Contemporary artificial intelligence

Contemporary Artificial Intelligence

Volumn , Issue , 2012, Pages 1-493

  Neapolitan, Richard E ^a   Jiang, Xia ^b  

^a NORTHEASTERN ILLINOIS UNIVERSITY   (United States)

^b UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION PROGRAMS; CHARACTER RECOGNITION; COMPUTATION THEORY; DECISION MAKING; DIAGNOSIS; MEDICAL PROBLEMS; PROBLEM SOLVING; SPEECH RECOGNITION; STUDENTS;

AI TECHNIQUES; FINANCIAL DECISIONS; GENETIC ANALYSIS; NATURAL LANGUAGE UNDERSTANDING; PROBABILISTIC LEARNING; SCIENCE FICTIONS; SOFTWARE DIAGNOSTICS; TEXT RECOGNITION;

SWARM INTELLIGENCE;

EID: 85054948391     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b12524     Document Type: Book

References (257)

1. Aczel, A., and J. Sounderpandian, 2002 Complete Business Statistics, McGraw-Hill, New York.
2. Allen, J., 1995 Natural Language Understanding, Benjamin/Cummings.
3. Allison, C., 2007 “Practical Computation Theory,” The Journal of Computing Sciences in Colleges, Vol. 22, No. 10.
4. Amarel, S., 1968 “On Representations of Problems of Reasoning About Actions,” in Michie, D. (Ed.): Machine Intelligence 3, Elsevier/North-Holland.
5. Anderson, D., D. Sweeney, and T. Williams, 2007 Statistics for Business and Economics, South-Western.
6. Ash, R. B., 1970 Basic Probability Theory, Wiley.
7. Baldassarre, G., et al., 2007 “Self-Organized Coordinated Motion in Groups of Physically Connect Robots,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 37, No. 1.
8. Banzhaf, W. P. Nordin, R.E. Keller, and F.D. Francone, 1998 Genetic Programming An Introduction, Morgan Kaufmann.
9. Basye, K., T. Dean, J. Kirman, and M. Lejter, 1992 “A Decision-Theoretic Approach to Planning, Perception and Control,” IEEE Expert, Vol. 7, No. 4.
10. Bauer, E., D. Koller, and Y. Singer, 1997 “Update Rules for Parameter Estimation in Bayesian Networks,” in Geiger, D., and P. Shenoy (Eds.): Uncertainty in Artificial Intelligence; Proceedings of the Thirteenth Conference, Morgan Kaufmann.
11. Beckers, E., S. Ceria, and G. Cornuéjols, 1992 “Trails and U-turns in the Selection of the Shortest Path by the Ant Lasius Nifwe,” Journal of Theoretical Biology, Vol. 159.
12. Beinlich, I. A., H. J. Suermondt, R. M. Chavez, and G. F. Cooper, 1989 “The ALARM Monitoring System: A Case Study with Two Probabilistic Inference Techniques for Belief Networks,” Proceedings of the Second European Conference on Artificial Intelligence in Medicine, London.
13. Bell, D.E., 1982 “Regret in Decision Making Under Uncertainty,” Operations Research, Vol. 30, No. 5.
14. Bellman, R., 1957 “A Markovian Decision Process.” Journal of Mathematics and Mechanics, Vol. 6, No. 4.
15. Benson, S., 1995 “Active Model Learning and Action Execution in a Reactive Agent,” Proceedings of the 1995 International Joint Conference in Artificial Intelligence (IJCAI).
16. Bentler, P. N., 1980 “Multivariate Analysis with Latent Variables,” Review of Psychology, Vol. 31.
17. Bernado, J., and A. Smith, 1994 Bayesian Theory, Wiley, New York.
18. Bilmes, J.A., 2000 “Dynamic Bayesian Multinets,” in Boutilier, C. and M. Goldszmidt (Eds.): Uncertainty in Artificial Intelligence; Proceedings of the Sixteenth Conference, Morgan Kaufmann.
19. Boden, M.A., 1977 Artificial Intelligence and Natural Man, Basic Books.
20. Brachman, R.J., and H.J. Levesque, 2004 Knowledge Representation and Reasoning, Morgan Kaufmann.
21. Breder, C.M., 1954 “Equations Descriptive of Fish Schools and Other Animal Aggregations,” Ecology, Vol. 35.
22. Breese, J., D. Heckerman, and C. Kadie, 1998 “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” in Cooper, G.F., and S. Moral (Eds.): Uncertainty in Artificial Intelligence; Proceedings of the Fourteenth Conference, Morgan Kaufmann.
23. Breiman, L., J. Friedman, R. Olshen, and C. Stone, 1984 Classification and Regression Trees, Wadsworth and Brooks.
24. Brooks, R.A., 1981 “Symbolic Reasoning Among 3-D Models and 3-D Images,” Artificial Intelligence, Vol. 17., No. 1-3.
25. Brooks, R.A., 1991 “Intelligence without Representation,” Artificial Intelligence, Vol. 47.
26. Buchanan, B.G., and E.H. Shortliffe, 1984 Rule-Based Expert System.
27. Bulmer, M., 2003 Francis Galton: Pioneer of Heredity and Biometry, Johns Hopkins University Press.
28. Buntine, W., “Learning Classification Trees,” in Hand, D.J. (Ed.): Artificial Intelligence Frontiers in Statistics: AI and Statistics III, Chapman … Hall/CRC, 1993.
29. Buntine, W., 2002 “Tree Classification Software,” Proceedings of the Third National Technology Transfer Conference and Exposition, Baltimore, MD.
30. Burnell, L., and E. Horvitz, 1995 “Structure and Chance: Melding Logic and Probability for Software Debugging,” Communications of the ACM, Vol. 38, No. 3.
