References
1 Russell, SJ, Norvig, P.
Artificial Intelligence: A Modern Approach. Upper Saddle River, NJ:
Prentice‐Hall; 2010.
2 Tecuci, G.
Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory, Methodology, Tool and Case Studies. San Diego, CA:
Academic Press; 1998.
3 Turing, A. Computing machinery and intelligence. Mind 1950, 59:433–460.
4 Samuel, AL. Some studies in machine learning using the game of checkers. IBM J Res Dev 1959, 3:210–229.
5 Robinson, JA. A machine‐oriented logic based on the resolution principle. JACM 1965, 12:23–41.
6 Buchanan, BG, Sutherland, GL, Feigenbaum, EA. %22Heuristic DENDRAL: a program for generating explanatory hypotheses in organic chemistry.%22 In: Meltzer, B, Michie, D, Swan, M, eds. Machine Intelligence, vol 4. Edinburgh:
Edinburgh University Press; 1969, 209–254.
7 Buchanan, BG, Shortliffe, EH.
Rule Based Expert Systems. Reading, MA:
Addison‐Wesley; 1984.
8 Anderson, JR.
The Architecture of Cognition. Cambridge, MA:
Harvard University Press; 1983.
9 Laird, J, Newell, A, Rosenbloom, PS. SOAR: an architecture for general intelligence. Artif Intell J 1987, 33:1–64.
10 Tecuci, G. DISCIPLE: a theory, methodology and system for learning expert knowledge. Thèse de Docteur en Science, University of Paris‐South, 1988.
11 Manning, C, Schutze, H.
Foundations of Statistical Natural Language Processing. Cambridge, MA:
MIT Press; 1999.
12 Rich, E, Knight, K.
Artificial Intelligence. New York, NY:
McGraw‐Hill; 1991.
13 Brachman, RJ, Levesque, HJ.
Readings in Knowledge Representation. San Mateo, CA:
Morgan Kaufmann; 1985.
14 Brachman, RJ, Levesque, H.
Knowledge Representation and Reasoning. San Francisco, CA:
Morgan Kaufman; 2004.
15 McCarthy, J. %22Programs with common sense.%22 In: Minsky, ML, ed.
Semantic Information Processing. Cambridge, MA:
MIT Press; 1968, 403–418.
16 Kowalski, R.
Logic for Problem Solving. Amsterdam, London, New York:
Elsevier, North‐Holland; 1979.
17 Genesereth, MR, Nilsson, NJ.
Logical Foundations of Artificial Intelligence. San Mateo, CA:
Morgan Kaufmann; 1987.
18 Brownston, L, Farrell, R, Kant, E, Martin, N.
Programming Expert Systems in OPS5: An Introduction to Rule‐Based Programming. Reading, MA:
Addison‐Wesley; 1985.
19 Quillian, MR. %22Semantic memory.%22 In: In Minsky, M, ed.
Semantic Information Processing. Cambridge, MA:
MIT Press; 1968, 227–270.
20 Minsky, M. %22A framework for representing knowledge.%22 In: Winston, PH, ed.
The Psychology of Computer Vision. New York:
McGraw‐Hill; 1975, 211–277.
21 Bobrow, DG, Winograd, T. An overview of KRL, a knowledge representation language. Cognit Sci 1977, 1:3–46.
22 Sowa, JF. Knowledge Representation: Logical, Philosophical, and Computational Foundations. Pacific Grove, CA:
Brooks/Cole; 1984.
23 Brachman, RJ, Schmolze, JG. An overview of the KL‐ONE knowledge representation system. Cognit Sci 1985, 9:171–216.
24 Lenat, DB, Guha, RV.
Building Large Knowledge‐Based Systems: Representation and Inference in the CYC Project. Reading, MA:
Addison‐Wesley; 1990.
25 Pearl, J.
Causality: Models, Reasoning, and Inference. New York:
Cambridge University Press; 2009.
26 Cohen, LJ.
The Probable and the Provable. Oxford:
Clarendon Press; 1977.
27 Shafer, G. A Mathematical Theory of Evidence. Princeton, NJ:
Princeton University Press; 1976.
28 Zadeh, L. The Role of Fuzzy Logic in the Management of Uncertainty in Expert Systems. Fuzzy Sets Syst 1983, 11:199–227.
29 Schum, DA. The Evidential Foundations of Probabilistic Reasoning. Evanston, IL:
Northwestern University Press; 2001.
30 OWL 2 Web Ontology Language. Available at: (Accessed August 14, 2011).
31 Campbell, MS, Hoane, AJ, Hsu, F‐H. Deep blue Artif Intell J 2002, 134:57–83.
32 Leake, DB.
Case‐based Reasoning: Experiences, Lessons and Future Directions.
MIT Press; 1996.
33 Nilsson, NJ.
Problem Solving Methods in Artificial Intelligence. New York:
McGraw‐Hill; 1971.
