Semantics II, in: A Collection of New Approaches in the Field, Quantitative Linguistics, Vol. 13., Brockmeyer, Bochum, Germany, 1981.
66. B.B. Rieger, Situational semantics and computational linguistics: towards informational ecology. in: Kornwachs, Jacoby (Eds.), Information. New questions to a multidisciplinary concept, Berlin, Academie, Germany, 1995.
67. B.B. Rieger, Tree-like Dispositional Dependency Structures for non-propositional Semantic Inferencing. in: Bouchon-Meunier/Yager, (Eds.), in: Proc. of IPMU-98, Paris (EKD), 1998, pp. 351-358.
68. J. Salerno, M. Hinman, D. Boulware, Building a framework for situational Awareness, in: Proc. of the 7th Int. Conf. on Information Fusion, Stockholm, Sweden, 2004, pp. 219-226.
69. Solomonoff R.J. A Formal Theory of Inductive Inference. Inform. Control. Part I 7 1 (1964) 1-22
70. Westfall R.S. Never at Rest (1981), Indiana University Press, IN
71. F.E. White, A Model for Data Fusion, in: Proc. 1st National Symp. on Sensor Fusion, Laurel, Maryland, 1988.
72. Zeki S. A Vision of the Brain (1993), Blackwell, Oxford, England