A wistech paradigm for intelligent systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4374 LNCS, Issue PART 1, 2007, Pages 94-132

  Jankowski, Andrzej ^a,b   Skowron, Andrzej ^c  

^a Instytut Wspomagania Procesow Decyzyjnych Sp z o o Warszawa   (Poland)

^b Institute of Decision Processes Support and AdgaM Solutions Sp Z Oo   (Poland)

^c UNIVERSITY OF WARSAW   (Poland)

Author keywords

Adaptive rough granular computing; Open innovation; Rough sets; Wisdom engine; Wisdom network; Wisdom technology

Indexed keywords

ADAPTIVE SYSTEMS; COMPUTATIONAL METHODS; PROBLEM SOLVING; ROUGH SET THEORY; SYSTEMS ANALYSIS;

ADAPTIVE ROUGH-GRANULAR COMPUTING; OPEN INNOVATION; WISDOM ENGINE; WISDOM NETWORK; WISDOM TECHNOLOGY;

INTELLIGENT SYSTEMS;

EID: 39149139663     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-71200-8_7     Document Type: Conference Paper

References (123)

1. G. Antoniou, F. van Harmelen, A Semantic Web Primer (Cooperative Information Systems) The MIT Press, 2004.
2. R. Axelrod, The Complexity of Cooperation, Princeton, NJ: Princeton University Press, 1997.
3. A. Bargiela, W. Pedrycz, Granular Computing: An Introduction, Dordrecht: Kluwer Academic Publishers, 2003.
4. J. Barwise, J. Seligman, Information Flow: The Logic of Distributed Systems, Cambridge University Press, 1997.
5. J. G. Bazan, J. F. Peters, and A. Skowron, Behavioral pattern identification through rough set modelling, in [71], pp. 688-697.
6. J. Bazan, A. Skowron, On-line elimination of non-relevant parts of complex objects in behavioral pattern identification, in [111], pp. 720-725.
7. J. Bazan, A. Skowron, Classifiers based on approximate reasoning schemes, in Monitoring, Security, and Rescue Tasks in Multiagent Systems (MSRAS 2004), B. Dunin-Keplicz, A. Jankowski, A. Skowron, and M. Szczuka, Eds., Advances in Soft Computing, pp. 191-202, Heidelberg: Springer, 2005.
8. J. Bazan, A. Skowron, R. Swiniarski, Rough sets and vague concept approximation: From sample approximation to adaptive learning, Transactions on Rough Sets V: Journal Subline, Lecture Notes in Computer Science, vol. 3100, pp. 39-63, Heidelberg: Springer, 2006.
9. R. Baeza-Yates, B. Ribeiro-Neto, Modern Information Retrieval, Addison Wesley, 1999.
10. M. W. Berry, Survey of Text Mining : Clustering, Classification, and Retrieval, Springer, 2003.
11. R. Brachman, H. Levesque, Knowledge Representation and Reasoning, Morgan Kaufmann, 2004.
12. L. Breiman, Statistical modeling: The two Cultures, Statistical Science 16(3) (2001) 199-231.
13. S. Brin, L. Page, The Anatomy of a Large-Scale Hypertextual Web Search Engine, Stanford University, 1998.
14. C. Carpineto, G. Romano, Concept Data Analysis: Theory and Applications, John Wiley & Sons, 2004.
15. N. L. Cassimatis, A cognitive substrate for achieving human-level intelligence, AI Magazine 27(2) (2006) 45-56.
16. N. L. Cassimatis, E. T. Mueller, P. H. Winston, Achieving human-level intelligence through integrated systems and research, AI Magazine 27(2) (2006) 12-14.
17. S. Chakrabarti, Mining the Web: Analysis of Hypertext and Semi Structured Data, The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann, 2002.
18. Mu-Y. Chen, An-P Chen, Knowledge management performance evaluation: a decade review from 1995 to 2004, Journal of Information Science 32 (1) 2006 17-38.
19. H. W. Chesbrough, Open Innovation: The New Imperative for Creating and Profiting from Technology, Cambridge MA: Harvard Business School Publishing, 2003.
20. J. Coleman, Introducing Speech and Language Processing (Cambridge Introductions to Language and Linguistics), Cambridge University Press, 2005.
