Skip to main content

Advertisement

Springer Nature Link
Log in

On the comparison of AI and DAI based planning techniques for automated manufacturing systems

  • Published:
Journal of Intelligent and Robotic Systems Aims and scope Submit manuscript

Abstract

AI and DAI based planning techniques, suitable for automated manufacturing systems are surveyed and compared. The relation of learning to planning is described and it is explained how learning may be used to improve planning. Several examples are presented. A complete reference list is provided.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Acar, L. and Ozguner, U.: Design of knowledge-rich hierarchical controllers for large functional systems,IEEE Trans. Systems, Man and Cybernetics 20(4) (1990), 791–803.

    Google Scholar 

  2. Albus, J. S.: Outline for a theory of intelligence,IEEE Trans. Systems, Man and Cybernetics 21(3) (1991).

  3. Albus, J. S.: A reference model architecture for intelligent systems design, inAn Introduction to Intelligent and Autonomous Control, Kluwer Academic Publishers, 1993.

  4. Albus, J. S., McLean, C., Barbera, A. J. and Fitzerald, M. L.: An architecture for real-time sensory-interactive control of robots in a manufacturing environment, in4th AC/FIP Symposium on Information Control Problems in Manufacturing Technology, Gaithesburg, MD, 1982.

  5. Albus, J. S. et al.: Theory and practice of intelligent control, inProc. 23rd IEEE COMPCON, 1981, pp. 19–39.

  6. Allen, J. F.: An internal based representation of temporal knowledge, inProc. 7th Internat. Joint Conference in Artificial Intelligence, 1981, pp. 221–226.

  7. Allen, J. F.: Towards a general theory of action and time,Artificial Intelligence 23(2) (1984), 123–154.

    Google Scholar 

  8. Allen, J. F.: Recognizing intentions from natural language utterances, in M. Brady and R. C. Berwick (eds),Computational Models of Discourse, MIT Press, Cambridge, MA, 1993.

    Google Scholar 

  9. Antsaklis, P. J., Passino, K. M. and Wang, S. J.: Towards intelligent autonomous control systems: Architecture and fundamental issues,J. Intelligent and Robotic Systems 1(4) (1989), 315–342.

    Google Scholar 

  10. Antsaklis, P. J., Passino, K. M. and Wang, S. J.: An introduction to autonomous control systems,IEEE Control Systems Magazine 11(4) (1991), 5–13.

    Google Scholar 

  11. Antsaklis, P. J. and Passino, K. M.: Introduction to intelligent control systems with high degrees of autonomy, inAn Introduction to Intelligent and Autonomous Control, Kluwer Academic Publishers, 1993.

  12. Appelt, D. E.: A planner for reasoning about knowledge and action, inProc. 1st Annual Conference of the American Association for Artificial Intelligence, Stanford, CA, 1980, pp. 131–133.

    Google Scholar 

  13. Appelt, D. E.:Planning Natural Language Utterances to Satisfy Multiple Goals, PhD Thesis, Stanford University, 1981.

  14. Gevarter, W. B.:Artificial Intelligence, Noyes, NJ, 1992.

    Google Scholar 

  15. Barber, S., Harbison-Briggs, K., Mitchell, R. and Tiernan, J. C. M.: Symbolic representation and planning for robot control systems in manufacturing, in A. Famili, D. S. Nau and S. H. Kim (eds),AI Applications in Manufacturing, AAAI Press, 1992.

  16. Barbera, A. J., Albus, M. L. and Fitzerald, M. L.: Hierarchical control of robots using microcomputers, inProc. 9th International Symposium on Industrial Robots, Washington, DC, 1979.

  17. Bertolotti, E.: Interactive problem solving for production planning, in A. Famili, D. S. Nau and S. H. Kim (eds),AI Applications in Manufacturing, AAAI Press, 1992.

  18. Bose, P.: An abstraction-based search and learning approach for effective scheduling, in A. Famili, D. S. Nau and S. H. Kim (eds),AI Applications in Manufacturing, AAAI Press, 1992.

  19. Bourjault, A.:Contribution a une approche methodologique de l'assemblage automatise, Elaboration automatique des sequences operatoires, These d'etat, Universite de France-Comte, Besancon, France, 1984.

    Google Scholar 

  20. Bratko, I.: AI tools and techniques for manufacturing systems,J. Robotics and Computer Integrated Manufacturing 4(1/2) (1988), 27–31.