31. Campo, A., et al., 2010 “Artificial Pheromone for Path Selection by a Foraging Swarm of Robots,” Biological Cybernetics, Vol. 103, No. 5, 2010.
32. Cardie, C., 1997 “Empirical Methods in Information Extraction,” AI Magazine, Vol. 18, No. 4.
33. Chalmers, D., 1996 The Conscious Mind: In Search of a Fundamental Theory, Oxford University Press.
34. Charniak, E., 1993 Statistical Language Learning, MIT Press.
35. Charniak, E, 1997 “Statistical Parsing with a Context-Free Grammar and Word Statistics,” Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI 1997).
36. Cheeseman, P., and J. Stutz, 1996 “Bayesian Classification (Autoclass): Theory and Results,” in Fayyad, D., G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (Eds.): Advances in Knowledge Discovery and Data Mining, AAAI Press.
37. Chib, S., 1995 “Marginal Likelihood from the Gibbs Output,” Journal of the American Statistical Association, Vol. 90, No. 432.
38. Chickering, D., 2001 “Learning Equivalence Classes of Bayesian Networks,” Technical Report # MSR-TR-2001-65, Microsoft Research, Redmond, WA.
39. Chickering, D., 2002 “Optimal Structure Identification with Greedy Search,” Journal of Machine Learning Research, Vol. 3, 2002.
40. Chickering, D., and D. Heckerman, “Efficient Approximation for the Marginal Likelihood of Bayesian Networks with Hidden Variables,” Technical Report # MSR-TR- 96-08, Microsoft Research, Redmond, WA, 1997.
41. Christensen, L.M., H. Harkema, P.J. Haug, J.Y. Irwin, and W.W. Chapman, 2009 “ONYX: A System for the Semantic Analysis of Clinical Text,” Proceedings of the Workshop on BioNLP, Boulder, CO, June 04-05.
42. Clemen, R.T., 1996 Making Hard Decisions, PWS-KENT.
43. Collins, M., “Three Generative, Lexicalised Models for Statistical Parsing,” Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics, 1997.
44. Collins, A., and M.R. Quillian, “Retrieval Time From Semantic Memory,” Journal of Verbal Learning and Verbal Behavior, Vol. 8, 1969.
45. Cooper, G. F., “The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks,” Artificial Intelligence, Vol. 42, No. 2-3. 1990.
46. Cooper, G. F., and E. Herskovits, “A Bayesian Method for the Induction of Probabilistic Networks from Data,” Machine Learning, Vol. 9, 1992.
47. Cotton, S., A. Bundy, and T. Walsh, “Automatic Invention of Integer Sequences,” Proceedings of AAAI-2000, MIT Press, 2000.
48. Cozman, F., and E. Krotkov, 1996 “Quasi- Bayesian Strategies for Efficient Plan Generation: Application to the Planning to Observe Problem,” in Horvitz, E., and F. Jensen (Eds.): Uncertainty in Artificial Intelligence; Proceedings of the Twelfth Conference, Morgan Kaufmann.
49. Dagum, P., and R. M. Chavez, “Approximating Probabilistic Inference in Bayesian Belief Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 3, 1993.
50. Dagum, P., and M. Luby, “Approximating Probabilistic Inference in Bayesian Belief Networks Is NP-hard,” Artificial Intelligence, Vol. 60, No. 1, 1993.
51. Davis, R., and D.B. Lenat, Knowledge-based Systems in Artificial Intelligence, McGraw-Hill, 1982.
52. Dean, T., and M. Wellman, Planning and Control, Morgan Kaufmann, 1991.
53. Dechter, R. and J. Pearl, “Generalized Best- First Search Strategies and the Optimality of A*,” Journal of the ACM, Vol. 32, No. 3, 1985.
54. de Finetti, B., “La prévision: See Lois Logiques, ses Sources Subjectives,” Annales de l’Institut Henri Poincaré, Vol. 7, 1937.
55. Delgrande, J., and T. Schaub, “On the Relation Between Reiter’s Default Logic and its (major) Variants,” Proceedings of the Seventh European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2003.
56. Demirer, R., R. Mau, and C. Shenoy, “Bayesian Networks: A Decision Tool to Improve Portfolio Risk Analysis,” Journal of Applied Finance, Vol. 6, No. 2, 2006.
57. Dempster, A, N. Laird, and D. Rubin, “Maximum Likelihood from Incomplete Data via the EM Algorithm,” Journal of the Royal Statistical Society B, Vol. 39, No. 1, 1977.
58. DePuy, G.W., R.J. Moraga, and G.E. Whitehouse, “Meta-RaPS: A Simple and Effective Approach for Solving the Traveling Salesman Problem,” Transportation Research Part E, Vol. 41, No. 2, 2005.
59. Diez, F.J., and M.J. Druzdzel, “Canonical Probabilistic Models for Knowledge Engineering,” Technical Report IA-02-01, Dpto. Inteligencia Artificial, UNED, Madrid, 2002.
60. Dorigo, M., and L.M. Gambardella, “Ant Colonies for the Traveling Salesperson Problem,” Biosystems, Vol. 43, 1997.
61. Dorigo, M., V. Maniezzo, and A. Colorni, “The Ant System: by a Colony of Cooperating Agents,” IEEE Transaction on Systems, Man, and Cybernetics, Part B, Vol. 26, No. 1, 1996.
62. Doyle, J., “A Truth Maintenance System,” Artificial Intelligence, Vol. 12, No. 3, 1979.
63. Druzdzel, M.J., and C. Glymour, “Causal Inferences from Databases: Why Universities Lose Students,” in Glymour, C., and G.F. Cooper (Eds.): Computation, Causation, and Discovery, AAAI Press, 1999.
64. Duda, R.O., P.E. Hart, and N.J. Nilsson, “Subjective Bayesian Methods for Rule- Based Inference Systems,” Technical Report 124, Stanford Research Institute, 1976.
