Annals of Operations Research

, Volume 12, Issue 1, pp 1–50 | Cite as

A selected artificial intelligence bibliography for operations researchers

  • Brigitte Jaumard
  • Peng Si OW
  • Bruno Simeone
Article

Abstract

We have compiled a selected, classified, and annotated Artificial Intelligence bibliography specifically addressed to an operations research audience. The bibliography includes approximately 450 references from the areas of search (including heuristics and games), automatic deduction (including theorem proving, logic programming, and logical aspects of databases), planning, learning, and knowledge-based systems (with numerous specific applications to management, engineering, science, medicine, and other fields). We have also added a general references section, as well as a special section on Artificial Intelligence/Operations Research interfaces.

Keywords

Artificial Intelligence General Reference Operation Research Logic Programming Special Section 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Bramer, M.A. and D. Bramer,The Fifth Generation: An Annotated Bibliography, Addison-Wesley, Reading, MA, 1984.Google Scholar
  2. [2]
    Gevarter, W.B.,Artificial Intelligence, Expert Systems, Computer Vision, and Natural Language Processing, Noyes Publications, Park Ridge, NJ, 1984.Google Scholar
  3. [3]
    Glover, F., Future paths for integer programming and links to artificial intelligence,Computers and Operations Research 13(5) (1987) 533–549.Google Scholar
  4. [4]
    Gonzalez, R.C. and P. Wintz,Digital Image Processing, Addison-Wesley, Reading, MA, 1977.Google Scholar
  5. [5]
    Papadimitriou, C.H., An algorithm for shortest path motion in three dimensions,Information Processing Letters 20 (1985) 259–263.Google Scholar
  6. [6]
    Pratt, W.K.,Digital Image Processing, Wiley, New York, 1978.Google Scholar
  7. [7]
    Rylko, H.M.,Artificial Intelligence: bibliographic summaries of the select literature, Vol. I, A Report Store Consensus Bibliography, Lawrence, KS, 1984.Google Scholar
  8. [8]
    Rylko, H.M.,Artificial Intelligence: bibliographic summaries of the select literature, Vol. II, A Report Store Consensus Bibliography, Lawrence, KS, 1985.Google Scholar
  9. [9]
    Wagner, H.,Principles of Management Science, Prentice-Hall, Englewood Cliffs, NJ, 1970.Google Scholar
  10. [10]
    Andrew, A.M.,Artificial Intelligence, Abacus Press, 1983. (Vol. 3 in the Cybernetics and Systems Series, J. Rose, series ed.)Google Scholar
  11. [11]
    Barr, A. and E.A. Feigenbaum,The Handbook of Artificial Intelligence, Vol. I, William Kaufman, Los Altos, CA, 1981.Google Scholar
  12. [12]
    Barr, A. and E.A. Feigenbaum,The Handbook of Artificial Intelligence, Vol. II, William Kaufman, Los Altos, CA, 1982.Google Scholar
  13. [13]
    Bramer, M.A. and D. Bramer,The Fifth Generation: An Annotated Bibliography, Addison-Wesley, Reading, MA, 1984.Google Scholar
  14. [14]
    Charniak, E. and D.V. McDermott,Introduction to Artificial Intelligence, Addison-Wesley, Reading, MA, 1985.Google Scholar
  15. [15]
    Clocksin, W.F. and C.S. Mellish,Programming in PROLOG, Springer-Verlag, New York, 1981.Google Scholar
  16. [16]
    Cohen, P.R. and E.A. Feigenbaum,The Handbook of Artificial Intelligence, Vol. III, William Kaufman, Los Altos, CA, 1982.Google Scholar
  17. [17]
    Dreyfus, H.L. and S.E. Dreyfus,Mind over Machine, MacMillan/The Free Press, New York, 1985.Google Scholar
  18. [18]
    Feigenbaum, E.A. and J. Feldman, eds.,Computers and Thought, McGraw-Hill, New York, 1963.Google Scholar
  19. [19]
    Feigenbaum, E.A. and P. McCorduck,The Fifth Generation: Artificial Intelligence and Japan's Computer Challenge to the World, Addison-Wesley, Reading, MA, 1983.Google Scholar
  20. [20]
    Fischler, M.A. and O. Firschein,Intelligence: the Eye, the Brain and the Computer, Addison-Wesley, Reading, MA, 1977.Google Scholar
  21. [21]
    Giannesini, F., H. Hanoui, R. Pasero and M. van Caneghem,PROLOG, Addison-Wesley, Reading, MA, 1986.Google Scholar
  22. [22]
    Nilsson, N.J.,Problem-solving Methods in Artificial Intelligence, McGraw-Hill, New York, 1971.Google Scholar
  23. [23]
    Nilsson, N.J.,Principles of Artificial Intelligence, Tioga, Palo Alto, CA, 1980.Google Scholar
  24. [24]
    Rich, E.,Artificial Intelligence, McGraw-Hill, New York, 1983.Google Scholar
  25. [25]
    Rylko, H.M.,Artificial Intelligence: bibliographic summaries of the select literature, Vol. I, A Report Store Consensus Bibliography, Lawrence, KS, 1984.Google Scholar
  26. [26]
    Rylko, H.M.,Artificial Intelligence: bibliographic summaries of the select literature, Vol. II, A Report Store Consensus Bibliography, Lawrence, KS, 1985.Google Scholar
  27. [27]
    O'Shea, T. and M. Eisenstadt,Artificial Intelligence: Tools, Techniques and Applications, Harper and Row, New York, 1984.Google Scholar
  28. [28]
    Scientific Datalink, The Scientific Datalink Index to Artificial Intelligence Research (1954–1984) and (1985 supplement), Comtex Scientific Corporation, New York, (Authors, Titles, Abstracts, Subjects).Google Scholar
  29. [29]
    Simon, H.A.,Science of the Artificial, M.I.T. Press, Cambridge, MA, 1969.Google Scholar
  30. [30]
    Winograd, T., R. Davis, S.E. Dreyfus and B. Smith, Expert systems: how far can they go?,Proceedings of IJCAI 9 (1985) 1306–1309.Google Scholar
  31. [31]
    Winston, P.H.,Artificial Intelligence, Addison-Wesley, Reading, MA, 1984, 2nd edition.Google Scholar
  32. [32]
    Winston, P.H. and R.H. Brown,Arificial Intelligence: An MIT Perspective, Vols. 1 and 2, M.I.T. Press, Cambridge, MA, 1979.Google Scholar
  33. [33]
    Blair, C.E., R.G. Jeroslow and J.K. Lowe, Some results and experiments in programming techniques for propositional logic,Computers and Operations Research 13(5) (1986) 633–645.Google Scholar
  34. [34]
    Glover, F., Future paths for integer programming and links to artificial intelligence,Computers and Operations Research 13(5) (1986) 533–549.Google Scholar
  35. [35]
    Grant, T.J., Lessons for O.R. from A.I.: a scheduling case study,Journal of the Operational Research Society 37(1) (1986) 41–57.Google Scholar
  36. [36]
    Jeroslow, R.G., An extension of mixed-integer programming models and techniques to some database and Artificial Intelligence settings, Technical Report, College of Management, Georgia Institute of Technology, March 1987.Google Scholar
  37. [37]
    Kusiak, A., Applications of operational research models and techniques in flexible manufacturing systems,European Journal of Operational Research 24(3) (1986) 336–345.Google Scholar
  38. [38]
    Kusiak, A., Artificial intelligence and operations research in flexible manufacturing systems,INFOR 25(1) (1987) 2–12.Google Scholar
  39. [39]
    O'Keefe, R.M., Expert systems and operational research — mutual benefits,Journal of the Operational Research Society 36(2) (1985) 125–129.Google Scholar
  40. [40]
    Paul, R.J. and G.I. Doukidis, Further developments in the use of artificial intelligence techniques which formulate simulation problems,Journal of the Operational Research Society 37(8) (1986) 787–810.Google Scholar
  41. [41]
    Phelps, R.I., Artificial intelligence, an overview of similarities with O.R.,Journal of the Operational Research Society 37(1) (1986) 13–20.Google Scholar
  42. [42]
    Radermacher, F.J., ed.,Proceedings of the International Conference on multi-attribute decision-making via OR-based expert systems, West Germany, April 1987.Google Scholar
  43. [43]
    Reggia, J.A., D.S. Nau and Y. Peng, A formal model of diagnostic inference I: problem formulation and decomposition,Information Sciences 37 (1985) 227–256.Google Scholar
  44. [44]
    Reggia, J.A., D.S. Nau and Y. Wang, A formal model of diagnostic inference II: algorithmic solution and application,Information Sciences 37 (1985) 257–285.Google Scholar
  45. [45]
    Shaw, M.J. and A.B. Whinston, Applications of artificial intelligence to planning and scheduling in flexible manufacturing, in A. Kusiak ed.,Flexible Manufacturing Systems: Methods and Studies, North-Holland, Amsterdam (1986) 223–242.Google Scholar
  46. [46]
    Amarel, S., An approach to heuristic problem-solving and theorem proving in the propositional calculus, in: J. Hart and S. Tahasu, eds.,Systems and Computer Science, University of Toronto Press, Toronto, 1967.Google Scholar
  47. [47]
    Evans, T.G., A heuristic program to solve geometric analogy problems, in: M. Minsky, ed.,Semantic Information Processing, M.I.T. Press, Cambridge, MA, 1963.Google Scholar
  48. [48]
    Kowalski, R., AND/OR graphs, theorem proving graphs, and bidirectional search,Machine Intelligence 7 (1972) 167–194.Google Scholar
  49. [49]
    Kowalski, R.A.,Logic for Problem Solving, Elsevier, New York, 1979.Google Scholar
  50. [50]
    Laurière, J.L., A language and a program for stating and solving combinatorial problems,Artificial Intelligence 10 (1978) 29–127.Google Scholar
  51. [51]
    Levi, G. and F. Sirovich, Generalized AND/OR graphs,Artificial Intelligence 7 (1976) 243–259.Google Scholar
  52. [52]
    Vanderbrug, G.J., Problem representations and formal properties of heuristic search,Information Sciences 2 (1976) 279–307.Google Scholar
  53. [53]
    Fisher, M.L., Worst-case analysis of heuristic algorithms,Management Science 26 (1980) 1–17.Google Scholar
  54. [54]