21. A. Desai, Adaptive complex enterprises, Communications ACM 48(5) (2005) 32-35.
22. P. Doherty, W. Lukaszewicz, A. Skowron, and A. Szalas, Knowledge Engineering: A Rough Set Approach, Studies in Fuzziness and Soft Computing, vol. 202, Heidelberg: Springer, 2006.
23. R. Dornfest, T. Calishain, Wistech Network Hacks O'Reilly Media, Inc.; 2004.
24. R. Duda, P. Hart, and R. Stork, Pattern Classification, New York, NY: John Wiley & Sons, 2002.
25. B. Dunin-Kȩplicz, A. Jankowski, A. Skowron, M. Szczuka, Monitoring, Security, and Rescue Tasks in Multiagent Systems (MSRAS'2004), Series in Soft Computing, Heidelberg: Springer, 2005.
26. A. E. Eiben, J. E. Smith, Introduction to Evolutionary Computing, Natural Computing Series, Springer, 2003.
27. E. Feigenbaum, J. Feldman (Eds.), Computers and Thought, New York: McGraw Hill, 1963.
28. S. Feldman, Why Search is Not Enough (white paper), IDC, 2003.
29. S. Feldman, Enterprise Search Technology: Information Disasters and the High Cost of Not Finding Information (Special IDC Report), IDC, 2004.
30. D. Fensel, Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce, Springer, 2003.
31. K. D. Forbus, T. R. Hinrisch, Companion cognitive systems: A step toward human-level AI, AI Magazine 27(2) (2006) 83-95.
32. M. Gell-Mann, The Quark and the Jaguar, NY: Freeman and Co., 1994.
33. J. H. Friedman, T. Hastie, R. Tibshirani, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Heidelberg: Springer, 2001.
34. M. Ghallab, D. Nau, and P. Traverso, Automated Planning: Theory and Practice, CA: Morgan Kaufmann, 2004.
35. S. Ghemawat, H. Gobioff, Shun-Tak Leung, The Wistech Network File System, Wistech Network, 2005.
36. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Professional, 1989.
37. A. Gomez-Perez, O. Corcho, M. Fernandez-Lopez, Ontological Engineering with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web (Advanced Information and Knowledge Processing), Springer, 2004.
38. R. Granger, Engines of the brain: The computational instruction set of human cognition, AI Magazine 27(2) (2006) 15-31.
39. S. Grimes, The Developing Text Mining Market, A white paper prepared for Text Mining Summit 2005, Boston, June 7-8 2005, Alta Plana Corporation, 2005.
40. A. OHagan, C E. Buck, A. Daneshkhah, J. R. Eiser, P. H. Garthwaite, D. J. Jenkinson, J. E. Oakley, T. Rakow, Uncertain Judgements: Eliciting Expert Probabilities, Wiley, New York, 2006.
41. J. Heaton, Programming Spiders, Bots, and Aggregators in Java, Sybex, 2002.
42. K. Hemenway, T. Calishain, Spidering Hacks O'Reilly Media, Inc.; 2003.
43. M. Henzinger, S. Lawrence, Extracting knowledge from the World Wide Web, Wistech Network, 2004.
44. M. Hirvensalo, Quantum Computing, Springer-Verlag, Heidelberg 2001.
45. M. N. Huhns, M. P. Singh, Readings in Agents, Morgan Kaufmann, 1998.
46. P. Jackson, I. Moulinier, Natural Language Processing for Online Applications: Text Retrieval, Extraction, and Categorization (Natural Language Processing, 5), John Benjamins Publishing Co, 2002.
47. Z. Janiszewski, On needs of mathematics in Poland (O potrzebach matematyki w Polsce) (in Polish), In: Nauka Polska. Jej Potrzeby, Organizacja i Rozwój, Warszawa, 1918; see also reprint in Wiadomości Matematyczne VII (1963) 3-8.
48. A. Jankowski, An alternative characterization of elementary logic, Bull. Acad. Pol. Sci., Ser. Math. Astr. Phys.XXX (1-2) (1982) 9-13.
49. A. Jankowski, Galois structures, Studia Logica 44(2) (1985) 109-124.
50. S. Johnson, Dictionary of the English Language in Which the Words are Deduced from Their Originals, and Illustrated in their Different Significations by Examples from the Best Writers, 2 Volumes. London: F.C. and J. Rivington, 1816.