    Google Scholar 

  21. Butler, J. and Otsubo, H.: ADDYMS: Architecture for distributed dynamic manufacturing scheduling, in A. Famili, D. S. Nau and S. H. Kim (eds),AI Applications in Manufacturing, AAAI Press, 1992.

  22. Cammarata, D., McArthur, D. and Steeb, R.: Strategies of cooperation in distributed problem solving, inProc. 8th International Joint Conf. Artificial Intelligence, 1983, pp. 767–770.

  23. Campbell, R. H. and Habermann, A. N.: The specification of process synchronization by path expressions, inLecture Notes in Computer Science 16, Springer-Verlag, 1974.

  24. Carbonell, J. G.: Introduction paradigms for machine learning,Artificial Intelligence 40(1–3) (1989), 1–9.

    Google Scholar 

  25. Carbonell, J. G. and Langley, P.: Machine learning, inEncyclopedia of Artificial Intelligence, Vol. 1, John Wiley, 1987, pp. 464–488.

  26. Coad, P. and Yourdon, E.:Object-Oriented Analysis, Yourdon Press, Englewood Cliffs, NJ, 1991.

    Google Scholar 

  27. Cohen, P. R. and Feigenbaum, E. A. (eds):The Handbook of Artificial Intelligence, Vol. 3, Pitman, 1982.

  28. IEEE Computer. Special issue on autonomous intelligent machines,IEEE Computer 22(6) (1989).

  29. Conry, S. E., Meyer, R. A. and Lesser, V. R.: Multistage negotiations in distributed planning, in A. H. Bond and L. Gasser (eds),Readings in Distributed Artificial Intelligence, Morgan Kaufmann, San Mateo, CA, 1988.

    Google Scholar 

  30. Corkill, D. D.: Hierarchical planning in a distributed environment, inProc. 6th International Joint Conference in Artificial Intelligence, 1979, pp. 168–175.

  31. Corkill, D. D.:A Framework for Organizational Self-Design in Distributed Problem Solving Networks, PhD Thesis, University of Massachusetts, Amherst, MA, 1983.

    Google Scholar 

  32. Corkill, D. D. and Lesser, V. R.: A goal-directed hearsay II architecture, unifying data-directed and goal-directed control, inProc. National Conference on Artificial Intelligence, Pittsburg, PA, 1982.

  33. Davis, R.:A Model for Planning in a Multi-Agent Environment, Steps Towards Principles of Teamwork, AI Working Paper, MIT, Cambridge, MA, 1981.

    Google Scholar 

  34. Davis, R. and Smith, R. G.:Negotiation as a Metaphor for Distributed Problem Solving, Artificial Intelligence Laboratory Memo No. 624, MIT, Cambridge, MA, 1981.

    Google Scholar 

  35. Davis, R. and Smith, R. G.: Negotiation as a metaphor for distributed problem solving,Artificial Intelligence 20 (1983), 63–109.

    Google Scholar 

  36. De Fazio, T. L. and Whitney, D. E.: Simplified Generation of all mechanical assembly sequences,IEEE J. Robotics and Automation RA-3(6) (1987), 640–658.

    Google Scholar 

  37. De Fazio, T. L. and Whitney, D. E.: Corrections on simplified generation of all mechanical assembly sequences,IEEE J. Robotics and Automation RA-4(6) (1988), 705–708.

    Google Scholar 

  38. Dijkstra, E. W.: Cooperating sequential processes, in F. Genuys (ed.),Programming Languages, Academic Press, New York, 1968.

    Google Scholar 

  39. Dougherty, E. R. and Giardina, C. R.:Mathematical Methods for Artificial Intelligence and Autonomous Systems, Prentice-Hall, 1988.

  40. Drummond, M. E.: A representation of action and belief for automatic planning systems, in M. P. Georgeff and A. L. Lansky (eds),Reasoning About Actions and Plans, Proc. 1986 Workshop at Timberline, Oregon, Morgan Kaufmann, Los Altos, CA, 1987, pp. 189–211.

    Google Scholar 

  41. Durfee, E. H.:Coordination of Distributed Problem Solvers, Kluwer Academic, Boston, MA, 1988.

    Google Scholar 

  42. Durfee, E. H. and Lesser, V. R.: Coordination through communication in distributed problem solving network, in M. N. Huhns (ed.),Distributed Artificial Intelligence, Pitman, London, UK, 1987, pp. 29–58.