65. Durbin, R., and D. Willshaw, “An Analogue Approach to the Traveling Salesperson Problem Using an Elastic Net Method,” Nature, Vol. 326, 1987.
66. Edelman, G.M., Second Nature: Brain Science and Human Knowledge, Yale University Press, 2006.
67. Edelman, G.M., “Learning In and From Brain-Based Devices,” Science, Vol. 318, No. 5853, 2007.
68. Eells, E., Probabilistic Causality, Cambridge University Press, 1991.
69. Farnsworth, G.V., J.A. Kelly, A.S. Othling, and R. J. Pryor, “Successful Technical Trading Agents Using Genetic Programming,” Technical Report # SAND2004- 4774, Sandia National Laboratories, Albuquerque, NM, 2004.
70. Feigenbaum, E.A., B.G. Buchanan, and J. Lederberg, “On Generality and Problem Solving: A Case Study Using the Dendral Program, in Meltzer, B., and D. Mitchie (Eds.): Machine Intelligence 6, Edinburgh University Press, 1971.
71. Feller, W., An Introduction to Probability Theory and its Applications, Wiley, New York, 1968.
72. Fikes, R.E., and N.J. Nilsson, “STRIPS: A New Approach to the Application of Theorem Proving to Artificial Intelligence,” Artificial Intelligence, Vol. 3, No. 2, 1971.
73. Fishelson, M., and D. Geiger, “Exact Genetic Linkage Computations for General Pedigrees,” Bioinformatics, Vol. 18 (supplement 1), 2002.
74. Fishelson, M., and D. Geiger, “Optimizing Exact Genetic Linkage Computation,” Journal of Computational Biology, Vol. 11, No. 2−3, 2004.
75. Fogel, D.B., “Evolutionary Programming in Perspective: The Top-Down View,” in Zurada, J.M., R.J. Marks II, and C.J. Robinson (Eds.): Computational Intelligence: Imitating Life, IEEE Press, 1994.
76. Fraser, A.S., 1958 “Monte Carlo Analyses of Genetic Models,” Nature, Vol. 181.
77. Freedman, F., R. Pisani, and R. Purves, 2007 Statistics, W.W. Norton … Co.
78. Friedman, N., M. Linial, I. Nachman, and D. Pe’er, 2000 “Using Bayesian Networks to Analyze Expression Data,” in Proceedings of the Fourth Annual International Conference on Computational Molecular Biology.
79. Friedman, N., M. Ninio, I Pe’er, and T. Pupko, 2002 “A Structural EM Algorithm for Phylogenetic Inference,” Journal of Computational Biology, Vol. 9, No. 2.
80. Friedman, N., and K. Koller, 2003 “Being Bayesian about Network Structure: A Bayesian Approach to Structure Discovery in Bayesian Networks,” Machine Learning, Vol. 50, No.1.
81. Fung, R., and K. Chang, 1990 “Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks,” in Henrion, M., R. D. Shachter, L. N. Kanal, and J. F. Lemmer (Eds.): Uncertainty in Artificial Intelligence 5, North-Holland, 1990.
82. Galán, S.F., and F. Aguado, F.J. Díez, and J. Mira, 2002 “NasoNet, Modeling the Spread of Nasopharyngeal Cancer with Networks of Probabilistic Events in Discrete Time,” Artificial Intelligence in Medicine, Vol. 25, No. 3.
83. Gambardella, L.M., and M. Dorigo, 1995 “Ant-Q: a Reinforcement Learning Approach to the Traveling Salesperson Problem,” in Prieditis, A., and S. Russell (Eds.): Proceedings of ML-95, 12th International Conference on Machine Learning, Morgan Kaufmann.
84. Geiger, D., and D. Heckerman, 1994 “Learning Gaussian Networks,” in de Mantras, R.L., and D. Poole (Eds.): Uncertainty in Artificial Intelligence; Proceedings of the Tenth Conference, Morgan Kaufmann.
85. Gelernter, H., 1959 “Realization of a Geometry Theorem-Proving Machine,” Proceedings of International Conference on Information Processing, UNESCO.
86. Geman, S., and D. Geman, 1984 “Stochastic Relaxation, Gibb’s Distributions and the Bayesian Restoration of Images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 6.
87. Gilks, W.R., S. Richardson, and D.J. Spiegelhalter (Eds.): 1996 Markov Chain Monte Carlo in Practice, Chapman … Hall/CRC.
88. Gillispie, S.B., and M.D. Pearlman, 2001 “Enumerating Markov Equivalence Classes of Acyclic Digraph Models,” in Koller, D., and J. Breese (Eds.): Uncertainty in Artificial Intelligence; Proceedings of the Seventeenth Conference, Morgan Kaufmann.
89. Glymour, C., 2001 The Mind’s Arrows: Bayes Nets and Graphical Causal Models in Psychology, MIT Press.
90. Glymour, C., and G. Cooper, 1999 Computation, Causation, and Discovery, MIT Press.
91. Goertzel, B., and C. Pennachin, 2007 Artificial General Intelligence, Springer.
92. Goldstone, R.L., and M.A. Janssen, 2005 “Computational Models of Collective Behavior,” Trends in Cognitive Sciences, Vol. 9, No. 9, 2005.
93. Goodwin, J., 1982 “An Improved Algorithm for Non-Monotonic Dependency Net Update,” Technical Report LITH-MAT-R-82-23, Department of Computer Science and Information Science, Linköping University, Linköping, Sweden.
94. Griffiths, J. F., S. R. Wessler, R. C. Lewontin, and S. B. Carroll, An Introduction to Genetic Analysis, W. H. Freeman and Company, 2007.