51. R. M. Jones, R. E. Wray, Comparative analysis of frameworks for knowledgeintensive intelligent agents, AI Magazine 27(2) (2006) 57-70.
52. D. Jurafsky, J. H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition, Prentice Hall, 2000.
53. D. Kahneman, P. Slovic, A. Tversky, A. (Eds.). Judgement under Uncertainty: Heuristics and Biases, Cambridge University Press, New York, 1982.
54. R. Keefe, Theories of Vagueness, Cambridge, UK: Cambridge Studies in Philosophy, 2000.
55. W. Kloesgen, J. Zytkow, Handbook of Knowledge Discovery and Data Mining, New York: Oxford University Press, 2002.
56. S. Kraus, Strategic Negotiations in Multiagent Environments, Massachusetts: The MIT Press, 2001.
57. J. Lambek, P. J. Scott, Introduction to Higher-Order Categorical Logic (Cambridge Studies in Advanced Mathematics 7), Cambridge University Press, 1986.
58. M. Lamb, Build Your Own Army of Web Bots Within 24 Hours (Army of Web Bots Series) Authorhouse, 2003.
59. P. Langley, H. A. Simon, G. L. Bradshaw, J. M. Zytkow, Scientific Discovery: Computational Explorations of the Creative Processes, MIT Press, 1987.
60. P. Langley, Cognitive architectures and general intelligent systems, AI Magazine 27(2) (2006) 33-44.
61. G. W. Leibniz, New Essays on Human Understanding, Cambridge UP, 1982.
62. J. Liu, Autonomous Agents and Multi-Agent Systems: Explorations in Learning, Self-Organization and Adaptive Computation, Singapore: World Scientific Publishing, 2001.
63. J. Liu, L. K. Daneshmend, Spatial Reasoning and Planning: Geometry, Mechanism, and Motion, Springer, 2003, Hardcover.
64. J. Liu, X. Jin, K. Ch. Tsui, Autonomy Oriented Computing: From Problem Solving to Complex Systems Modeling, Heidelberg: Kluwer Academic Publisher/Springer, 2005.
65. S. MacLane, I. Moerdijk, Sheaves in Geometry and Logic: A First Introduction to Topos Theory (Universitext), Springer, 1994.
66. A. Madhavapeddy, N. Ludlam, Ubiquitious Computing needs to catch up with Ubiquitous Media, University of Cambridge Computer Laboratory, Interceptor Communications Ltd., 2005.
67. C. D. Manning, H. Schütze, H., Foundations of Statistical Natural Language Processing, The MIT Press, 1999.
68. S. H. Nguyen, J. Bazan, A. Skowron, and H. S. Nguyen, Layered learning for concept synthesis, Transactions on Rough Sets I: Journal Subline, Lecture Notes in Computer Science, vol. 3100, pp. 187-208, Heidelberg: Springer, 2004.
69. T. T. Nguyen, Eliciting domain knowledge in handwritten digit recognition, in [71] 762-767.
70. S. K. Pal, L. Polkowski, and A. Skowron (Eds.), Rough-Neural Computing: Techniques for Computing with Words, Cognitive Technologies, Heidelberg: SpringerVerlag, 2004.
71. S. K. Pal, S. Bandoyopadhay, and S. Biswas (Eds.), Proceedings of the First International Conference on Pattern Recognition and Machine Intelligence (PReMI'05), December 18-22, 2005, Indian Statistical Institute, Kolkata, Lecture Notes in Computer Science vol. 3776, Heidelberg: Springer, 2005.
72. T. B. Passin, Explorer's Guide to the Semantic Web Mining Publications, 2004.
73. Pawlak, Z.: Classification of objects by means of attributes, Research Report PAS 429, Institute of Computer Science, Polish Academy of Sciences, ISSN 138-0648, January (1981).
74. Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, System Theory, Knowledge Engineering and Problem Solving 9, Dordrecht: Kluwer Academic Publishers, 1991.
75. Z. Pawlak, A. Skowron, Rudiments of rough sets. Information Sciences. An International Journal. 177(1) (2007) 3-27.