    Graham, R.L., E.L. Lawler, J.K. Lenstra and A.H.G. Rinnooy Kan, Optimization and approximation in deterministic sequencing and scheduling: a survey,Annals of Discrete Mathematics 5 (1979) 287–326.Google Scholar
  55. [55]
    Groner, R., M. Groner and W.F. Bischof, eds.,Methods of Heuristics, Lawrence Erlbaum Associates Publishers, 1983.Google Scholar
  56. [56]
    Hu, T.C. and E.S. Kuh, Theory and concepts of circuit layout, in: T.C. Hu and E.S. Kuh, eds.,VLSI Circuit Layout: Theory and Design, IEEE Press, New York, 1985.Google Scholar
  57. [57]
    Johnson, D.S., Approximation algorithms for combinatorial problems,Journal of Computer and System Sciences 9 (1976) 256–278.Google Scholar
  58. [58]
    Kusiak, A., ed.,Flexible Manufacturing Systems: Methods and Studies, North-Holland, Amsterdam, 1986.Google Scholar
  59. [59]
    Lenat, D.B., Discovery in mathematics as heuristic search, Ph.D. Dissertation, Computer Science Department, Carnegie-Mellon University, 1977.Google Scholar
  60. [60]
    Lenat, D.B., The nature of heuristics, Computer Science Department, Stanford University, 1980.Google Scholar
  61. [61]
    Lin, S., Computer solutions of the travelling salesman problem,Bell Systems Technical Journal 44 (1965) 2245–2269.Google Scholar
  62. [62]
    Minsky, M., Heuristic aspects of the Artificial Intelligence problem, Group Report No. 34-55, Lincoln Laboratory, MIT (ASTIA Doc. No. AS236885), Lexington, MA, 1956.Google Scholar
  63. [63]
    Mueller-Merbach, H., Heuristic methods: structures, applications, computational experience, in: R. Cottle and J. Krarup, eds.,Optimization Methods for Resource Allocation, English Universities Press, London, 1974, 401–416.Google Scholar
  64. [64]
    Newell, A. and H.A. Simon, Heuristic problem solving: the next advance in Operations Research,Operations Research 6(1) (1958).Google Scholar
  65. [65]
    Newell, A. and H.A. Simon,Human Problem Solving, Prentice-Hall, Englewood Cliffs, NJ, 1972.Google Scholar
  66. [66]
    Pearl, J., Heuristic search theory: a survey of recent results,Proceedings of IJCAI 7, (1981) 24–28.Google Scholar
  67. [67]
    Pearl, J.,Search and Heuristics, North-Holland, Amsterdam, 1983.Google Scholar
  68. [68]
    Pearl, J.,Heuristics: Intelligent Search Strategies for Computer Problem Solving, Addison-Wesley, Reading, MA, 1984.Google Scholar
  69. [69]
    Pohl, I., Practical and theoretical considerations in heuristic search algorithms, in: E.W. Elcock and D. Michie, eds.,Machine Intelligence 8, Wiley, New York, 1977, 55–72.Google Scholar
  70. [70]
    Slagle, J.R.,Artificial Intelligence: The Heuristic Programming Approach, McGraw-Hill, New York, 1971.Google Scholar
  71. [71]
    Sussman, G. and R. Stallman, Heuristic techniques in computer-aided circuit analysis,IEEE Transactions on Circuits and Systems, CAS 22(11) (1975) 857–865.Google Scholar
  72. [72]
    Thompson, G.L., Heuristic programming: theory and computation, in: J.F. Pierce, ed.,Operations Research and the Design of Management Information Systems, Techn. Ass. of the Pulp and Paper Industry, New York, 1967.Google Scholar
  73. [73]
    Zadeh, L.A., Approximate reasoning based on fuzzy logic,Proceedings of IJCAI 6 (1979) 1004–1010.Google Scholar
  74. [74]
    Deo, N. and C. Pang, Shortest path algorithms: taxonomy and annotations, CS-80-057, Computer Science Department, Washington State University, 1971.Google Scholar
  75. [75]
    Gallo, G. and S. Pallottino, Shortest path methods: a unifying approach,Mathematical Programming Studies 26 (1986) 38–64.Google Scholar
  76. [76]
    Horowitz, E. and S. Sahni,Fundamentals of Computer Algorithms, Computer Science Press, Potomac, MD, 1978.Google Scholar
  77. [77]
    Tarjan, R.E., Depth-first search and linear graph algorithms,SIAM Journal of Computing 1 (1972) 146–160.Google Scholar
  78. [78]
    Bagchi, A. and A. Mahanti, Search algorithms under different kinds of heuristics — a comparative study,Journal of ACM 30(1) (1983) 1–21.Google Scholar
  79. [79]
    Camerini, P.M. and F. Maffioli, Heuristically guided algorithm fork-parity matroid problems,Discrete Mathematics 21 (1978) 103–116.Google Scholar
  80. [80]
    Chakrabarti, P.P., S. Ghose and S.C. De Sarkar, Heuristic search through islands,Artificial Intelligence 29 (1986) 339–347.Google Scholar
  81. [81]
    De Champeaux, B. and L. Sint, An improved bi-directional heuristic search algorithm,Journal of ACM 24 (1977) 177–191.Google Scholar
  82. [82]
    Dechter, R. and J. Pearl, Generalized best first strategies and the optimality ofA*,Journal of ACM 32 (1985) 505–536.Google Scholar
  83. [83]
    Hart, P.E., N.J. Nilsson and B. Raphael, A formal basis for the heuristic determination of minimum test paths,IEEE Transactions on Systems and Cybernetics SSC-4(2) (1968) 100–107.Google Scholar
  84. [84]
    Hart, P.E., N.J. Nilsson and B. Raphael, Correction to a formal basis for the heuristic determination of minimum cost paths,SIGART Newsletter 37 (1972) 28–29.Google Scholar
  85. [85]
    Ibaraki, T., The power of dominance relations in branch-and-bound algorithms,Journal of ACM 24(2) (1977) 264–279.Google Scholar
  86. [86]
    Ibaraki, T., Branch-and-bound procedures and state space representation of combinatorial optimization problems,Information and Control 36 (1978 a) 1–27.Google Scholar
  87. [87]
    Ibaraki, T.,M-depth search in branch-and-bound algorithms,International Journal on Computers and Information Science 7 (1978 b) 315–373.Google Scholar
  88. [88]
    Kernighan, B.W. and S. Lin, An efficient heuristic procedure for partitioning graphs,Bell Systems Technical Journal 29 (February 1970) 291–307.Google Scholar
  89. [89]
    Korf, R.E., Depth-first iterative-deepening: an optimal admissible tree search,Artificial Intelligence 27 (1985) 97–109.Google Scholar
  90. [90]
    Krishnamurthy, B., An improved min-cut algorithm for partitioning VLSI networks,IEEE Transactions on Computers C-33(5) (May 1984) 438–446.Google Scholar
  91. [91]
    Laurierè, J.L., A language and a program for stating and solving combinatorial problems,Artificial Intelligence 10 (1978) 29–127.Google Scholar
  92. [92]
    Lawler, E.L. and D.E. Wood, Branch-and-bound methods: a survey,Operations Research 14 (1966) 699–719.Google Scholar
  93. [93]
    Lin, S., Computer solutions of the travelling salesman problem,Bell Systems Technical Journal 44 (1965) 2245–2269.Google Scholar
  94. [94]
    Martelli, A., On the complexity of admissible search heuristics,Artificial Intelligence 8 (1977) 1–13.Google Scholar
  95. [95]
    Mérő, L., Some remarks on heuristic search algorithms,Proceedings IJCAI 7 (1981) 572–574.Google Scholar
  96. [96]
    Mitten, L.G., Branch-and-bound methods: general formulation and properties,Operations Research 18 (1970) 23–34.Google Scholar
  97. [97]
    Montanari, U., Heuristically guided search and chromosome matching,Artificial Intelligence 1 (1970) 227–245.Google Scholar
  98. [98]
    Morin, T.L. and R.E. Marsten, Branch and bound strategies for dynamic programming,Operations Research 24 (1976) 611–627.Google Scholar
  99. [99]
    Nemhauser, G.L., A generalized permanent label setting algorithm for the shortest path between specified nodes,Journal of Mathematical Analysis and Applications 38 (1972) 328–334.Google Scholar
  100. [100]
    Pearl, J., Knowledge versus search: a quantitative analysis usingA*,Artificial Intelligence 20 (1983) 1–13.Google Scholar
  101. [101]
    Pearl, J.,Heuristics: Intelligent Search Strategies for Computer Problem Solving, Addison-Wesley, Reading, MA, 1984.Google Scholar
  102. [102]
    Pohl, I., First results on the effect of error in heuristic search, in: B. Meltger and D. Michie, eds.,Machine Intelligence 5, Elsevier, New York, 1970, 219–236.Google Scholar
  103. [103]
    Amarel, S., An approach to heuristic problem-solving and theorem proving in the propositional calculus, in: J. Hart and S. Tahasu, eds.,Systems and Computer Science, University of Toronto Press, Toronto, 1967.Google Scholar
  104. [104]
    Bagchi, A. and A. Mahanti, AND/OR graph heuristic search methods,Journal of ACM 32 (1985) 1–27.Google Scholar
  105. [105]
    Chang, C.L. and J.R. Slagle, An admissible and optimal algorithm for searching AND/OR graphs,Artificial Intelligence 2 (1971) 117–128.Google Scholar
  106. [106]
    Dechter, R. and J. Pearl, Generalized best first search strategies and the optimality ofA*,Journal of ACM 32 (1985) 505–536.Google Scholar
  107. [107]
    Hinxmann, A.I., Problem reduction and the two-dimensional trim-loss problem, in:Proceedings Summer Conference on Artificial Intelligence and Simulation of Behaviour, Department of Artificial Intelligence, University of Edinburgh, Scotland, 1976.Google Scholar
  108. [108]
    Kowalski, R., AND/OR graphs, theorem proving graphs, and bidirectional search,Machine Intelligence 7 (1972) 167–194.Google Scholar
  109. [109]
    Kumar, V. and L.N. Kanal, A general branch-and-bound formulation for understanding and synthesizing AND/OR tree search procedures,Artificial Intelligence 21 (1983) 179–198.Google Scholar
  110. [110]
    Martelli, A. and U. Montanari, Additive AND/OR graphs,Proceedings IJCAI 3(1973) 1–11.Google Scholar
  111. [111]
    Martelli, A. and U. Montanari, Optimizing decision trees through heuristically guided search,Communications of ACM 21 (1978) 1025–1039.Google Scholar
  112. [112]
    Nilsson, N.J., Searching problem solving and game-playing trees for minimal cost solutions, in: A.J.H. Morrell, ed.,Information Processing 68, Vol. 2, North-Holland, Amsterdam, 1969, 1556–1562.Google Scholar