95. Hamilton, W.D., 1971 “Geometry for the Selfish Herd,” Journal of Theoretical Biology, Vol. 31.
96. Harnad, S. “What’s Wrong and Right About Searle’s Chinese Room Argument,” in Bishop, M., and J. Preston (Eds.): Essays on Searle’s Chinese Room Argument, Oxford University Press, 2001.
97. Hartl, D. L., and E. W. Jones, Essential Genetics, Jones and Bartlett, 2006.
98. Hanks, S., and D. McDermott, “Nonmonotonic Logic and Temporal Projection,” Artificial Intelligence, Vol. 33, No. 3, 1987.
99. Hardwick, J.P., and Q.F. Stout, “Bandit Strategies for Ethical Sequential Allocation,” Computing Science and Statistics, Vol. 23, 1991.
100. Hastings, W.K., 1970 “Monte Carlo Sampling Methods Using Markov Chains and their Applications,” Biometrika, Vol. 57, No. 1.
101. Hebb, D., 1949 The Organization of Behavior, Wiley.
102. Heckerman, D., 1996 “A Tutorial on Learning with Bayesian Networks,” Technical Report # MSR-TR-95-06, Microsoft Research, Redmond, WA.
103. Heckerman, D., and C. Meek, “Embedded Bayesian Network Classifiers,” Technical Report MSR-TR-97-06, Microsoft Research, Redmond, WA, 1997.
104. Heckerman, D., E. Horvitz, and B. Nathwani, “Toward Normative Expert Systems: Part I The Pathfinder Project,” Methods of Information in Medicine, Vol. 31, 1992.
105. Heckerman, D., J. Breese, and K. Rommelse, 1994 “Troubleshooting Under Uncertainty,” Technical Report MSR-TR-94-07, Microsoft Research, Redmond, WA.
106. Heckerman, D., C. Meek, and G. Cooper, 1999 “A Bayesian Approach to Causal Discovery,” in Glymour, C., and G.F. Cooper (Eds.): Computation, Causation, and Discovery, AAAI Press.
107. Heppner, F., and U. Grenander, 1990 “A Stochastic Nonlinear Model for Coordinated Bird Flocks,” in Kasner, S. (Ed.): The Ubiquity of Chaos, AAAS Publications.
108. Herskovits, E. H., and G. F. Cooper, 1990 “Kutató: An Entropy-Driven System for the Construction of Probabilistic Expert Systems from Databases,” in Shachter, R. D., T. S. Levitt, L. N. Kanal, and J. F. Lemmer (Eds.): Uncertainty in Artificial Intelligence; Proceedings of the Sixth Conference, North-Holland.
109. Herskovits, E.H., and A.P. Dagher, 1997 “Applications of Bayesian Networks to Health Care,” Technical Report NSI-TR-1997-02, Noetic Systems Incorporated, Baltimore, MD.
110. Hey, J.D., and A. Morone, 2004 “Do Markets Drive Out Lemmings-or Vice Versa?,” Economica, London School of Economics and Political Science, Vol. 71, No. 284.
111. Hogg, R. V., and A. T. Craig, 1972 Introduction to Mathematical Statistics, Macmillan.
112. Holland, J., Adaptation in Natural and Artificial Systems, University of Michigan Press, 1975.
113. Horvitz, E., S. Srinivas, C. Rouokangas, and M. Barry, 1992 “A Decision-Theoretic Approach to the Display of Information for Time- Critical Decisions: The Vista Project,” Proceedings of SOAR-92, Houston, TX.
114. Huang, T., D. Koller, J. Malik, G. Ogasawara, B. Rao, S. Russell, and J. Weber, 1994 “Automatic Symbolic Traffic Scene Analysis Using Belief Networks,” Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI94), AAAI Press.
115. Hume, D., An Inquiry Concerning Human Understanding, Prometheus, 1988 (originally published in 1748).
116. Iversen, G. R., W. H. Longcor, F. Mosteller, J. P. Gilbert, and C. Youtz, 1971 “Bias and Runs in Dice Throwing and Recording: A Few Million Throws,” Psychometrika, Vol. 36.
117. Jadbabaie, A., J. Lin, and A.S. Morse, 2003 “Coordination of Groups of Mobile Autonomous Agents Using Nearest Neighbor Rules,” IEEE Transactions on Automatic Control, Vol. 48.
118. Jensen, F., S. Lauritzen, and K. Olesen, 1990 “Bayesian Updating in Causal Probabilistic Networks by Local Computations,” Computational Statistics Quarterly 4.
119. Jensen, F.V., Bayesian Networks and Decision Graphs, Springer-Verlag, New York, 2001.
120. Jiang, X., M.M. Barmada, R.E. Neapolitan, S. Visweswaran, and G.F. Cooper, 2010 “A Fast Algorithm for Learning Epistatic Genomic Relationships,” AMIA 2010 Symposium Proceedings.
121. Jiang, X., R.E. Neapolitan, M.M. Barmada, and S. Visweswaran, 2011 “Performance of Bayesian Network Scoring Criteria for Learning Genetic Epistasis,” BMC Bioinformatics, Vol. 12, No. 89.
122. Jiang, X., S. Visweswaran, and R.E. Neapolitan, 2011 “Mining Epistatic Interactions from High-Dimensional Data Sets Using Bayesian Networks,” in Holmes, D. and L. Jain (Eds.): Foundations and Intelligent Paradigms—3, Springer-Verlag.
123. Joereskog, K. G., 1982 Systems Under Indirect Observation, North Holland.
124. Jurafsky, D., and J. Martin, 2009 Speech and Language Processing, Prentice Hall.
125. Kaelbling, L.P., M.L. Littman, and A.R. Cassandra, 1998 “Planning and Acting in Partially Observable Stochastic Domains,” Artificial Intelligence, Vol. 101.