76. Z. Pawlak, A. Skowron, Rough sets: Some extensions. Information Sciences. An International Journal. 177(1) (2007) 28-40.
77. Z. Pawlak, A. Skowron, Rough sets and Boolean reasoning. Information Sciences. An International Journal. 177(1) (2007) 41-73.
78. J. F.Peters, Rough ethology: Toward a biologically-inspired study of collective behavior in intelligent systems with approximation spaces. Transactions on Rough Sets III: Journal Subline, Lecture Notes in Computer Science, vol. 3400, pp. 153-174, Heidelberg: Springer, 2005.
79. J. F. Peters, C. Henry, C., Reinforcement learning with approximation spaces. Fundamenta Informaticae 71(2-3) (2006) 323-349.
80. S. Plous, The Psychology of Judgement and Decision Making, McGraw-Hill, New York, 1993.
81. T. Poggio, S. Smale, The mathematics of learning: Dealing with data, Notices of the AMS 50(5) (2003) 537-544.
82. L. Polkowski, A. Skowron, Rough mereology: A new paradigm for approximate reasoning, International Journal of Approximate Reasoning 15 (1996) 333-365.
83. L. Polkowski, S. Tsumoto, T. Y. Lin (Eds.), Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems, Studies in Fuzziness and Soft Computing vol. 56, Physica-Verlag Heidelberg, 2000.
84. G. Pólya, How to Solve It, 2nd ed., Princeton University Press, 1957; see also http://en.wikipedia.org/wiki/How_to_Solve_It.
85. H. Rasiowa, W. Marek, On reaching consensus by groups of intelligent agents, In: Z. W. Ras (Ed.), Methodologies for Intelligent Systems, North-Holland, Amsterdam, 1989, 234-243.
86. H. Rasiowa, Algebraic Models of Logics, Warsaw University, 2001.
87. H. Rasiowa, R. Sikorski, The Mathematics of Metamathematics, Monografie Matematyczne vol. 41, PWN Warsaw, 1963.
88. S. Read, Thinking about Logic. An Introduction to the Philosophy of Logic, Oxford, New York: Oxford University Press, 1995.
89. P. Saint-Dizier, E. Viegas, B. Boguraev, S. Bird, D. Hindle, M. Kay, D. McDonald, H. Uszkoreit, Y. Wilks, Computational Lexical Semantics (Studies in Natural Language Processing), Cambridge University Press, 2005.
90. C. Schlenoff, J. Albus, E. Messina, A. J. Barbera, R. Madhavan, S. Balakirsky, Using 4D/RCS to address AI knowledge integration, AI Magazine 27(2) (2006) 71-81.
91. Scientific Datalink, The Scientific DataLink index to artificial intelligence research, 1954-1984, Scientific DataLink, 1985.
92. Scientific Datalink, The Scientific DataLink index to artificial intelligence research, 1985 Supplement, Scientific DataLink, 1985.
93. A. Skowron, Approximate reasoning in distributed environments, in N. Zhong, J. Liu (Eds.), Intelligent Technologies for Information Analysis, Heidelberg: Springer, pp. 433-474.
94. A. Skowron, Perception logic in intelligent systems (keynote talk), In: S. Blair et al (Eds.), Proceedings of the 8th Joint Conference on Information Sciences (JCIS 2005), July 21-26, 2005, Salt Lake City, Utah, USA, X-CD Technologies: A Conference & Management Company, ISBN 0-9707890-3-3, 15 Coldwater Road, Toronto, Ontario, M3B 1Y8, 2005, pp. 1-5.
95. A. Skowron, Rough sets in perception-based computing (keynote talk), in [71], pp. 21-29.
96. A. Skowron, R. Agrawal, M. Luck, T. Yamaguchi, O. Morizet-Mahoudeaux, J. Liu, N. Zhong (Eds.), Proceedings of the 2005 IEEE/WIC/ACM International Conference on WEB Intelligence, Compiegne, France, September 19-22, 2005, IEEE Computer Society Press, Los Alamitos, CA, 2005, pp. 1-819.
97. A. Skowron, J.-P. Barthes, L. Jain, R. Sun, P. Morizet-Mahoudeaux, J. Liu, N. Zhong (Eds.), Proceedings of the 2005 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Compiegne, France, September 19-22, 2005, IEEE Computer Society Press, Los Alamitos, CA, 2005, pp. 1-766.