    Google Scholar 

  43. Durfee, E. H. and Lesser, V. R.: Using partial global plans to coordinate distributed problem solvers, inProc. 10th International Joint Conference Artificial Intelligence, 1987, pp. 875–883.

  44. Durfee, E. H. and Lesser, V. R.: The distributed artificial intelligence melting Pet,IEEE Trans. System, Man and Cybernetics 21(6) (1991), 1301–1306.

    Google Scholar 

  45. Durfee, E. H. and Lesser, V. R.: Partial global planning, a coordination framework for distributed hypothesis formation,IEEE Trans. System, Man and Cybernetics 21(5) (1991), 1167–1183.

    Google Scholar 

  46. Durfee, E. H., Lesser, V. R. and Corkill, D. D.: Increased coherence in a distributed problem solving network, inProc. 9th International Joint Conference Artificial Intelligence, 1985, pp. 1025–1030.

  47. Egilmez, K. and Kim, S. H.: Teamwork among intelligent agents: Framework and case study in robotic service, in A. Famili, D. S. Nau and S. H. Kim (eds),AI Applications in Manufacturing, AAAI Press, 1992.

  48. Erman, L. D., Hayes-Roth, F., Lesser, V. R. and Reddy, D. R.: The Hearsay III speech understanding system: integrating knowledge to resolve uncertainty,Computing Surveys 12 (1980), 213–253.

    Google Scholar 

  49. Astrom, K. J. et al.: Expert control,Automatica 22(3) (1986), 277–286.

    Google Scholar 

  50. Firschein, O. et al.:Artificial Intelligence for Space Station Automation, Noyes, NJ, 1986.

    Google Scholar 

  51. Famili, A. and Turney, P.: Application of machine learning to industrial planning and decision making, in A. Famili, D. S. Nau and S. H. Kim (eds),AI Applications in Manufacturing, AAAI Press, 1992.

  52. Fikes, R. E. and Nilsson, N. J.: STRIPS: A new approach to the application of theorem proving problem solving,Artificial Intelligence 2 (1971), 198–208.

    Google Scholar 

  53. Fikes, R. E. et al.: Learning and executing generalized robot plans,Artificial Intelligence 3 (1972), 251–288.

    Google Scholar 

  54. Findeisen, W. et al.:Control and Coordination in Hierarchical Systems, Wiley, New York, 1980.

    Google Scholar 

  55. Findler, N. V. and Lo, R.: An examination of distributed planning in the world of air traffic control,J. Parallel and Distributed Computing 3 (1986), 411–431.

    Google Scholar 

  56. Fox, B. R. and Kempf, K. G.: Opportunistic scheduling for robotics assembly, inProc. IEEE Int. Conf. Robotics and Automation, 1985, pp. 880–889.

  57. Georgeff, M. P.: Communication and interaction in multiagent planning, inProc. 8th Int. Joint Conference in Artificial Intelligence, 1983, pp. 125–129.

  58. Georgeff, M. P.: In M. P. Georgeff and A. L. Lansky (eds),Actions, Processes and Causality Reasoning About Actions and Plans, Morgan Kaufmann, 1986.

  59. Glorioso, R. M. and Colon Osorio, F. C.:Engineering Intelligent Systems, Digital Press, 1980.

  60. Green, C.: Theorem-proving by resolution as a basis for question-answering systems, inMachine Intelligence, Vol. 4, American Elsevier, New York, NY, 1969.

    Google Scholar 

  61. Lum, H. (ed.):Machine Intelligence and Autonomy for Aerospace Systems, AIAA, Washington, DC, 1988.

    Google Scholar 

  62. Hadavi, K., Hsu, W. L., Chen, T. and Lee, C. N.: An architecture for real time scheduling, in A. Famili, D. S. Nau and S. H. Kim (eds),AI Applications in Manufacturing, AAAI Press, 1992.

  63. Halpern, J., Manna, Z. and Moszkowski, B.: A hardware semantics based on temporal intervals, inProc. 9th ICALP, pp. 278–292, Springer Lecture Notes in Computer Science 54, 1983.