  113. [113]
    Raiffa, H.,Decision Analysis, Addison-Wesley, Reading, MA, 1968.Google Scholar
  114. [114]
    Vanderbrug, G.J. and J. Minker, State-space, problem-reduction and theorem proving — some relationships,Communications of ACM 18 (1975) 107–115.Google Scholar
  115. [115]
    Bagchi, A. and A. Mahanti, Search algorithms under different kinds of heuristics — a comparative study,Journal of ACM 30(1) (1983) 1–21.Google Scholar
  116. [116]
    Dechter, R. and J. Pearl, Generalized best first strategies and the optimality ofA*,Journal of ACM 32 (1985) 505–536.Google Scholar
  117. [117]
    Gaschnig, J., Performance measurement and analysis of certain search heuristics, Ph.D. Dissertation, Computer Science Department, Carnegie-Mellon University, 1979.Google Scholar
  118. [118]
    Gelperin, D., On the optimality ofA*,Artificial Intelligence 8(1) (1977) 69–76.Google Scholar
  119. [119]
    Harris, L.R., The heuristic search under conditions of error,Artificial Intelligence 5(3) (1974) 217–134.Google Scholar
  120. [120]
    Hart, P.E., N.J. Nilsson and B. Raphael, A formal basis for the heuristic determination of minimum test paths,IEEE Transactions on Systems Science and Cybernetics SSC-4(2) (1968) 100–107.Google Scholar
  121. [121]
    Huyn, N., R. Dechter and J. Pearl, Probabilistic analysis of the complexity ofA*,Artificial Intelligence 15 (1980) 241–254.Google Scholar
  122. [122]
    Karp, R.M., The probabilistic analysis of some combinatorial search algorithms, in: J.F. Traub, ed.,Algorithms and Complexity: New Directions and Recent Results, Academic Press, New York, 1976, 1–19.Google Scholar
  123. [123]
    Knuth, D.E., Estimating the efficiency of backtrack programs,Mathematics of Computation 24 (1975) 121–136.Google Scholar
  124. [124]
    Munyer, J., Some results on the complexity of heuristic search in graphs, Technical Report HP-76-2, Information Sciences Department, University of California, Santa Cruz, 1976.Google Scholar
  125. [125]
    Pearl, J., Knowledge versus search: a quantitative analysis usingA*,Artificial Intelligence 20 (1983) 1–13.Google Scholar
  126. [126]
    Pohl I., First results on the effect of error in heuristic search, in: B. Meltger and D. Michie, eds.,Machine Intelligence 5, Elsevier, New York, 1970, 219–236.Google Scholar
  127. [127]
    Vanderbrug, G.J., Problem representations and formal properties of heuristic search,Information Sciences 2 (1976) 279–307.Google Scholar
  128. [128]
    Banerji, R.B.,Artificial Intelligence: A Theoretical Approach, North-Holland, Amsterdam, 1980.Google Scholar
  129. [129]
    Ernst, G.W. and M.M. Goldstein, Mechanical discovery of classes of problem-solving strategies,Journal of ACM 29(1) (1982) 1–23.Google Scholar
  130. [130]
    Gaschnig, J., A problem similarity approach to devising heuristics: first results,Proceedings IJCAI 6 (1979) 301–307.Google Scholar
  131. [131]
    Geoffrion, A.M., Elements of large scale mathematical programming. Part I: Concepts, in: A.M. Geoffrion, ed.,Perspectives in Optimization, Addison-Wesley, Reading, MA, 1972, 25–48.Google Scholar
  132. [132]
    Guida, G. and M. Somalvico, A method for computing heuristics in problem solving,Information Sciences 19 (1979) 251–259.Google Scholar
  133. [133]
    Kibler, D., Natural generation of admissable heuristics, Department of Computer Science, University of California at Irvine, 1982.Google Scholar
  134. [134]
    Lenat, D.B., EURISKO: A program that learns new heuristics and domain concepts. The nature of heuristics III: program design and results,Artificial Intelligence 21(1–2) (1983) 61–98.Google Scholar
  135. [135]
    Maffioli, F., M.G. Speranza and C. Vercellis, Randomized algorithms: an annotated bibliography,Annals of Operations Research 1 (1984) 331–345.Google Scholar
  136. [136]
    Mitchell, T.M., R. Utgoff, B. Nudel and R.B. Banerji, Learning problem solving heuristics through practice,Proceedings of IJCAI 7 (1981) 127–134.Google Scholar
  137. [137]
    Pearl, J., On the discovery and generation of certain heuristics,The UCLA Computer Science Quarterly 10(2) (1982) 121–132, Reprinted inAI Magazine (Winter/Spring 1983) 23–33.Google Scholar
  138. [138]
    Valtorta, M., A result on the computational complexity of heuristic estimates for theA* algorithm, Department of Conputer Science, Duke University, Durham, NC, 1981.Google Scholar
  139. [139]
    Waterman, D., Generalization of learning techniques for automating the learning of heuristics,Artificial Intelligence 1 (1970) 121–170.Google Scholar
  140. [140]
    Bacharach, M.,Economics and the Theory of Games, McMillan, London, 1976.Google Scholar
  141. [141]
    Berge, C.,Théorie Générale des jeux à n Personnes, Mem. des Sciences Math. 138, Gauthier-Villars, Paris, 1957.Google Scholar
  142. [142]
    Berlekamp, E.R., J.H. Conway and R.K. Guy,Winning Ways for your Mathematical Plays, Academic Press, New York, 1982.Google Scholar
  143. [143]
    Brams, S.J.,Game Theory and Politics, The Free Press, New York, 1975.Google Scholar
  144. [144]
    Gillies, D.B., Solutions to general non-zero-sum games,Annals of Mathematics Studies 40, Princeton University Press, Princeton, NJ, 1959, 47–85.Google Scholar
  145. [145]
    Kaplan, E.L.,Mathematical Programming and Games, Wiley, New York, 1982.Google Scholar
  146. [146]
    Luce, R.D. and H. Raiffa,Games and Decisions, Wiley, New York, 1957.Google Scholar
  147. [147]
    Owen, G.,Game Theory, W.B. Saunders, Philadelphia, 1968.Google Scholar
  148. [148]
    Shapley, L.S. and M. Shubik, Characteristic function, core and stable sets,Game Theory in Economics, Ch. 6, Rand Corporation Report R-904/6-NSF, 1973.Google Scholar
  149. [149]
    Von Neumann, J. and O. Morgenstern,Theory of Games and Economic Behavior, Princeton University Press, Princeton, NJ, 1944, 1967, 1983.Google Scholar
  150. [150]
    Acosta, A., Conditions for pathological search on MIN-MAX games, Master's thesis, Department of Engineering Systems, School of Engineering and Applied Science, University of California, Los Angeles, 1982.Google Scholar
  151. [151]
    Beal, D., An analysis of minimax, in: M.R.B. Clarke, ed.,Advances in Computer Chess 2, University Press, Edinburgh, 1980, 103–109.Google Scholar
  152. [152]
    Beal, D., Benefits of minimax search, in: M.R.B. Clarke, ed.,Advances in Computer Chess 3, Pergamon, Elmsford, NY, 1982, 17–24.Google Scholar
  153. [153]
    Berliner, H.J., TheB* tree search algorithm: a best-first proof procedure,Artificial Intelligence 12(1) (1979) 23–40.Google Scholar
  154. [154]
    Campbell, M.S. and T.A. Marshland, A comparison of minimax tree search algorithms,Artificial Intelligence 20(4) (1983) 347–367.Google Scholar
  155. [155]
    Hart, T.P. and D.J. Edwards, The tree prune (TP) algorithm,M.I.T. Artificial Intelligence Project Memo #30, R.L.E. and Computation Center, M.I.T., Cambridge, MA, 1961.Google Scholar
  156. [156]
    Ibaraki, T., Generalization of Alpha-Beta and SSS* search procedures,Artificial Intelligence 29 (1986) 73–117.Google Scholar
  157. [157]
    Knuth, D.E. and R.W. Moore, An analysis of Alpha-Beta pruning,Artificial Intelligence 6(4) (1975) 293–326.Google Scholar
  158. [158]
    Nau, D.S., An investigation of the causes of pathology in games, Technical Report TR-999, Computer Science Department, University of Maryland, 1981.Google Scholar
  159. [159]
    Nau, D.S., Pathology on game trees: a summary of results,Proceedings 1st National Conference on Artificial Intelligence (1980) 102–104.Google Scholar
  160. [160]
    Nau, D.S., Pathology on game trees revisited and an alternative to minimaxing,Artificial Intelligence 21(1–2) (1983) 221–244.Google Scholar
  161. [161]
    Newell, A., J.C. Shaw and H.A. Simon, Chess-playing programs and the problem of complexity,IBM Journal of Research and Development 2 (1958) 320–355.Google Scholar
  162. [162]
    Nilsson, N.J., Searching problem solving and game-playing trees for minimal cost-solutions, in: A.J.H. Morrell, ed.,Information Processing, North-Holland, Amsterdam, 1969, 1556–1562.Google Scholar
  163. [163]
    Pearl, J., Asymptotic properties of minimax trees and game searching procedures,Artificial Intelligence 14(2) (1980) 113–138.Google Scholar
  164. [164]
    Pearl, J., On the nature of pathology in game searching,Artificial Intelligence 20(4) (1983) 427–453.Google Scholar
  165. [165]
    Pearl, J., The solution for the branching factor of the Alpha-Beta pruning algorithm and its optimality,Communications of ACM 25(8) (1982) 559–564.Google Scholar
  166. [166]
    Roizen, I. and J. Pearl, A minimax algorithm better than Alpha-Beta? Yes and no,Artificial Intelligence 21(1–2) (1983) 199–220.Google Scholar
  167. [167]
    Slagle, J.R. and J.K. Dixon, Experiments with some programs that search game trees,Journal of ACM 16(2) (1969) 189–207.Google Scholar
  168. [168]
    Stockman, G., A minimax algorithm better than Alpha-Beta?Artificial Intelligence 12(2) (1979) 179–196.Google Scholar
  169. [169]
    Tarsi, M., Optimal search on some game trees,Journal of ACM 30(3) (1983) 389–396.Google Scholar
  170. [170]
    Verstraete, R., Models for game trees with independent terminal values, Technical Report UCLA-ENG-82-48, Cognitive Systems Laboratory, University of California, Los Angeles, 1982.Google Scholar
  171. [171]