98. A. Skowron, J. Stepaniuk, Tolerance approximation spaces, Fundamenta Informaticae 27 (1996) 245-253.
99. A. Skowron, J. Stepaniuk, Information granules: Towards foundations of granular computing, International Journal of Intelligent Systems 16(1) (2001) 57-86.
100. A. Skowron, J. Stepaniuk, Information granules and rough-neural computing, in [70], pp. 43-84.
101. A. Skowron, P. Synak, Complex patterns, Fundamenta Informaticae 60(1-4) (2004) 351-366.
102. A. Skowron, R. Swiniarski, and P. Synak, Approximation spaces and information granulation, Transactions on Rough Sets III: Journal Subline, Lecture Notes in Computer Science, vol. 3400, pp. 175-189, Heidelberg: Springer, 2005.
103. J. F. Sowa, Knowledge Representation: Logical, Philosophical, and Computational Foundations, Course Technology, 1999.
104. S. Staab, R. Studer, Handbook on Ontologies, in International Handbooks on Information Systems, Heidelberg: Springer 2004.
105. J. Stepaniuk, J. Bazan, and A. Skowron, Modelling complex patterns by information systems, Fundamenta Informaticae 67 (1-3) (2005) 203-217.
106. J. Stepaniuk, A. Skowron, J. Peters, and R. Swiniarski, Calculi of approximation spaces, Fundamenta Informaticae 72 (1-3) (2006) 363-378.
107. P. Stone, Layered Learning in Multi-Agent Systems: A Winning Approach to Robotic Soccer, Cambridge, MA: The MIT Press, 2000.
108. W. Swartout, J. Gratch, R. W. Hill, E. Hovy, S. Marsella, J. Rickel, D. Traum, Towards virtual humans, AI Magazine 27(2) (2006) 96-108.
109. R. Sun (Ed.), Cognition and Multi-Agent Interaction. From Cognitive Modeling to Social Simulation. New York, NY: Cambridge University Press, 2006.
110. K. Sycara, Multiagent systems, in AI Magazine, Summer 1998, 79-92.
111. D. Ślȩzak, J. T. Yao, J. F. Peters, W. Ziarko, and X. Hu (Eds.), Proceedings of the 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC'2005), Regina, Canada, August 31-September 3, 2005, Part II, Lecture Notes in Artificial Intelligence, vol. 3642, Heidelberg: Springer, 2005.
112. A. S. Troelstra, H. Schwichtenberg, Basic Proof Theory, Cambridge University Press, 2000.
113. A. S. Troelstra, D. Van Dalen, Constructivism in Mathematics: An Introduction, Studies in Logic and the Foundations of Mathematics Vol. 1 & 2, Elsevier Science Publishing Company, 1988.
114. A. Turing, Computing machinery and intelligence, Mind LIX(236) (October 1950) 433-460.
115. W. Van Wezel, R. Jorna, and A. Meystel, Planning in Intelligent Systems: Aspects, Motivations, and Methods. Hoboken, New Jersey: John Wiley & Sons, 2006.
116. V. Vapnik, Statistical Learning Theory, New York: John Wiley & Sons, 1998.
117. S. Weiss, N. Indurkhya, T. Zhang, F. Damerau, Text Mining: Predictive Methods for Analyzing Unstructured Information, Springer, 2004.
118. I. H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Second Edition, Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann, 2005.
119. I. H. Witten, A. Moffat, T. C. Bell, Managing Gigabytes: Compressing and Indexing Documents and Images, The Morgan Kaufmann Series in Multimedia and Information Systems, Morgan Kaufmann, 1999.
120. L. A. Zadeh, Fuzzy sets, Information and Control 8 (1965) 333-353.
121. L. A. Zadeh, From computing with numbers to computing with words - from manipulation of measurements to manipulation of perceptions, IEEE Transactions on Circuits and Systems - I: Fundamental Theory and Applications 45(1) (1999) 105-119.
122. L. A. Zadeh, A new direction in AI: Toward a computational theory of perceptions, AI Magazine 22 (1) (2001) 73-84.
123. W. Ziarko, Variable precision rough set model, Journal of Computer and System Sciences 46 (1993) 39-59.