  64. Hammond, K. J.: CHEF: A model of case-based planning, inProc. 5th National Conference on Artificial Intelligence, 1986, pp. 267–271.

  65. Hayes-Roth, B.: A blackboard architecture for control,Artificial Intelligence 26 (1985), 251–321.

    Google Scholar 

  66. Hoare, C. A. R.: Communicating sequencial processes,Communications of the ACM 21(8) (1978), 666–677.

    Google Scholar 

  67. Holland, J. H.: Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems, in R. S. Michalski, J. G. Carbonell and T. M. Mitchell (eds),Machine Learning, Morgan Kaufmann, 1986.

  68. Homem de Mello, L. S. and Sanderson, A. C.: Planning repair sequences using the AND/OR graph representation of assembly plans, inProc. IEEE Int. Conf. on Robotics and Automation, 1988, pp. 1861–1862.

  69. Homem de Mello, L. S. and Sanderson, A. C.: Task sequence planning for assembly, inProc. 12th World Congress Scientific Computation, 1988, pp. 390–392.

  70. Homem de Mello, L. S. and Sanderson, A. C.: AND/OR graph representation of assembly plans,IEEE Transactions on Robotics and Automation 6(2) (1990), 188–199.

    Google Scholar 

  71. Homem de Mello, L. S. and Sanderson, A. C.: A correct and complete algorithm for the generation of mechanical assembly sequences,IEEE Transactions on Robotics and Automation 7(2) (1991), 228–240.

    Google Scholar 

  72. Homem de Mello, L. S. and Sanderson, A. C.: Representation of mechanical assembly sequences,IEEE Transactions on Robotics and Automation 7(2) (1991), 211–227.

    Google Scholar 

  73. Homem de Mello, L. S. and Sanderson, A. C.: Two criteria for the selection of assembly plans, maximizing the flexibility of sequencing the assembly tasks and minimizing the assembly time through parallel execution of assembly tasks,IEEE Transactions on Robotics and Automation 7(5) (1991), 626–633.

    Google Scholar 

  74. Jin, V. Y. and Levis, A. H.: Compensatory behavior in team decision making, inProc. IEEE Int. Symp. Intelligent Control, Philadelphia, PA, 1990, pp. 107–112.

  75. Kautz, H. A.: A circumscriptive theory of plan recognition, in P. R. Cohen, J. Morgan and M. E. Pollack (eds),Intentions in Communication, MIT Press, Cambridge, MA, 1990.

    Google Scholar 

  76. Kocabas, S.: A review of learning,The Knowledge Engineering Review 6(3) (1991), 195–222.

    Google Scholar 

  77. Kodratoff, Y. and Michalski, R. S. (eds):Machine Learning: An Artificial Intelligence Approach, Vol. III, Morgan Kaufmann, 1990.

  78. Konolige, K.: A deductive model of belief, inProc. 8th Int. Joint Conference in Artificial Intelligence, 1983, pp. 377–381.

  79. Konolige, K.: Defeasible argumentation in reasoning about events, inProc. Int. Symp. Machine Intelligence and Systems, Torino, Italy, 1989.

  80. Konolige, K. and Nilsson, N. J.: Multiple-agent planning systems, inProc. 1st Annual Conference of the American Association for Artificial Intelligence, 1980, pp. 138–141.

  81. Koren, Y.:Robotics for Engineers, McGraw-Hill, New York, NY, 1985.

    Google Scholar 

  82. Kripke, S.: Semantical considerations on modal logic,Acta Philosophica Fennica 16 (1963), 83–94.

    Google Scholar 

  83. Kusiak, A.:Intelligent Manufacturing Systems, Prentice-Hall, 1990.

  84. Morgenstern, L.: A first order theory of planning, knowledge and action, in Morgan Kaufmann (ed.),Proc. Conf. Theoretical Aspects of Reasoning About Knowledge, Los Altos, 1986.

  85. Morgenstern, L.: Knowledge preconditions for actions and plans, inProc. 10th Int. Joint Conf. Artificial Intelligence, 1987, pp. 867–874.

  86. Ladner, R. E.: The complexity of problems in systems of communicating sequential processes, inProc. 11th ACM Symp. Theory of Computing, 1979, pp. 214–223.

  87. Laird, J. E., Newell, A. and Rosenbloom, P.: Chunking in SOAR,Machine Learning 1 (1986), 11–46.

    Google Scholar 

  88. Laird, J. E., Newell, A. and Rosenbloom, P.: SOAR: An architecture for general intelligence,Artificial Intelligence 33 (1987), 1–64.