    Berliner, H.J., A chronology of computer chess and its literature,Artificial Intelligence 10(2) (1978) 201–214.Google Scholar
  172. [172]
    Kister, J., P. Stein, S. Ulam, W. Walden and M. Wells, Experiments in chess,Journal of ACM 4 (1957) 174–177.Google Scholar
  173. [173]
    Korf, R.E., A program that learns to solve Rubik's cube,Proceedings of the 2nd AAAI Conference, Pittsburg, PA, 1982, 164–167.Google Scholar
  174. [174]
    Levy, D. and M. Newborn,All About Chess and Computers, Computer Science Press, Rockville, MD, 1982.Google Scholar
  175. [175]
    Newell, A., J.C. Shaw and H.A. Simon, Chess-playing programs and the problem of complexity,IBM Journal of Research and Development 2 (1958) 320–355.Google Scholar
  176. [176]
    Quinlan, J., Knowledge-based system for locating missing high cards in bridge,Proceedings of IJCAI 6 (1979) 705–707.Google Scholar
  177. [177]
    Samuel, A.L., Some studies in machine learning using the game of checkers,IBM Journal of Research and Development 3 (1959) 211–229.Google Scholar
  178. [178]
    Samuel, A.L., Some studies in machine learning using the game of checkers II — recent progress,IBM Journal of Research and Development 11(6) (1967) 601–617.Google Scholar
  179. [179]
    Shannon, C.E., Programming a computer for playing chess,Philosophical Magazine 41(7) (1950) 256–275.Google Scholar
  180. [180]
    Welsh, D.E.,Computer Chess, W.C. Brown, Dubuque, IA, 1984.Google Scholar
  181. [181]
    Wilkins, D., Using plans in chess,Proceedings of IJCAI 6 (1979) 960–967.Google Scholar
  182. [182]
    Baudet, G.M., On the branching factor of the Alpha-Beta pruning algorithm,Artificial Intelligence 10(2) (1978) 173–199.Google Scholar
  183. [183]
    Bratko, J. and M. Gams, Error analysis of the minimax principle, in: M.R.B. Clarke, ed.,Advances in Computer Chess 3, Pergamon, Elmsford, NY, 1982, 1–15.Google Scholar
  184. [184]
    Fuller, S.H., J. Gaschnig and J.J. Gillogly, An analysis of the Alpha-Beta pruning algorithm, Department of Computer Science Report, Carnegie-Mellon University, 1973.Google Scholar
  185. [185]
    Knuth, D.E. and R.W. Moore, An analysis of the Alpha-Beta pruning,Artificial Intelligence 6(4) (1975) 293–326.Google Scholar
  186. [186]
    Newborn, M., The efficiency of the Alpha-Beta search on trees with branch-dependent terminal node scores,Artificial Intelligence 8(2) (1977) 137–153.Google Scholar
  187. [187]
    Noe, T., A comparison of the Alpha-Beta and SCOUT algorithms using the game of Kalah, Technical Report UCLA-ENG-80-17, Cognitive Systems Laboratory, University of California, Los Angeles, 1980.Google Scholar
  188. [188]
    Pearl, J., The solution for the branching factor of the Alpha-Beta pruning algorithm and its optimality,Communications of ACM 25(8) (1982) 559–564.Google Scholar
  189. [189]
    Roizen, I., On the average number of terminal nodes examined by Alpha-Beta, Technical Report UCLA-ENG-81-01, Cognitive Systems Laboratory, University of California, Los Angeles, 1981.Google Scholar
  190. [190]
    Andrews, P.B., Theorem proving via general matings,Journal of ACM 28(2) (April 1981) 193–214.Google Scholar
  191. [191]
    Bibel, W., A comparative study of several proof procedures,Artificial Intelligence 18 (1982) 269–293.Google Scholar
  192. [192]
    Bibel, W.,Automated Theorem Proving, Vieweg, Braunschweig, 1982.Google Scholar
  193. [193]
    Biermann, A.W., Automatic programming: a tutorial on formal methodologies,Journal of Symbolic Computation 1 (1985) 119–142.Google Scholar
  194. [194]
    Bledsoe, W.W., Non-resolution theorem proving,Artificial Intelligence 9 (1977) 1–35.Google Scholar
  195. [195]
    Bobrow, D.G., ed., Special issue on non-monotonic logic,Artificial Intelligence 13(1,2) (1980).Google Scholar
  196. [196]
    Boyer, R.S. and J.S. Moore,A Computational Logic, Academic Press, New York, 1979.Google Scholar
  197. [197]
    Chang, C.L. and R.C. Lee,Symbolic logic and Mechanical Theorem Proving, Academic Press, New York, 1973.Google Scholar
  198. [198]
    Cohen, P.R. and E.A. Feigenbaum,The Handbook of Artificial Intelligence, Vol. III, William Kaufman, Los Altos, CA, 1982.Google Scholar
  199. [199]
    Davis, M. and H. Putnam, A computing procedure for quantification theory,Journal of ACM 7(3) (1960) 201–215.Google Scholar
  200. [200]
    Gelernter, H., Realization of a geometry theorem proving machine, in: E.A. Feigenbaum and J. Feldman, eds.,Computers and Thought, McGraw-Hill, New York, 1963, 134–152.Google Scholar
  201. [201]
    Kowalski, R.A., A proof procedure using connection graphs,Journal of ACM 22 (1975) 572–595.Google Scholar
  202. [202]
    Lewis, H.R. and C.H. Papadimitriou,Elements of the Theory of Computation, Prentice-Hall, Englewood Cliffs, NJ, 1981.Google Scholar
  203. [203]
    Loveland, D.,Automated Theorem Proving: A Logical Basis, North-Holland, Amsterdam, 1978.Google Scholar
  204. [204]
    Lusk, E.L. and R.A. Overbeck, Reasoning about equality,Journal of Automated Reasoning 1 (1985) 209–228.Google Scholar
  205. [205]
    Manna, Z.,Mathematical Theory of Computation, McGraw-Hill, New York, 1974.Google Scholar
  206. [206]
    Manna, Z. and R. Waldinger,The Logical Basis for Computer Programming, Vol. 1: Deductive Reasoning, Addison-Wesley, Reading, MA, 1985.Google Scholar
  207. [207]
    McCharen, J., R. Overbeck and L. Wos, Problems and experiments for and with automated theorem-proving programs,IEEE Transactions on Computation C-25 (1976).Google Scholar
  208. [208]
    Newell, A. and H.A. Simon, The logic theory machine,IRE Transactions on Information Theory 2 (1956) 61–79.Google Scholar
  209. [209]
    Nilsson, N.J.,Principles of Artificial Intelligence, Tioga, Palo Alto, CA, 1980.Google Scholar
  210. [210]
    Parrelo, B.D., W.C. Kabat and L. Wos, Job-shop scheduling using automated reasoning: A case study of the car sequencing problem,Journal of Automated Reasoning 2(1) (1986) 1–42.Google Scholar
  211. [211]
    Paul, E., Equational methods in first order predicate calculus,Journal of Symbolic Computation 1 (1985) 7–29.Google Scholar
  212. [212]
    Robinson, J.A.,Logic: Form and Function, Edinburgh University Press, Edinburgh, 1979.Google Scholar
  213. [213]
    Siekmann, J. and G. Wrightson, eds.,Automation of Reasoning 1: Classical Papers on Computational Logic 1957–1966, Symbolic Computation, Springer-Verlag, Heidelberg, 1983.Google Scholar
  214. [214]
    Siekmann, J. and G. Wrightson, eds.,Automation of Reasoning 2: Classical Papers on Computational Logic 1967–1970, Symbolic Computation, Springer-Verlag, Heidelberg, 1983.Google Scholar
  215. [215]
    Wang, H., Towards mechanical mathematics,IBM Journal of Research and Development 4 (1960) 2–22.Google Scholar
  216. [216]
    Wos, L., R. Overbeck, E. Lusk and J. Boyle,Automated Reasoning: Introduction and Applications, Prentice-Hall, Englewood Cliffs, NJ, 1984.Google Scholar
  217. [217]
    Wos, L., F. Pereira, R. Hong, R.S. Boyer, J.S. Moore, W.W. Bledsoe, L.J. Henschen, B.G. Buchanan, G. Wrightson and C. Green, An overview of automated reasoning and related fields,Journal of Automated Reasoning 1 (1985) 5–48.Google Scholar
  218. [218]
    Wu, W.-T., Basic principles of mechanical theorem proving in elementary geometries,Journal of Automated Reasoning 2 (1986) 221–252.Google Scholar
  219. [219]
    Freuder, E.C., Synthesizing constraint expressions,Communications of ACM 21(11) 1978.Google Scholar
  220. [220]
    Gaschnig, J., A general backtrack algorithm that eliminates most redundant tests,Proceedings of the International Conference on Artificial Intelligence, Cambridge, MA (1977) 457.Google Scholar
  221. [221]
    Gaschnig, J., Performance measurement and analysis of certain search algorithms, Ph.D. thesis, Department of Computer Science, Carnegie-Mellon University (May, 1979).Google Scholar
  222. [222]
    Haralick, R.M., Scene analysis: homomorphism and arrangements, in: Hanson and Reisman, eds.,Machine Vision, Academic Press, New York, 1978.Google Scholar
  223. [223]
    Haralick, R.M. and G.L. Elliott, Increasing tree search efficiency for constraint satisfaction problems,Artificial Intelligence 14 (1980) 263–313.Google Scholar
  224. [224]
    Haralick, R.M. and J. Kartus, Arrangements, homomorphisms and discrete relaxation,IEEE Transactions on Systems, Man and Cybernetics SMC-8 (August 1978) 600–612.Google Scholar
  225. [225]
    Haralick, R.M. and L. Shapiro, The consistent labeling problem: PartI,IEEE Transactions on Pattern Analysis and Machine Intelligence 1(2) (April 1979) 173–184.Google Scholar
  226. [226]
    Haralick, R.M. and L. Shapiro, The consistent labeling problem: Part II,IEEE Transactions on Pattern Analysis and Machine Intelligence 2(3) (1980) 193–203.Google Scholar
  227. [227]
    Haralick, R.M., L.S. Davis, A. Rosenfeld and D.L. Milgram, Reduction operations for constraint satisfaction,Information Sciences 14 (1978) 199–219.Google Scholar
  228. [228]
    Kowalski, R.A., A proof procedure using connection graphs,Journal of ACM 22 (October 1975) 572–595.Google Scholar
  229. [229]
    Mackworth, A.K., Consistency in networks of relations,Artificial Intelligence 8(1) (1977) 99–118.Google Scholar
  230. [230]
    Mackworth, A.K. and E.C. Freuder, The complexity of some polynomial network consistency algorithms for constraint satisfaction problems,Artificial Intelligence 25 (1985) 65–74.Google Scholar
  231. [231]