126. Kahneman, D., and A. Tversky, 1979 “Prospect Theory: An Analysis of Decision Under Risk,” Econometrica, Vol. 47.
127. Katz, E.P., 1997 “Extending the Teleo-Reactive Paradigm for Robotic Agent Task Control Using Zadehan (fuzzy) Logic,” Proceeding of IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA’97).
128. Kemmerer, B., Mishra, S., and P. Shenoy, 2002 “Bayesian Causal Maps as Decision Aids in Venture Capital Decision Making,” Proceedings of the Academy of Management Conference.
129. Kennedy, J., and R.C. Eberhart, 1995 “Particle Swarm Optimization,” Proceedings of the IEEE Conference on Neural Networks IV, IEEE Service Center, New York.
130. Kennedy, J., and R.C. Eberhart, 2001 Swarm Intelligence, Morgan Kaufmann.
131. Kennett, R., K. Korb, and A. Nicholson, 2001 “Seabreeze Prediction Using Bayesian Networks: A Case Study,” Proceedings of the 5th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining - PAKDD, Springer-Verlag, New York.
132. Kenny, D. A., 1979 Correlation and Causality, Wiley.
133. Kerrich, J. E., 1946 An Experimental Introduction to the Theory of Probability, Einer Munksgaard.
134. Klein, W.B., C.R. Stern, G.F. Luger, and D. Pless, 2000 “Teleo-Reactive Control for Accelerator Beam Tuning,” Artificial Intelligence and Soft Computing: Proceedings of the IASTED International Conference, IASTED/ACTA Press.
135. Koehler, S., 1998 Symtext: a Natural Language Understanding System for Encoding Free Text Medical Data, Ph.D. dissertation, University of Utah.
136. Korf, R., 1993 “Linear-Space Best-First Search,” Artificial Intelligence, Vol. 62.
137. Koza, J., 1992 Genetic Programming, MIT Press.
138. Krishnamurthy, B., C. Wills, and Y. Zhang, 2001 “On the Use and Performance of Content Distribution Networks,” ACM SIGCOMM Internet Measurement Workshop.
139. Lander, D. M., and P. Shenoy, 1999 “Modeling and Valuing Real Options Using Influence Diagrams,” School of Business Working Paper No. 283, University of Kansas.
140. Langley, P., H.A. Simon, G.L. Bradshaw, and J.M. Zytkow, 1990 Scientific Discovery: Computational Explorations of the Creative Processes, MIT Press.
141. Lari, K., and S. Young, 1990 “The Estimation of Stochastic Context-Free Grammars Using the Inside-Outside Algorithm,” Computer Speech and Language, Vol. 4.
142. Latane, B., 1981 “The Psychology of Social Impact,” American Psychologist, Vol. 36.
143. Lauritzen, S. L., and D. J. Spiegelhalter, 1988 “Local Computation with Probabilities in Graphical Structures and Their Applications to Expert Systems,” Journal of the Royal Statistical Society B, Vol. 50, No. 2.
144. Lenat, D.B., 1983 “EURISKO: A Program that Learns New Heuristics,” Artificial Intelligence, Vol. 21, No. 1-2.
145. Lenat, D.B., 1998 “ From 2001 to 2001: Common sense and the mind of HAL,” in Stork D.G. (Ed.): Hal’s Legacy: 2001 as Dream and Reality, MIT Press.
146. Lenat, D.B., and J.S. Brown, 1984 “Why AM and Eurisko Appear to Work,” Artificial Intelligence, Vol. 23, No. 3.
147. Leung, K.S., H.D. Jin, and Z.B. Xu, 2004 “An Expanding Self-Organizing Neural Network for the Traveling Salesman Problem,” Neurocomputing, Vol. 62.
148. Li, W., 1997 Molecular Evolution, Sinauer Associates.
149. Li, Z., and B. D’Ambrosio, 1994 “Efficient Inference in Bayes’ Networks as a Combinatorial Optimization Problem,” International Journal of Approximate Inference, Vol. 11.
150. Lifschitz, V., 1994 “Circumscription,” In Gabbay, D., C. J. Hogger, and J. A. Robinson (Eds.): Handbook of Logic in Artificial Intelligence and Logic Programming, Volume 3: Nonmonotonic Reasoning and Uncertain Reasoning, Oxford University Press.
151. Lindley, D.V., 1985 Introduction to Probability and Statistics from a Bayesian Viewpoint, Cambridge University Press, London.
152. Lindsay, R. K., B. G. Buchanan, E.A. Feigenbaum, and J. Lederberg, 1980 Applications of Artificial Intelligence for Organic Chemistry: The Dendral Project, McGraw-Hill.
153. Lugg, J. A., J. Raifer, and C. N. F. González, 1995 “Dehydrotestosterone Is the Active Androgen in the Maintenance of Nitric Oxide- Mediated Penile Erection in the Rat,” Endocrinology, Vol. 136, No. 4.
154. Mani, S., S. McDermott, and M. Valtorta, 1997 “MENTOR: A Bayesian Model for Prediction of Mental Retardation in Newborns,” Research in Developmental Disabilities, Vol. 8, No.5.
155. Manning, C., and Schuetze, H., 2003 Foundations of Statistical Natural Language Processing, MIT Press.
156. Marcus, P., B. Santorini, and M. Marcinkiewicz, 1993 “Building a Large Annotated Corpus of English: The Penn Treebank,” Computational Linguistics, Vol. 19, No. 2.
157. Margaritis, D., C. Faloutsos, and S. Thrun, 2001 “NetCube: A Scalable Tool for Fast Data Mining and Compression,” Proceedings of the 27th VLB Conference, Rome, Italy.
158. McCarthy, J., 1958 “Programs with Common Sense,” Symposium on Mechanization of Thought Processes. National Physical Laboratory, Teddington, England.