    Article  Google Scholar 

  89. Langley, P.: BACON1 a general discovery system, inProc. 2nd National Conf. Canadian Society for Computational Studies, 1978.

  90. Langley, P., Simon, H. A., Bradshaw, G. L. and Zutkow, J. M.:Scientific Discovery: Computational Explorations of the Creative Processes, MIT Press, 1987.

  91. Lansky, A. L. and Folgesong, D.: Localized representation and planning methods for parallel domain, inProc. 6th National Conf. Artificial Intelligence, 1987, pp. 240–245.

  92. Lee, S.: Disassemply planning by subassembly extraction, inProc. 3rd ORSA/TIMS Conf. Flexible Manufacturing Systems, Elsevier Science, MIT, MA, 1989.

    Google Scholar 

  93. Lee, S.: Backward assemply planning, in A. Famili, D. S. Nau and S. H. Kim (eds),AI Applications in Manufacturing, AAAI Press, 1992.

  94. Lee, S. and Shin, Y. G.: Automatic construction of assembly partial-order graph, inProc. 1988 Int. Conf. Computer Integrated Manufacturing, Troy, New York, RPI, 1988.

    Google Scholar 

  95. Lenat, D. B.: On automated scientific theory formation: A case study using the AM program,Machine Intelligence 9 (1979), 251–238.

    Google Scholar 

  96. Lenat, D. B.: EURISKO: A program that learns new heuristics and domain concepts,Artificial Intelligence 21(1) (1983), 61–98.

    Google Scholar 

  97. Lenat, D. B., Davis, R., Doyle, J., Genesereth, M., Goldstein, I. and Schrobe, H.: Reasoning about reasoning, in F. Hayes-Roth, D. A. Waterman and D. B. Lenat (eds),Building Expert Systems, Addison-Wesley, Reading, MA, 1983.

    Google Scholar 

  98. Lenat, D. B. and Feigenbaum, E. A.: On the threshold of knowledge, inProc. 10th Int. Joint Conf. Artificial Intelligence, 1987, pp. 1173–1182.

  99. Lesser, V. R. and Corkill, D.: Functionally accurate cooperative distributed systems,IEEE Trans. Systems, Man and Cybernetics 11(1) (1981), 81–96.

    Google Scholar 

  100. Lesser, V. R. and Corkill, D.: The application of artificial intelligence techniques to cooperative distributed processing, inProc. 6th Int. Joint Conf. Artificial Intelligence, 1979, pp. 537–540.

  101. Lesser, V. R. and Erman, L. D.: Distributed interpretation: A model and experiment,IEEE Trans. Computers 29(12) (1980), 1144–1163.

    Google Scholar 

  102. Lesser, V. R. et al.: A high level simulation testbed for cooperative distributed problem solving, inProc. 3rd Int. Conf. Distributed Computer Systems, 1982, pp. 341–349.

  103. Litman, D. J. and Allen, J. F.: A plan recognition model for subdialogues in conversations,Cognitive Science 11(2) (1987).

  104. Mesarovic, M., Macko, D. and Takahara, Y.:Theory of Hierarchical, Multilevel Systems, Academic Press, 1970.

  105. Manna, Z. and Wolper, P.: Synthesis of communicating processes from temporal logic specifications, inProc. Workshop on Logics of Programs, Lecture Notes in Computer Science, Springer-Verlag, 1981.

  106. McCarthy, J. and Hayes, P.: Some philosophical problems for the stand-point of artificial intelligence, inMachine Intelligence 4, Meltzer, B., 1969.

  107. McDermott, D.: A deductive model of control of a problem solver, inProc. Workshop on Pattern-Directed Inference Systems, pp. 2–7, SIGART Newsletter No. 63, 1977.

  108. McDermott, D.: A deductive model of control of a problem solver, inProc. 5th Int. Joint Conf. Artificial Intelligence, 1977, pp. 229–234.

  109. McDermott, D.:Flexibility and Efficiency in a Computer Program for Designing Circuits, MIT AI Laboratory Technical Report 402, 1977.

  110. McDermott, D.: Circuit design as problem solving, inProc. IFIP Workshop in Artificial Intelligence and Pattern Recognition in Computer-Aided Design, Springer-Verlag, New York, NY, 1978.