    McGregor, J.J., Relational consistency algorithms and their applications in finding subgraph and graph isomorphisms,Information Sciences 19 (1979) 229–250.Google Scholar
  232. [232]
    Mohr, R. and T.C. Henderson, Arc and path consistency revisisted,Artificial Intelligence 28 (1986) 225–233.Google Scholar
  233. [233]
    Montanari, U., Networks of constraints: fundamental properties and applications to picture processing,Information Sciences 7 (1976) 95–132.Google Scholar
  234. [234]
    Nudel, B., Consistent-labeling problems and their algorithms: expected-complexities and theory-based heuristics,Artificial Intelligence 21 (1983) 135–178.Google Scholar
  235. [235]
    Rosenfeld, A., R. Hummel and S. Zucker, Scene labeling by relaxation operations,IEEE Transactions on Systems, Man and Cybernetics SMC-6 (1976) 420–423.Google Scholar
  236. [236]
    Shapiro, L.G. and R.M. Haralick, Structural descriptions and inexact matching,IEEE Transactions on Pattern Analysis and Machine Intelligence 3 (1981) 504–519.Google Scholar
  237. [237]
    Ullman, J.R., An algorithm for subgraph homomorphisms,Journal of ACM 23(1) (1976) 31–42.Google Scholar
  238. [238]
    Waltz, D.L., Generating semantic descriptions from drawings of scenes with shadows, MAC AI-TR-271, M.I.T. 1972.Google Scholar
  239. [239]
    Aspvall, B., M.F. Plass and R.E. Tarjan, A linear-time algorithm for testing the truth of certain quantified Boolean formulas,Information Processing Letters 8 (1979) 121–123.Google Scholar
  240. [240]
    Balas, E. and J.N. Hooker, Applying polyhedral methods to logical inference, paper presented at the TIMS/ORSA meeting, New Orleans, LA, May 1987.Google Scholar
  241. [241]
    Blair, C.E., R.G. Jeroslow and J.K. Lowe, Some results and experiments in programming techniques for propositional logic,Computers and Operations Research 13(5) (1986) 633–645.Google Scholar
  242. [242]
    Bugrara, K. and C.A. Brown, On the average case analysis of some satisfiability model problems,Information Sciences 40 (1986) 21–37.Google Scholar
  243. [243]
    Chang, C.L. and R.C. Lee,Symbolic Logic and Mechanical Theorem Proving, Academic Press, New York, 1973.Google Scholar
  244. [244]
    Cook, S.A., The complexity of theorem-proving procedures,Proceedings 3rd ACM Symposium on Theory of Computing (May 1971) 151–158.Google Scholar
  245. [245]
    Davis, M. and H. Putnam, A computing procedure for quantification theory,Journal of ACM 7 (1960) 201–215.Google Scholar
  246. [246]
    Desrosiers, J., B. Jaumard and L. Le Bars, Algorithms for the satisfiability problem, GERAD-HEC Research Report, to appear.Google Scholar
  247. [247]
    Franco, J., Probabilistic analysis of the pure literal heuristic for the satisfiability problem,Annals of Operations Research 1 (1984) 273–289.Google Scholar
  248. [248]
    Franco, J., On the probabilistic performance of algorithms for the satisfiability problem,Information Processing Letters 23 (1986) 103–106.Google Scholar
  249. [249]
    Franco, J. and M. Paull, Probabilistic analysis of the Davis—Putman procedure for solving the satisfiability problem,Discrete Applied Mathematics 5 (1983) 77–87.Google Scholar
  250. [250]
    Gallo, G. and G.P. Urbani, New algorithms for the satisfiability problem, Nota Scientifica 5-87-6, Dipartimen to Informatica, Universitá di Pisa, March 1987.Google Scholar
  251. [251]
    Goldberg, A., P.W. Purdom and C.A. Brown, Average time analyses of simplified Davis—Putnam procedures,Information Processing Letters 15(2, 6) (September 1982) 72–75 (Errata 16 (1983) 213).Google Scholar
  252. [252]
    Hammer, P.L. and S. Rudeanu,Boolean Methods in Operations Research and Related Areas, Springer-Verlag, Heidelberg, 1968.Google Scholar
  253. [253]
    Hansen, P. and B. Jaumard, Algorithms for the maximum satisfiability problem, RUTCOR Research Report, Rutgers University, November 1987.Google Scholar
  254. [254]
    Hansen, P., B. Jaumard and M. Minoux, A linear-expected time algorithm for deriving all logical conclusions implied by a set of Boolean inequalities,Mathematical Programming 34 (1986) 223–231.Google Scholar
  255. [255]
    Jeroslow, R.G., Ten lectures on mixed integer model formulations for logic-based decision support, Technical Report, College of Management, Georgia Institute of Technology, March 1987 (to appear in theAnnals of Discrete Mathematics).Google Scholar
  256. [256]
    Johnson, D.S., Approximation algorithms for combinatorial problems,Journal of Computer and System Sciences 9 (1976) 256–278.Google Scholar
  257. [257]
    Karp, R.M., Reducibility among combinatorial problems, in: R.G. Miller and J.W. Thatcher, eds.,Complexity of Computer Computations, Plenum press, New York (1972) 85–104.Google Scholar
  258. [258]
    Lieberherr, K.J., Algorithmic extremal problems in combinatorial optimization,Journal of Algorithms 3 (1982) 225–244.Google Scholar
  259. [259]
    Lieberherr, K.J. and E. Specker, Complexity of partial satisfaction,Journal of ACM 28(2) (1981) 411–421.Google Scholar
  260. [260]
    Nilsson, N.J., Probabilistic logic,Artificial Intelligence 28 (1986) 71–88.Google Scholar
  261. [261]
    Petreschi, R. and B. Simeone, A switching algorithm for the solution of quadratic Boolean equations,Information Processing Letters 11 (1980) 193–198.Google Scholar
  262. [262]
    Purdom, P.W., Evaluating search methods analytically,Proceedings National Conference on Artificial Intelligence, Pittsburg, PA (1982) 124–127.Google Scholar
  263. [263]
    Purdom, P.W., Search rearrangement backtracking and polynomial average time,Artificial Intelligence 21 (1983) 117–133.Google Scholar
  264. [264]
    Purdom, P.W. and C. Brown, The pure literal rule and polynomial average time,SIAM Journal of Computing 14(4) (November 1985) 943–953.Google Scholar
  265. [265]
    Rudeanu, S.,Boolean Functions and Equations, North-Holland, Amsterdam, 1974.Google Scholar
  266. [266]
    Schaefer, T.J., The complexity of satisfiability problems,Proceedings 10th Annual ACM Symposium on Theory of Computing (1978) 216–226.Google Scholar
  267. [267]
    Shostak, R.E., Refutation graphs,Artificial Intelligence 7 (1976) 51–64.Google Scholar
  268. [268]
    Simeone, B., Consistency of quadratic Boolean equations and the König-Egerváry property for graphs,Annals of Discrete Mathematics 25 (1985) 281–290.Google Scholar
  269. [269]
    Tovey, C.A., A simplified NP-complete satisfiability problem,Discrete Applied Mathematics 8 (1984) 85–89.Google Scholar
  270. [270]
    Arvind, V. and S. Biswas, An O(n 2) algorithm for the satisfiability problem of a subset of propositional sentences in CNF that includes all Horn sentences,Information Processing Letters 24 (1987) 67–69.Google Scholar
  271. [271]
    Dowling, W.F. and J.H. Gallier, Linear-time algorithms for testing the satisfiability of propositional Horn formulae,Journal of Logic Programming 3 (1984) 267–284.Google Scholar
  272. [272]
    Henschen, L.J., Semantic resolution for Horn sets,IEEE Transactions on Computers 25(8) (1976).Google Scholar
  273. [273]
    Henschen, L. and L. Wos, Unit refutations and Horn sets,Journal of ACM 21(4) (1974) 590–605.Google Scholar
  274. [274]
    Jaumard, B. and B. Simeone, On the complexity of the maximum satisfiability problem for Horn formulas,Information Processing Letters 26 (1987/88) 1–4.Google Scholar
  275. [275]
    Jones, N.D. and W.T. Laaser, Complete problems for deterministic polynomial time,Theoret. Computer Science 3 (1976) 105–117.Google Scholar
  276. [276]
    Lewis, H.R., Renaming a set of clauses as a Horn set,Journal of ACM 25(1) (January 1978) 134–135.Google Scholar
  277. [277]
    Mannila, H. and K. Mehlhorn, A fast algorithm for renaming a set of clauses as a Horn set,Information Processing Letters 21 (1985) 269–272.Google Scholar
  278. [278]
    Sykora, O., An optimal algorithm for renaming a set of clauses into the Horn set,Computer and Artificial Intelligence 4(1) (1985) 37–43.Google Scholar
  279. [279]
    Tärnlund, S.A., Horn clause computability,BIT 17 (1977) 215–216.Google Scholar
  280. [280]
    Yamasaki, S. and S. Doshita, The satisfiability problem for a class consisting of Horn sentences and some non-Horn sentences in proportional (sic!) logic,Information and Control 59 (1983) 1–12.Google Scholar
  281. [281]
    Yamasaki, S., M. Yoshida, S. Doshita and M. Hirata, A new combination for input and unit deductions for Horn sentences,Information Processing Letters 18 (1984) 209–213.Google Scholar
  282. [282]
    Dilger, W. and A. Janson, Intelligent backtracking in deduction systems by means of extended unification graphs,Journal of Automated Reasoning 2 (1986) 43–62.Google Scholar
  283. [283]
    Eder, E., Properties of substitutions and unifications,Journal of Symbolic Computation 1 (1985) 31–46.Google Scholar
  284. [284]
    Fages, F. and G. Huet, Unification and matching in equational theories,CAAP 83, Lecture Notes in Computer Science 159, Springer-Verlag, Heidelberg, 1983.Google Scholar
  285. [285]
    Martelli, A. and U. Montanari, An efficient unification algorithm,ACM Transactions on Programming Languages of Systems 4(2) (1982) 258–282.Google Scholar
  286. [286]
    Morokhovets, M.K., A modified unification procedure,Cybernetics 20(20) 1 (1984) 147–152.Google Scholar
  287. [287]
    Paterson, M.S. and M.N. Wegman, Linear unification,Journal of Computers and System Sciences 16(1978) 158–167.Google Scholar
  288. [288]