159. McCarthy, J., 1969 “Circumscription—A Form of Non-Monotonic Reasoning,” Artificial Intelligence, Vol. 13.
160. McCarthy, J., 2007 From Here to Human-Level AI, Artificial Intelligence, Vol. 171, No. 18.
161. McCarthy, J., and P. J. Hayes, 1969 “Some Philosophical Problems From the Standpoint of Artificial Intelligence, Machine Intelligence, Vol. 4.
162. McCorduck, P., 2004 Machines Who Think, A.K. Peters, Ltd.
163. McCulloch, W., and W. Pitts, 1943 “A Logical Calculus of the Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics, Vol. 5.
164. McDermott, J., 1982 “A Rule-Based Configurer of Computer Systems,” Artificial Intelligence, Vol. 19, No. 1.
165. McDermott, D., and J. Doyle, 1980 “Nonmonotonic Logic 1,” Artificial Intelligence, Vol. 13.
166. McLachlan, G. J., and T. Krishnan, 2008 The EM Algorithm and Extensions, Wiley.
167. Meek, C., 1995 “Strong Completeness and Faithfulness in Bayesian Networks,” in Besnard, P., and S. Hanks (Eds.): Uncertainty in Artificial Intelligence; Proceedings of the Eleventh Conference, Morgan Kaufmann.
168. Meek, C., 1997 “Graphical Models: Selecting Causal and Statistical Models,” Ph.D. dissertation, Carnegie Mellon University.
169. Metropolis, N., A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller, 1953 “Equation of State Calculation by Fast Computing Machines,” Journal of Chemical Physics, Vol. 21.
170. Meuleau, N., and P. Bourgine, 1999 “Exploration of Multi-State Environments: Local Measures and Back-Propagation of Uncertainty,” Machine Learning, Vol. 35, No. 2.
171. Meystre, S., and PJ Haug, 2005 “Automation of a Problem List Using Natural Language Processing,” BMC Medical Informatics and Decision Making, Vol. 5, No. 30.
172. Minsky, M.L., P. Singh, and A. Sloman, 2004 “The St. Thomas Commonsense Symposium: Designing Architectures for Human- Level Intelligence, AI Magazine, Vol. 25, No. 2.
173. Minsky, M.L., 2007 The Emotion Machine: Commonsense Thinking, Artificial Intelligence and the Future of the Human Mind, Simon and Schuster.
174. Montes de Oca, M.A., T. Stützle, K. Van den Enden, and M. Dorigo, 2011 “Incremental Social Learning in Particle Swarms,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 41, No. 2.
175. Morjaia, M., F. Rink, W. Smith, G. Klempner, C. Burns, and J. Stein, 1993 “Commercialization of EPRI’s Generator Expert Monitoring System (GEMS),” in Expert System Application for the Electric Power Industry, EPRI, Phoenix, AZ.
176. Neal, R., 1992 “Connectionist Learning of Belief Networks,” Artificial Intelligence, Vol. 56.
177. Neapolitan, R. E., 1989 Probabilistic Reasoning in Expert Systems, Wiley.
178. Neapolitan, R. E., 1996 “Is Higher-Order Uncertainty Needed?” in IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, Vol. 26, No. 3.
179. Neapolitan, R. E., 2004 Learning Bayesian Networks, Prentice Hall.
180. Neapolitan, R. E., 2009 Probabilistic Methods for Bioinformatics, Morgan Kaufmann.
181. Neapolitan, R. E., and X. Jiang, 2007 Probabilistic Methods for Financial and Marketing Informatics, Morgan Kaufmann.
182. Neapolitan, R. E., and K. Naimipour, 2010 Foundations of Algorithms, Jones and Bartlett.
183. Nease, R.F., and D.K. Owens, 1997 “Use of Influence Diagrams to Structure Medical Decisions,” Medical Decision Making, Vol. 17.
184. Nefian, A.F., L.H. Liang, X.X. Liu, X. Pi and K. Murphy, 2002 “Dynamic Bayesian Networks for Audio-Visual Speech Recognition,” Journal of Applied Signal Processing, Special Issue on Joint Audio Visual Speech Processing, Vol. 11.
185. Newell, A., and H. Simon, 1961 “GPS, a Program that Simulates Human Thought,” in Building, H. (Ed.): Lerenede Automaten, R. Oldenbourg.
186. Newell, A., 1981 “The Knowledge Level,” AI Magazine, Summer.
187. Nicholson, A.E., 1996 “Fall Diagnosis Using Dynamic Belief Networks,” Proceedings of the 4th Pacific Rim International Conference on Artificial Intelligence (PRICAI-96), Cairns, Australia.
188. Nillson, N.J., 1991 “Logic and Artificial Intelligence,” Artificial Intelligence, Vol. 47.
189. Nillson, N.J., 1994 “Teleo-Reactive Programs for Agent Control,” Journal of Artificial Intelligence Research, Vol. 1.
190. Norwick, S.M., M.E. Dean, D.L. Dill, and M. Horowitz, 1993 “The Design of a High- Performance Cache Controller: A Case Study in Synchronous Synthesis,” Integration, the VLSI Journal, Vol. 15, No. 3.
191. Ogunyemi, O., J. Clarke, N. Ash, and B. Webber, 2002 “Combining Geometric and Probabilistic Reasoning for Computer-Based Penetrating-Trauma Assessment,” Journal of the American Medical Informatics Association, Vol. 9, No. 3.
192. Olesen, K. G., S. L. Lauritzen, and F. V. Jensen, 1992 “aHUGIN: A System Creating Adaptive Causal Probabilistic Networks,” in Dubois, D., M. P. Wellman, B. D’Ambrosio, and P. Smets (Eds.): Uncertainty in Artificial Intelligence; Proceedings of the Eighth Conference, Morgan Kaufmann.