34 Mitchell, TM. Version spaces: an approach to concept learning. Doctoral Dissertation. Stanford, CA:
Stanford University; 1978.
35 Michalski, RS. %22A theory and methodology of inductive learning.%22 In: Michalski, RS, Carbonell, JG, Mitchell, TM, eds. Machine Learning: An Artificial Intelligence Approach, vol 1. Palo Alto, CA:
Tioga Publishing Co; 1983, 83–129.
36 Quinlan, JR. Induction of decision trees. Mach Learn 1986, 1:81–106.
37 Mitchell, TM. Machine Learning. Boston, MA:
McGraw‐Hill; 1997.
38 Hearst, MA, Dumais, ST, Osman, E, Platt, J, Scholkopf, B. Support vector machines. IEEE Intell Syst 1998, 13:18–28.
39 Mitchell, TM, Keller, RM, Kedar‐Cabelli, ST. Explanation‐based generalization: a unifying view. Mach Learn 1986, 1:47–80.
40 DeJong, G, Mooney, R. Explanation‐based learning: an alternative view. Mach Learn 1986, 1: 145–176.
41 Winston, PH. Learning and reasoning by analogy. Commun ACM 1980, 23:689–703.
42 Gentner, D. Structure mapping: a theoretical framework for analogy. Cognit Sci 1983, 7:155–170.
43 Josephson, J, Josephson, S.
Abductive Inference.
Cambridge University Press; 1994.
44 Fisher, DH. Knowledge acquisition via incremental conceptual clustering. Mach Learn 1987, 2:139–172.
45 Langley, P, Simon, HA, Bradshow, GL, Zytkow, JM.
Scientific Discovery: Computational Explorations of the Creative Processes. Cambridge, MA:
MIT Press; 1987.
46 Kaelbling, LP, Littman, ML, Moore, AV. Reinforcement learning: a survey. J AI Res 1996, 4:237–285. Available at: . (Accessed November 11, 2011).
47 DeJong, K.
Evolutionary Computation: Theory and Practice.
MIT Press; 2006.
48 Tecuci, G. Plausible justification trees: a framework for the deep and dynamic integration of learning strategies. Mach Learn J 1993, 11:237–261.
49 Michalski, RS, Tecuci, G, eds.
Machine Learning: A Multistrategy Approach. San Mateo, CA:
Morgan Kaufmann Publishers; 1994.
50 Tecuci, G, Boicu, M, Boicu, C, Marcu, D, Stanescu, B, Barbulescu, M. The Disciple‐RKF learning and reasoning agent. Comput Intell 2005, 21:462–479.
51 Tecuci, G, Boicu, M, Cox, MT. Seven aspects of mixed‐initiative reasoning: an introduction to the special issue on mixed‐initiative assistants. AI Mag 2007, 28:11–18.
52 Tufiş, D, Ion, R, Ide, N. Fine‐grained word sense disambiguation based on parallel corpora, word alignment, word clustering and aligned wordnets. Proceedings of the 20th International Conference on Computational Linguistics, COLING2004, Geneva; 2004, 1312–1318.
53 Tufiş, D. %22Language engineering for lesser‐studied languages.%22 In: Nirenburg, S, ed.
Algorithms and Data Design Issues for Basic NLP Tools. NATO Science for Peace and Security Series ‐ D: Information and Communication Security.
IOS Press; 2009, 3–50.
54 WordNet: A Lexical Database for English. . (Accessed October 1, 2011).
55 Allen, J.
Natural Language Understanding. Redwood City, CA:
The Benjamin/Cummings Publishing Company, Inc; 1995.
56 Jurafsky, D, Martin, JH.
Speech and Language Processing.
Prentice Hall; 2008.
57 Huang, XD, Acero, A, Hon, H.
Spoken Language Processing. Upper Saddle River, NJ:
Prentice Hall; 2001.
58 Forsyth, D, Ponce, J.
Computer Vision: A Modern Approach. Upper Saddle River, NJ:
Prentice Hall; 2002.
59 Bekey, G, Ambrose R, Kumar V, Lavery D, Sanderson A, Wilcox B, Yuh J, Zheng Y. Robotics: State of the Art and Future Challenges. London, England:
Imperial College Press; 2008.
60 Turoff, M. Design of Interactive Systems. In Emergency Management Information Systems Tutorial. The Hawaii International Conference on System Sciences, HICSS‐40, Hawaii; 2007.
61 Phillips‐Wren, G, Jain, LC, Nakamatsu, K, Howlett, RJ, eds.
Advances in Intelligent Decision Technologies, SIST 4. Berlin, Heidelberg:
Springer‐Verlag; 2010.
62 Toward Knowledge Societies. Available at: . (Accessed October 1, 2011).
.gif)
.jpg)
.jpg)
.jpg)