    Apt, K.R. and M.H. Van Emden, Contributions to the theory of logic programming,Journal of ACM 29(3) (July 1982) 841–862.Google Scholar
  289. [289]
    Balbin, I. and K. Lecot,Logic Programming: A Classified Bibliography, Wildgrass Books, Fitzroy, Australia, 1985.Google Scholar
  290. [290]
    Clark, K.L., Negation as failure, in: H. Gallaire and J. Minker, eds.,Logic and Databases, Plenum Press, New York, 1978, 293–322.Google Scholar
  291. [291]
    Clark, K.L. and S.A. Tärnlund,Logic Programming, Academic Press, New York, 1982.Google Scholar
  292. [292]
    Clocksin, W.F. and C.S. Mellish,Programming in PROLOG, Springer-Verlag, Heidelberg, 1981.Google Scholar
  293. [293]
    Coelho, H. and L.M. Pereira, Automated reasoning in geometry theorem proving with PROLOG,Journal of Automated Reasoning 2(4) (1986) 329–390.Google Scholar
  294. [294]
    Colmerauer, A., H. Hanoui, P. Russel and R. Pasero, Un système de communication homme-machine en Francais, Groupe de Recherche en Intelligence Artificielle, Université d'Aix-Marseille, 1973.Google Scholar
  295. [295]
    Gabbay, D.M. and M.J. Sergot, Negation as inconsistency, Department of Computing Reports, Imperial College of Science and Technology, London, England, 1984.Google Scholar
  296. [296]
    Gallaire, H. and J. Minker, eds.,Logic and Databases, Plenum Press, New York, 1978.Google Scholar
  297. [297]
    Gallier, J.H. and S. Raatz, Logic programming and graph rewriting, Research Report, Department of Computer and Information Science, University of Pennsylvania, May 1985.Google Scholar
  298. [298]
    Hayes, P.J., Computation and deduction,Proceedings 2nd Symposium on Mathematical Foundation of Computer Science, Czechoslovak Academy of Sciences (1973) 105–118.Google Scholar
  299. [299]
    Hogger, C.J.,Introduction to Logic Programming, APIC Studies in Data Processing 21, Academic Press, New York, 1984.Google Scholar
  300. [300]
    Jaffar, J., J.L. Lassez and J.W. Lloyd, Completeness of the negation as failure rule,Proceedings of IJCAI 8, 1983.Google Scholar
  301. [301]
    Kowalski, R.A., Predicate logic as a programming language, in: J.L. Rosenfeld, ed.,Information Processing 74, North-Holland, Amsterdam, 1974, 569–574.Google Scholar
  302. [302]
    Kowalski, R.A., Algorithm = Logic + Control,Communications of ACM 22(7) (July 1979) 424–436.Google Scholar
  303. [303]
    Kowalski, R.A.,Logic for Problem Solving, Elsevier, New York, 1979.Google Scholar
  304. [304]
    Kowalski, R.A. and D. Kuehner, Linear resolution with selection function,Artificial Intelligence 2 (1970) 227–260.Google Scholar
  305. [305]
    Lloyd, J.W.,Foundations of Logic Programming, Symbolic Computation, Springer-Verlag, Berlin, 1984.Google Scholar
  306. [306]
    Pereira, L.M., Logic control with logic,Proceedings 1st International Logic Programming Conference, Luminy, France (September, 1982).Google Scholar
  307. [307]
    Robinson, J.A.,Logic: Form and Function, Edinburgh University Press, Edinburgh, 1979.Google Scholar
  308. [308]
    Van Emden, M.H. and R.A. Kowalski, The semantics of predicate logic as a programming language,Journal of ACM 23(4) (1976) 733–742.Google Scholar
  309. [309]
    Warren, D.H.D., L.M. Pereira and F. Pereira, PROLOG — the language and its implementation compared with LISP,Proceedings of the Symposium on Artificial Intelligence and Programming Languages (ACM); SIGPLAN Notices 12(8); andSIGART Newsletter 64 (1977) 109–115.Google Scholar
  310. [310]
    Warren, D.H.D. and M. Van Caneghem,Logic Programming and its Applications, Albex, Norwood, NJ, 1985.Google Scholar
  311. [311]
    Aho, A.V., C. Beeri and J.D. Ullman, The theory of joins in relational databases,ACM Transactions on Database Systems (4,3) (September, 1979) 297–314.Google Scholar
  312. [312]
    Aho, A.V., Y. Sagiv and J.D. Ullman, Equivalence among relational expressions,SIAM Journal on Computing 8(2) (1979) 218–246.Google Scholar
  313. [313]
    Asirelli, P., M. De Santis and A. Martelli, Integrity constraints in logic databases,Journal of Logic Programming 3 (1985) 221–232.Google Scholar
  314. [314]
    Ausiello, G., A. D'Atri and D. Sacca, Graph algorithms for functional dependency manipulation,Journal of ACM 30(4) (1983) 752–766.Google Scholar
  315. [315]
    Chandra, A.K. and D. Harel, Horn clause queries and generalizations,Journal of Logic Programming 1 (1985) 1–15.Google Scholar
  316. [316]
    Chang, C.L., Deduce 2: Further investigations of deductions in relational databases, in: H. Gallaire and J. Minker, eds.,Logic and Databases, Plenum Press, New York, 1978, 201–236.Google Scholar
  317. [317]
    Colmerauer, A. and J.F. Pique, About natural logic, in: H. Gallaire, J. Minker and J.M. Nicolas, eds.,Advances in Database Theory, Vol. 1, Plenum Press, New York, 1981, 343–345.Google Scholar
  318. [318]
    Delobel, C. and R.G. Casey, Decomposition of a database and the theory of Boolean switching functions,IBM Journal of Research and Development 17(5) (September 1973) 484–485.Google Scholar
  319. [319]
    Demolombe, R., Estimation of the number of triples satisfying a query expressed in predicate calculus language,Proceedings 6th International Conference on Very Large Data Bases, IEEE, Montreal (October 1980) 55–63.Google Scholar
  320. [320]
    Fagin, R., Horn clauses and database dependencies,Journal of ACM 29(4) (1982) 952–985.Google Scholar
  321. [321]
    Gallaire, H., Impacts of logic on data bases,Proceedings 7th International Conference on Very Large Data Bases (Cannes, France, September 9–11), IEEE, New York (1981) 248–259.Google Scholar
  322. [322]
    Gallaire, H. and J. Minker,Logic and Databases, Plenum Press, New York, 1978.Google Scholar
  323. [323]
    Gallaire, H., J. Minker and J.M. Nicolas, Logic and databases: a deductive approach,Computing Surveys 16(2) (1984) 153–185.Google Scholar
  324. [324]
    Gardarin, G. and M. Melkanoff, Proving consistency of database transactions,Proceedings 5th International Conference on Very Large Data Bases (Rio de Janeiro, October 3–5), IEEE, New York (1979) 291–298.Google Scholar
  325. [325]
    Gray, P.,Logic, Algebra and Databases, Ellis Horwood Series on Computers and their Applications, Wiley, Chichester, 1984.Google Scholar
  326. [326]
    Henschen, L.J., W.W. McCune and S.A. Naqvi, Compiling constraint checking programs from first order formulas, in: H. Gallaire, J. Minker and J.M. Nicolas, eds.,Advances in Database Theory, Vol. 2, Plenum Press, New York, 1984, 145–169.Google Scholar
  327. [327]
    Jacobs, B.E., On database logic,Journal of ACM 29(2) (April 1982) 310–332.Google Scholar
  328. [328]
    Kellog, C., P. Klahr and L. Travis, Deductive planning and path finding for relational databases, in: H. Gallaire and J. Minker, eds.,Logic and Databases, Plenum Press, New York, 1978, 179–200.Google Scholar
  329. [329]
    Maier, D., A.O. Mendelzon and Y. Sagiv, Testing implications of data dependencies,ACM Transactions on Database Systems 4(4) (1979) 455–469.Google Scholar
  330. [330]
    Maier, D., Y. Sagiv and M. Yannakakis, On the complexity of testing implications of functional and join dependencies,Journal of ACM 28(4) (1981) 680–695.Google Scholar
  331. [331]
    Minker, J., An experimental relational data base system based on logic, in: H. Gallaire and J. Minker, eds.,Logic and Databases, Plenum Press, New York, 1978, 107–147.Google Scholar
  332. [332]
    Naqvi, S.A. and L.J. Henschen, On compiling queries in recursive first order databases,Journal of ACM 31(1) (January 1984) 47–85.Google Scholar
  333. [333]
    Reiter, R., Equality and domain closure in first order databases,Journal of ACM 27(2) (April 1980) 235–249.Google Scholar
  334. [334]
    Sacca, D., Closures of databases hypergraphs,Journal of ACM 32(4) (1985) 774–803.Google Scholar
  335. [335]
    Sagiv, Y., Quadratic algorithms for minimizing joins in restricted relational expressions,SIAM Journal on Computing 12(2) (1983) 316–328.Google Scholar
  336. [336]
    Sagiv, Y., An algorithm for inferring multivalued dependencies with an application to propositional logic,Journal of ACM 27(2) (1980) 250–262.Google Scholar
  337. [337]
    Sagiv, Y., C. Delobel, D.S. Parker and R. Fagin, An equivalence between relational database dependencies and a subclass of propositional logic,Journal of ACM 28(3) (1981) 435–453.Google Scholar
  338. [338]
    Ullman, J.D.,Principles of Database Systems, 2nd ed., Computer Science Press, Potomac, MD, 1982.Google Scholar
  339. [339]
    Vardi, M.Y., The decision problem for database dependencies,Information Processing Letters 12(5) (1981) 251–254.Google Scholar
  340. [340]
    Warren, D.H.D., Efficient processing of interactive relational database queries expressed in logic,Proceedings 7th International Conference on Very Large Data Bases, Cannes, France (1981) 272–281.Google Scholar
  341. [341]
    Fikes, R.E. and N.J. Nilsson, STRIPS: a new approach to the application of theorem proving to problem solving,Artificial Intelligence 2 (1971) 189–208.Google Scholar
  342. [342]
    Fikes, R.E., P.E. Hart and N.J. Nilsson, Learning and executing generalized robot plans,Artificial Intelligence 3 (1972) 251–288.Google Scholar
  343. [343]
    Hax, A.C. and J.J. Golovin, Hierarchical production planning systems, in: A.C. Hax, ed.,Studies in Operations Management, North-Holland, Amsterdam, 1978.Google Scholar
  344. [344]
    Hayes-Roth, B. and F. Hayes-Roth, A cognitive model of planning,Cognitive Science 3 (1979) 275–310.Google Scholar