193. Olmsted, S.M., 1983 “On Representing and Solving Influence Diagrams,” Ph.D. dissertation, Dept. of Engineering-Economic Systems, Stanford University, California.
194. Onisko, A., 2001 “Evaluation of the Hepar II System for Diagnosis of Liver Disorders,” Working Notes on the European Conference on Artificial Intelligence in Medicine (AIME- 01): Workshop Bayesian Models in Medicine,” Cascais, Portugal.
195. Partridge, B., 1982 “The Structure and Function of Fish Schools,” Scientific American, Vol. 246, No 6.
196. Pearl, J., 1986 “Fusion, Propagation, and Structuring in Belief Networks,” Artificial Intelligence, Vol. 29.
197. Pearl, J., 1988 Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann.
198. Pearl, J., 2000 Causality: Models, Reasoning, and Inference, Cambridge University Press.
199. Pearl, J., D. Geiger, and T. S. Verma, 1989 “The Logic of Influence Diagrams,” in Oliver, R.M., and J. Q. Smith (Eds.): Influence Diagrams, Belief Networks and Decision Analysis, Wiley, 1990. (a shorter version originally appeared in Kybernetica, Vol. 25, No. 2.)
200. Pham, T.V., M. Worring, A. W. Smeulders, 2002 “Face Detection by Aggregated Bayesian Network Classifiers,” Pattern Recognition Letters, Vol. 23. No. 4.
201. Piaget, J., 1966 The Child’s Conception of Physical Causality, Routledge and Kegan Paul.
202. Pinker, S., 2007 The Stuff of Thought: Language as a Window into Human Nature, Viking.
203. Potvin, J.Y., 1983 “The Traveling Salesperson Problem: A Neural Network Perspective,” ORSA Journal on Computing, Vol. 5, No. 4.
204. Pradhan, M., and P. Dagum, 1996 “Optimal Monte Carlo Estimation of Belief Network Inference,” in Horvitz, E., and F. Jensen (Eds.): Uncertainty in Artificial Intelligence; Proceedings of the Twelfth Conference, Morgan Kaufmann.
205. Quinlan, J.R., 1983 “Learning Efficient Classification Procedures and their Application to Chess Endgames,” in Michalski. R.S., J.G. Carbonell, and T.M. Mitchell (Eds.), Machine Learning — An Artificial Intelligence Approach, Tioga.
206. Quinlan, J.R., 1986 “Induction of Decision Trees,” Machine Learning, Vol. 1, No. 1.
207. Quinlan, J.R., 1987 “Simplifying Decision Trees,” International Journal of Man-Machine Studies, Vol. 27, No. 3.
208. Rechenberg, I., 1994 Evolution Strategies, in Zurada, J.M., R.J. Marks II, and C.J. Robinson (Eds.): Computational Intelligence: Imitating Life, IEEE Press.
209. Reiter, R., 1980 “A Logic for Default Reasoning,” Artificial Intelligence, Vol. 13.
210. Reiter, R., 1991 “The Frame Problem in the Situation Calculus: A Simple Solution (Sometimes) and a Completeness Result for Goal Regression,” in Lifschitz, V. (Ed.): Artificial Intelligence and Mathematical Theory of Computation: Papers in Honor of John Mc- Carthy, Academic Press.
211. Reynolds, C.W., 1987 “Flocks, Herds, and Schools: A Distributed Behavioral Model,” Computer Graphics, Vol. 21, No. 4.
212. Robbins, H., 1952 “Some Aspects of the Sequential Design of Experiments,” Bulletin of the American Mathematical Society, Vol. 58, No. 5.
213. Robinson, R. W., 1977 “Counting Unlabeled Acyclic Digraphs,” in Little, C. H. C. (Ed.): Lecture Notes in Mathematics, 622: Combinatorial Mathematics V, Springer-Verlag.
214. Rosenschein, S.J., 1985 “Formal Theories of Knowledge in AI and Robotics,” New Generation Computing, Vol.3, No. 4.
215. Royalty, J., R. Holland, A. Dekhtyar, and J. Goldsmith, 2002 “POET, The Online Preference Elicitation Tool,” Proceedings of AAAI Workshop on Preferences in AI and CP.
216. Ruhnka, J.C., H.D. Feldman, and T.J. Dean, 1992 “The ‘Living Dead’ Phenomena in Venture Capital Investments,” Journal of Business Venturing, Vol. 7, No. 2.
217. Russell, S., and P. Norvig, 1995 Artificial Intelligence A Modern Approach, Prentice Hall.
218. Salmon, W., 1997 Causality and Explanation, Oxford University Press.
219. Savage, L.J., 1954 Foundations of Statistics, Wiley, New York.
220. Scheines, R., P. Spirtes, C. Glymour, and C. Meek, 1994 Tetrad II: User Manual, Erlbaum.
221. Searle, J.R., 1980 “Mind, Brains, and Programs,” Behavioral and Brain Sciences, Vol. 3.
222. Segal, E., D. Pe’er, A. Regev, D. Koller, and N. Friedman, 2005 “Learning Module Networks,” Journal of Machine Learning Research, Vol. 6.
223. Shachter, R. D., 1986 “Evaluating Influence Diagrams,” Operations Research, Vol. 34.
224. Shachter, R. D., and M. Peot, 1990 “Simulation Approaches to General Probabilistic Inference in Bayesian Networks,” in Henrion, M., R. D. Shachter, L. N. Kanal, and J. F. Lemmer (Eds.): Uncertainty in Artificial Intelligence 5, North-Holland.
225. Shannon, C.E., 1948 “A Mathematical Theory of Communication,” The Bell System Technical Journal, Vol. 27.
226. Shaparau, D., M. Pistore, and P. Traverso, 2008 “Fusing Procedural and Declarative Planning Goals for Nondeterministic Domains,” Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, Chicago, IL.