  345. [345]
    Hayes-Roth, B., A blackboard architecture for control,Artificial Intelligence 26 (1985) 251–321.Google Scholar
  346. [346]
    McDermott, D.V., Planning and action,Cognitive Science 2(2) (1978) 71–109.Google Scholar
  347. [347]
    Sacerdoti, E.D., Planning in a hierarchy of abstraction spaces,Proceedings of IJCAI 3 (1973) 412–422.Google Scholar
  348. [348]
    Sacerdoti, E.D., The nonlinear nature of plans,Proceedings of IJCAI 4 (1975) 206–294.Google Scholar
  349. [349]
    Sacerdoti, E.D.,A Structure for Plans and Behavior, Elsevier, New York, 1977.Google Scholar
  350. [350]
    Stefik, M., Planning with constraints (MOLGEN: part 1),Artificial Intelligence 16 (1981) 111–140.Google Scholar
  351. [351]
    Stefik, M., Planning with constraints (MOLGEN: part 2),Artificial Intelligence 16 (1981) 141–170.Google Scholar
  352. [352]
    Wilensky, R.,Planning and Understanding: A Computational Approach to Human Reasoning, Addison-Wesley, MA, 1983.Google Scholar
  353. [353]
    Allen, J.F. and J.A. Koomen, Planning using a temporal world model,Proceedings of IJCAI 8 (1983) 742–747.Google Scholar
  354. [354]
    McDermott, D.V., A temporal logic for reasoning about processes and plans,Cognitive Science 6 (1982) 101–155.Google Scholar
  355. [355]
    Wilkins, D., Domain-independent planning: representation and plan generation,Artificial Intelligence 22 (1984) 269–301.Google Scholar
  356. [356]
    Corkill, D.D. and V.R. Lesser, The use of meta-level control for coordination in a distributed problem solving,Proceedings of IJCAI 8 (1983) 748–756.Google Scholar
  357. [357]
    Davis, R. and R.G. Smith, Negotiation as a metaphor for distributed problem-solving,Artificial Intelligence 20 (1983) 63–109.Google Scholar
  358. [358]
    Kornfeld, W.A., Combinatorially implosive algorithms,Communications of ACM 25(10) (October 1982) 734–738.Google Scholar
  359. [359]
    Lesser, V.R. and D.D. Corkill, Functionally-accurate, cooperative distributed systems,IEEE Transactions on Systems, Man and Cybernetics SMC-11(1) (1981) 81–96.Google Scholar
  360. [360]
    Carbonell, J.G., R.S. Michalski and T.M. Mitchell, An overview of machine learning, in: R.S. Michalski, J.G. Carbonell and T.M. Mitchell, eds.,Machine Learning, Tioga Press, Palo Alto, 1983, 3–23.Google Scholar
  361. [361]
    Farquhar, P.H., Application of utility theory in artificial intelligence research, in: Y. Sawaragi, ed.,Multiple Criteria Decision-Making, Springer-Verlag, New York, to appear.Google Scholar
  362. [362]
    Forsyth, R. and R. Dada,Machine Learning Applications in Expert Systems and Information Retrieval, Wiley, New York, 1986.Google Scholar
  363. [363]
    Langley, P., Learning to search: from weak methods to domain specific heuristics,Cognitive Science 9(2) (1985) 217–261.Google Scholar
  364. [364]
    Michalski, R.S., J.G. Carbonell and T.M. Mitchell, eds.,Machine Learning, Vol. 1, Tioga, Palo Alto, CA, 1983.Google Scholar
  365. [365]
    Michalski, R.S., J.G. Carbonell and T.M. Mitchell, eds.,Machine Learning, Vol. 2, Morgan Kaufmann, Los Altos, CA, 1986.Google Scholar
  366. [366]
    Mitchell, T.M., J.G. Carbonell and R.S. Michalski,Machine Learning: A Guide to Current Research, Academic Press, New York, 1986.Google Scholar
  367. [367]
    Breiman, L., J. Friedman, R. Olshun and C. Stone,Classification and Regression Trees, Wadsworth, Belmont, CA, 1984.Google Scholar
  368. [368]
    Fu, K.S., ed.,Applications of Pattern Recognition, CRC Press, Boca Ratón, FL, 1982.Google Scholar
  369. [369]
    Nandhakumar, N. and J.K. Aggarwal, The artificial intelligence approach to pattern recognition — a perspective and an overview,Pattern Recognition 18(6) (1985) 383–389.Google Scholar
  370. [370]
    Quinlan, J.R., Inductive inference as a tool for the construction of high performance program, in: R. Michalski, J. Carbonell and T.M. Mitchell, eds.,Machine Learning: An Artificial Intelligence Approach, Tioga Press, Palo Alto, CA, 1984.Google Scholar
  371. [371]
    Quinlan, J.R., Induction of decision trees,Machine Learning 1(1) (1986) 81–106.Google Scholar
  372. [372]
    Holland, J.H., K.J. Holyoak, R.E. Nisbett and P.R. Thagard,Induction: Processes of Inference, Learning and Discovery, M.I.T. Press, Bradford, MA, 1986.Google Scholar
  373. [373]
    Johnson, M.K. and L. Hasher, Human learning and memory, in: M.R. Rosenzweig and L.W. Porter, eds.,Annual Review of Psychology, Stanford, CA, 1987, 631–638.Google Scholar
  374. [374]
    Laird, J.E., P.S. Rosenbloom and A. Newell, Chunking in SOAR: the anatomy of a general learning mechanism,Machine Learning 1(1) (1986) 11–46.Google Scholar
  375. [375]
    ⋆ Lenat, D., EURISKO: A program that learns new heuristics and domain concepts. The nature of heuristics III: program design and results,Artificial Intelligence 21(1–2) (1983) 61–98.Google Scholar
  376. [376]
    Mitchell, T.M., Generalization as search,Artificial Intelligence 18(2) (1982) 203–226.Google Scholar
  377. [377]
    Ackley, D.H. and G.E. Hinton, A learning algorithm for Boltzmann machines,Cognitive Science 9 (1985) 147–169.Google Scholar
  378. [378]
    Feldman, J.A. and D.H. Ballard, Connectionist models and their properties,Cognitive Science 6 (1982) 205–254.Google Scholar
  379. [379]
    McClelland, J.L., D.E. Rumelhart and PDP Research Group, eds.,Parallel Distributed Processing: Explorations in the Microstructure of Cognition, M.I.T. Press, Cambridge, MA, 1985.Google Scholar
  380. [380]
    Grefenstette, J., ed.,Proceedings of the International Conference on Genetic Algorithms and Their Applications, Pittsburgh, PA, 1985.Google Scholar
  381. [381]
    Holland, J.H.,Adaptation in Natural and Artificial Systems, University of Michigan Press, Michigan, 1975.Google Scholar
  382. [382]
    Smith, S.F., Adaptive learning systems, in: R. Forsyth, ed.,Expert Systems, Principles and Case Studies, Chapman and Hall, Ltd., New York, 1984.Google Scholar
  383. [383]
    Buchanan, B.G., Research on expert systems,Machine Intelligence, Wiley, New York, 1982, 269–300.Google Scholar
  384. [384]
    Duda, R.O. and E.H. Shortliffe, Expert systems research,Science 220 (1983) 262–268.Google Scholar
  385. [385]
    Hayes-Roth, F., D.A. Waterman and D. Lenat, eds.,Building Expert Systems, Addison-Wesley, Reading, MA, 1983.Google Scholar
  386. [386]
    Klahr, P., and D.A. Waterman, eds.,Expert Systems, Addison-Wesley, Reading, MA, 1986.Google Scholar
  387. [387]
    Michie, D., ed.,Expert Systems in the Micro-Electronic Age, Edinburgh University Press, Edinburgh, 1980.Google Scholar
  388. [388]
    Reitman, W., ed.,Artificial Intelligence Applications for Business, Ablex, Norwood, NJ, 1984.Google Scholar
  389. [389]
    Stefik, M., J. Aikins, R. Balzer, J. Benoit, L. Birnbaum, F. Hayes-Roth and E.H. Sacerdoti, The organization of expert systems: a tutorial,Artificial Intelligence 18 (1982) 135–173.Google Scholar
  390. [390]
    Waterman, D.A., ed.,A Guide to Expert Systems, Addison-Wesley, Reading, MA, 1986.Google Scholar
  391. [391]
    Weiss, S.M. and C.A. Kulikowski,A Practical Guide to Designing Expert Systems, Rowman and Allanheld, Totowa, NJ, 1984.Google Scholar
  392. [392]
    Winston, P.H. and K.A. Prendergast,The AI Business, M.I.T. Press, Bradford, MA, 1984.Google Scholar
  393. [393]
    Fischer, E.L., An AI-Based methodology for factory design,AI Magazine (Fall 1986) 72–85.Google Scholar
  394. [394]
    Fox, B.R. and K.G. Kempf, Opportunistic scheduling for robotic assembly,Proceedings IEEE International Conference on Robotics and Automation, 1985, 880–889.Google Scholar
  395. [395]
    Fox, M.S. and S.F. Smith, ISIS: a knowledge-based system for factory scheduling,Expert Systems 1 (1984) 25–49.Google Scholar
  396. [396]
    Gilmore, J.F. and A.C. Semeco, Terrain navigation through knowledge-based route planning,Proceedings of IJCAI 9 (1985) 1086–1088.Google Scholar
  397. [397]
    Goldstein, I.P. and R.B. Roberts, NUDGE — a knowledge-based scheduling program,Proceedings of IJCAI 5 (1977) 257–263.Google Scholar
  398. [398]
    Kastner, J., C. Apte, J. Griesmier, S.J. Hong, M. Karnaugh, E. Mays and Y. Tozawa, A knowledge-based consultant for financial marketing,AI Magazine 7(5) (Winter 1986) 71–79.Google Scholar
  399. [399]
    Lynch, F., C. Marshall, D.E. O'Connor and M. Kiskiel, AI in manufacturing at Digital,AI Magazine 7(5) (Winter 1986) 53–57.Google Scholar
  400. [400]
    ⋆ McDermott, D., A temporal logic for reasoning about processes and plans,Cognitive Science 6 (1982) 101–155.Google Scholar
  401. [401]
    Miller, D., R.J. Firby and T. Dean, Deadlines, travel time and robot problem solving,Proceedings of IJCAI 9 (1985) 1052–1054.Google Scholar
  402. [402]
    Murphy, F.H. and E.A. Stohr, An intelligent system for formulating linear programs,Decision Support Systems 2(1) (March 1986) 39–47.Google Scholar
  403. [403]
    O'Connor, D.E., Using expert systems to manage change and complexity in manufacturing, in: W. Reitman, ed.,Artificial Intelligence Applications for Business, Ablex, Norwood, NJ, 1984.Google Scholar
  404. [404]
    Orciuch, E. and J. Frost, ISA: an intelligent scheduling assistant,IEEE Computer Society, Proceedings 1st Conference on AI Applications, 1984.Google Scholar
  405. [405]
    Ow, P.S. and S.F. Smith, Viewing scheduling as an opportunistic problem-solving process,this volume.Google Scholar
  406. [406]