227. Shenoy, P., 2006 “Inference in Hybrid Bayesian Networks Using Mixtures of Gaussians,” in Dechter, R., and T. Richardson (Eds.): Uncertainty in Artificial Intelligence: Proceedings of the Twenty-Second Conference, AUAI Press.
228. Shepherd, D.A., and A. Zacharakis, 2002 “Venture Capitalists’ Expertise: A Call for Research into Decision Aids and Cognitive Feedback,” Journal of Business Venturing, Vol. 17.
229. Shiller, R.J., 2000 Irrational Exuberance, Princeton University Press.
230. Simon, H., 1957 Models of Man: Social and Rational, Wiley.
231. Sims, M.H., 1987 “Empirical and Analytic Discovery in IL,” Proceedings of the 4h International Machine Learning Workshop, Morgan Kaufmann.
232. Singh, M., and M. Valtorta, 1995 “Construction of Bayesian Network Structures from Data: A Brief Survey and an Efficient Algorithm,” International Journal of Approximate Reasoning, Vol. 12.
233. Spirtes, P., C. Glymour, and R. Scheines, 2000 Causation, Prediction, and Search, Springer-Verlag, New York, 1993; second edition, MIT Press.
234. Srinivas, S., 1993 “A Generalization of the Noisy OR Model,” in Heckerman, D., and A. Mamdani (Eds.): Uncertainty in Artificial Intelligence; Proceedings of the Ninth Conference, Morgan Kaufmann.
235. Strutt, J.E., and P.L. Hall, 2003 Global Vehicle Reliability: Prediction and Optimization Techniques, Wiley.
236. Sun, L., and P. Shenoy, 2006 “Using Bayesian Networks for Bankruptcy Prediction: Some Methodological Issues,” School of Business Working Paper No. 302, University of Kansas, Lawrence, KS.
237. Süral, H., N.E. Özdemirel, I Önder, and M.S. Turan, 2010 “An Evolutionary Approach for the TSP and the TSP with Backhauls,” in Tenne, Y., and C.K. Goh (Eds.): Computational Intelligence in Expensive Optimization Problems, Springer-Verlag.
238. Szolovits, P., and S.G. Pauker, 1978 “Categorical and Probabilistic Reasoning in Medical Diagnosis,” Artificial Intelligence, Vol. 11.
239. Tatman, J.A., and R.D. Shachter, 1990 “Dynamic Programming and Influence Diagrams,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 20, No. 2.
240. Tierney, L., 1996 “Introduction to General State- Space Markov Chain Theory,” in Gilks, W. R., S. Richardson, and D. J. Spiegelhalter (Eds.): Markov Chain Monte Carlo in Practice, Chapman … Hall/CRC.
241. Torres-Toledano, J.G and L.E. Sucar, 1998 “Bayesian Networks for Reliability Analysis of Complex Systems,” in Coelho, H. (Ed.): Progress in Artificial Intelligence - IBERAMIA 98, Springer-Verlag, Berlin
242. Turing, A., 1950 “Computing Machinery and Intelligence,” Mind, Vol. 59.
243. Tversky, A., and D. Kahneman, 1981 “The Framing of Decisions and the Psychology of Choice,” Science, Vol. 211.
244. Valadares, J. 2002 “Modeling Complex Management Games with Bayesian Networks: The FutSim Case Study,” Proceeding of Agents in Computer Games, a Workshop at the 3rd International Conference on Computers and Games (CG’02), Edmonton, Alberta, Canada.
245. van Lambalgen, M., 1987 “Random Sequences,” Ph.D. thesis, University of Amsterdam.
246. VanLehn, K., C. Lynch, K. Schulze, J.A. Shapiro, R. Shelby, L. Taylor, D. Treacy, A. Weinstein, and M. Wintersgill, 2005 “The Andes Physics Tutoring System: Lessons Learned,” International Journal of Artificial Intelligence and Education, Vol. 15, No. 3.
247. Vermorel, J., and M. Mohri, 2005 “Multi-Armed Bandit Algorithms and Empirical Evaluation,” European Conference on Machine Learning.
248. Vicsek, T., A. Czirók, E. Ben-Jacob, and O. Shochet, 1995 “Novel Type of Phase Transition in a System of Self-Driven Particles,” Physical Review Letters, Vol. 75.
249. von Mises, R., 1919 “Grundlagen der Wahrscheinlichkeitsrechnung,” Mathematische Zeitschrift, Vol. 5.
250. Williams, B., M. Ingham, S. Chung, and P. Elliot, 2003 “Model-based Programming of Intelligent Embedded Systems and Robotic Space Explorers,” Proceedings of IEEE Special Issue on Modeling and Design of Embedded Software.
251. Winograd, T., 1972 “Understanding Natural Language,” Cognitive Psychology, Vol. 3, No. 1.
252. Winston, P., 1973 “Progress in Vision and Robotics,” M.I.T. Artificial Intelligence TR-281.
253. Wright, S., 1921 “Correlation and Causation” Journal of Agricultural Research, Vol. 20.
254. Xiang, Y., S. K. M. Wong, and N. Cercone, 1996 “Critical Remarks on Single Link Search in Learning Belief Networks,” in Horvitz, E., and F. Jensen (Eds.): Uncertainty in Artificial Intelligence; Proceedings of the Twelfth Conference, Morgan Kaufmann.
255. Yob, G., 1975 “Hunt the Wumpus,” Creative Computing, Vol. 1, No. 5, Sep-Oct.
256. Zadeh, L., 1965 “Fuzzy sets,” Information and Control, Vol. 8, No. 3.
257. Zell, J., and R. Mooney, 1996 “Learning to Parse Database Queries Using Inductive Logic Programming,” Proceedings of AAAI- 96, MIT Press.