    ⋆ Paul. R.J. and G.I. Doukidis, Further developments in the use of artificial intelligence techniques which formulate simulation problems,Journal of the Operational Research Society 37(8) (1986) 787–810.Google Scholar
  407. [407]
    Sathi, A., T.E. Morton and S.F. Roth, Callisto: an intelligent project management system,AI Magazine 7(5) (Winter 1986) 34–52.Google Scholar
  408. [408]
    Scarl, E., Applications of a non-rule knowledge-based system to NASA scheduling and monitoring problems,IECEC Proceedings San Francisco (August 1984).Google Scholar
  409. [409]
    Smith, S.F., M.S. Fox and P.S. Ow, Constructing and maintaining detailed production plans: investigations into the development of knowledge-based factory scheduling systems,AI Magazine (Fall 1986) 45–61.Google Scholar
  410. [410]
    Sugie, M., D. Menzilcioglu and H.T. Kung, CAR guide — onboard computer for automobile route guidance,Proceedings National Computer Conference, 1984, 695–706.Google Scholar
  411. [411]
    Tonge, F.M., Summary of a heuristic line balancing procedure, in: E.A. Feigenbaum and J. Feldman, eds.,Computers and Thought, McGraw-Hill, New York, 1983.Google Scholar
  412. [412]
    Buchanan, B.G. and E.A. Feigenbaum, DENDRAL and META-DENDRAL,Artificial Intelligence 11 (1978) 5–24.Google Scholar
  413. [413]
    Engelmore, R. and A. Terry, Structure and function of the CRYSALIS system,Proceedings of IJCAI 6 (1979) 250–256.Google Scholar
  414. [414]
    Erman, L.D., F. Hayes-Roth, V.R. Lesser and D.R. Reddy, The HEARSAY-II speech understanding system: integrating knowledge to resolve uncertainty,Computing Surveys 12(2) (June 1980) 213–253.Google Scholar
  415. [415]
    McCracken, D., Representation and efficiency in a production system for speech understanding,Proceedings of IJCAI 6 (1979) 556–561.Google Scholar
  416. [416]
    Campbell, A., V. Hollister, R.O. Duda and P.E. Hart, Recognition of a hidden mineral deposit by an artificial intelligence program,Science 217 (1982) 927–929.Google Scholar
  417. [417]
    Davis, R., H. Austin, I. Carlbom, B. Frawley, P. Pruchnik, R. Sneiderman and J. Gilreath, The DIPMETER ADVISOR system,Proceedings of IJCAI 8 (1983) 122–129.Google Scholar
  418. [418]
    Descotte, Y. and J.C. Latombe, GARI: a problem solver that plans how to machine mechanical parts,Proceedings of IJCAI 7 (1981) 776–772.Google Scholar
  419. [419]
    Gallanti, M., G. Guida, L. Spampinato and A. Stefanini, Representing procedural knowledge in expert systems: an application to process control,Proceedings of IJCAI 9 (1985) 345–352.Google Scholar
  420. [420]
    Geneseretti, M.R., The use of design descriptions in automated diagnosis,Artificial Intelligence 24 (1984) 411–436.Google Scholar
  421. [421]
    Konolige, K., An inference net compiler for the PROSPECTOR rule-based consultation system,Proceedings of IJCAI 6 (1979) 487–489.Google Scholar
  422. [422]
    Weiss, S., C. Kulikowski, C. Apte, M. Uschold, J. Patchette, R. Brigham and B. Spitzer, Building expert systems for controlling complex programs,Proceedings of NCAI 2, Pittsburgh, PA, 1982, 322–326.Google Scholar
  423. [423]
    Buchanan, B.G. and E.H. Shortliffe, (eds.),Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project, Addison-Wesley, Reading, MA, 1985.Google Scholar
  424. [424]
    Clancey, W.J. and R. Letsinger, NEOMYCIN: reconfiguring a rule-based expert system for application to teaching,Proceedings of IJCAI 7 (1981) 829–836.Google Scholar
  425. [425]
    Frienland, P., Knowledge-based experiment design in molecular genetics,Proceedings of IJCAI 6 (1979) 285–288.Google Scholar
  426. [426]
    Shortliffe, E.H., A.C. Scott, M.B. Bischoff, A.B. Campbell, W. Van Mellerd and C.D. Jacobs, ONCOCIN: an expert system for oncology protocol management,Proceedings of IJCAI 7 (1981) 876–881.Google Scholar
  427. [427]
    Balzer, R., L. Erman, P. London and C. Williams, HEARSAY-III: a domain-independent framework for expert systems,Proceedings of NCAI 1 (August 1980) 108–110.Google Scholar
  428. [428]
    Feigenbaum, E.A., B.G. Buchanan and J. Lederberg, On generality and problem solving: a case study using the DENDRAL program, in: B. Meltzer and D. Michie, eds.,Machine Intelligence 6, Elsevier, New York, 1971.Google Scholar
  429. [429]
    Neches, R., W.R. Swartout and J. Moore, Explainable (and maintainable) expert systems,Proceedings of IJCAI 9 (1985) 382–389.Google Scholar
  430. [430]
    Nii, H.P., J.J. Anton, E.A. Feigenbaum and A.J. Rockmore, Signal-to-symbol transformation: HASP/SIAP case study,AI Magazine (1982) 23–35.Google Scholar
  431. [431]
    Ow, P.S. and S.F. Smith, Two design principles for knowledge-based systems,Decision Sciences (July 1987).Google Scholar
  432. [432]
    Quinlan, J., Knowledge-based system for locating missing high cards in bridge,Proceedings of IJCAI 6 (1979) 705–707.Google Scholar
  433. [433]
    Swartout, W.R., Explaining and justifying expert consulting programs,Proceedings of IJCAI 7 (1981) 815–823.Google Scholar
  434. [434]
    Bachant, J. and J. McDermott, R1 revisited: four years in the trenches,AI Magazine (Fall 1984) 21–32.Google Scholar
  435. [435]
    McDermott, J., R1's formative years,AI Magazine 2(2) (Summer 1981) 21–29.Google Scholar
  436. [436]
    McDermott, J., R1: a rule-based configurer of computer systems,Artificial Intelligence 19(1) (September 1982) 39–88.Google Scholar
  437. [437]
    McDermott, J., Domain knowledge and the design process,Design Studies 3(1) (January 1982).Google Scholar
  438. [438]
    Polit, S., R1 and beyond: technology transfer at DEC,AI Magazine (Winter 1985) 76–78.Google Scholar
  439. [439]
    Rosenbloom, P.S., J.E. Laird, J. McDermott, A. Newell and E. Orciuch, R1-SOAR: an experiment in knowledge-intensive programming in a problem-solving architecture,IEEE Transactions on Pattern Analysis and Machine Intelligence 7(5) (1985) 561–569.Google Scholar
  440. [440]
    Scown, S.J.,The Artificial Intelligence Experience: An Introduction, Digital Equipment Corporation, 1985.Google Scholar
  441. [441]
    Van de Brug, A., J. Bachant and J. McDermott, The taming of R1,IEEE Expert (Fall 1986) 33–39.Google Scholar
  442. [442]
    Bonissone, P.P., Plausible reasoning: coping with uncertainty in expert systems, in: S.C. Shapiro, ed.,Encyclopedia of Artificial Intelligence, Wiley, New York, 1986.Google Scholar
  443. [443]
    Cheeseman, P., In defense of probability,Proceedings of 9th International Joint Conference on AI, Los Angeles, CA, 1985.Google Scholar
  444. [444]
    Henrion, M., Uncertainty in artificial intelligence: is probability epistemologically and heuristically adequate?Expert Systems and Expert Judgement, Proceedings of the NATO Advanced Research Workshop, Porto, Portugal, August 1986.Google Scholar
  445. [445]
    Kanal, L.N. and J. Lemmer, eds.,Uncertainty in AI, North-Holland, Amsterdam, 1986.Google Scholar
  446. [446]
    Spiegelhalter, D.J., A statistical view of uncertainty in expert systems, in: W. Gale, ed.,AI and Statistics, Addison-Wesley, Reading, MA, 1986, 17–55.Google Scholar
  447. [447]
    Duda, R.O., P.E. Hart and N.J. Nilsson, Subjective Bayesian methods for rule-based inference systems,AFIPS Conference Proceedings, New York, June 1976, 1075–1082.Google Scholar
  448. [448]
    Jeroslow, R.G., On monotone chaining procedures of the CF type, Technical Report, College of Management, Georgia Institute of Technology, 1985.Google Scholar
  449. [449]
    Negoita, C.V.,Expert Systems and Fuzzy Systems, Benjamin/Cummings Publishing Co., Menlo Park, CA, 1985.Google Scholar
  450. [450]
    Pearl, J., Fusion, propagation, and structuring belief networks,Artificial Intelligence 29(3) (September 1986) 241–288.Google Scholar
  451. [451]
    Shafer, G.,A Mathematical Theory of Evidence, Princeton University Press. Princeton, 1976.Google Scholar
  452. [452]
    Shachter, R.D., Evaluating influence diagrams,Operations Research (to appear in 1987).Google Scholar
  453. [453]
    Shortliffe, E.H. and B.G. Buchanan, A model of inexact reasoning in medicine, in: B.G. Buchanan and E.H. Shortliffe, eds.,Rule-Based Expert Systems, Addison-Wesley, Reading, MA, 1984, 233–262.Google Scholar
  454. [454]
    Zadeh, L.A., Making computers think like people,IEEE Spectrum (August 1984) 26–32.Google Scholar
  455. [455]
    Tong, R.M. and D.G. Shapiro, Experimental investigations of uncertainty in a rule-based system for information retrieval,International Journal of Man-Machine Studies 17 (March 1985) 265–282.Google Scholar
  456. [456]
    Wise, B., An experimental comparison of uncertain inference systems, Ph.D. thesis, Department of Engineering and Public Policy, Carnegie-Mellon University, 1986.Google Scholar
  457. [457]
    Yadrick, R.M., D.S. Vaughan, B.M. Perrin, P.D. Kempf and K.G. Kempf, Evaluation of uncertain interference models I: PROSPECTOR,2nd Workshop on Uncertainty AAAI, 1986.Google Scholar

Copyright information

© J.C. Baltzer AG, Scientific Publishing Company 1988

Authors and Affiliations

  • Brigitte Jaumard
    • 1
  • Peng Si OW
    • 2
  • Bruno Simeone
    • 3
    • 4
  1. 1.Hill Center for the Mathematical SciencesCAIP, Rutgers UniversityNew BrunswickUSA
  2. 2.Graduate School of Industrial AdministrationCarnegie-Mellon UniversityPittsburghUSA
  3. 3.Hill Center for the Mathematical SciencesRutcor, Rutgers UniversityNew BrunswickUSA
  4. 4.Department of StatisticsUniversity of Rome “La Sapienza”Italy

Personalised recommendations