    Google Scholar 

  111. McDermott, D.: Planning and acting,Cognitive Science 2 (1978), 71–109.

    Google Scholar 

  112. McDermott, D.: A temporal logic for reasoning about actions and plans,Cognitive Science 6 (1982), 101–155.

    Google Scholar 

  113. Mendel, J. and Zapalac, J.: The application of techniques of artificial intelligent to control system design, in C. T. Leondes (ed.),Advances in Control Systems, Academic Press, NY, 1968.

    Google Scholar 

  114. Meystel, A.: Intelligent control: Issues and perspectives, inProc. IEEE Workshop on Intelligent Control, 1985, pp. 1–15.

  115. Michalski, R. S.: Understanding the nature of learning: Issues and research directions, in R. S. Michalski, J. G. Carbonell and T. M. Mitchell (eds),Machine Learning, Morgan Kaufmann, 1986.

  116. Michalski, R. S.: Concept learning, in S. C. Shapiro (ed.),Encyclopedia of Artificial Intelligence, Wiley, 1987, pp. 185–194.

  117. Michalski, R. S., Carbonell, J. G. and Mitchell, T. M. (eds):Machine Learning: An Artificial Intelligence Approach, Morgan Kaufmann, 1983.

  118. Michalski, R. S., Carbonell, J. G. and Mitchell, T. M. (eds):Machine Learning: An Artificial Intelligence Approach, Vol. II, Morgan Kaufmann, 1986.

  119. Minton, S. (ed.):Machine Learning Methods for Planning, Morgan Kaufmann, 1993.

  120. Minton, S., Johnston, M. D., Philips, A. B. and Laird, P.: Solving large-scale constraint satisfaction and scheduling problems using a heuristic repair method, inProc. 8th National Conf. Artificial Intelligence, Menlo Park, CA, 1990, pp. 17–24.

  121. Mitchell, T.: Learning and problem solving, inProc. 8th Int. Joint Conf. Artificial Intelligence, Karlsruhe, Germany, 1983, pp. 1139–1151.

  122. Mitchell, T., Keller, R. M. and Kedar-Cabelli, S. T.: Explanation based generalization: A unifying view,Machine Learning 1(1) (1986), 47–80.

    Google Scholar 

  123. Moore, R.: Reasoning about knowledge and action, inProc. 5th Int. Joint Conf. Artificial Intelligence, Cambridge, MA, 1977, pp. 223–227.

  124. Moore, R.: Reasoning about knowledge and action,SRI Technical Note 191, 1980.

  125. Saridis, G. N.: Intelligent controls for advanced automated processes, inProc. Automated Decision Making and Problem Solving Conference, NASA CP-2180, 1980.

  126. Nawell, A. and Simon, H. A.:Human Problem Solving, Prentice-Hall, Englewood Cliffs, NJ, 1972.

    Google Scholar 

  127. Nii, H. P., Feigenbaum, E. A., Anton, J. J. and Rockmore, A. J.: Signal-to-symbol transformation: HASP/SIAP case study,AI Magazine 3 (1982), 23–35.

    Google Scholar 

  128. Nii, H. P. and Aiello, N.: AGE (attempt to generalize): A knowledge-based program for building knowledge-based programs, inProc. 6th Int. Joint Conf. Artificial Intelligence, Tokyo, Japan, 1979, pp. 645–665.

  129. Nilsson, N. J.:Problem Solving Methods in Artificial Intelligence, McGraw Hill, New York, NY, 1971.

    Google Scholar 

  130. Nordhausen, B. and Langley, P.: Towards an integrated discovery system, inProc. 10th Int. Joint Conf. Artificial Intelligence, 1987, pp. 198–200.

  131. Georgeff, M. P., Lansky, A. L. and Bessiere, P.: Procedural Logic, inProc. 9th Int. Joint Conf. Artificial Intelligence, 1985, pp. 516–523.

  132. Passino, K. M.: Restructurable controls and artificial intelligence,McDonell Aircraft Internal Report IR-0392, 1986.

  133. Passino, K. M. and Antsaklis, P. J.: Artificial intelligence planning problems in a Petri Net framework,Dept. of Electrical and Computer Engineering, University of Notre Dame, Technical Report 880, 1988.

  134. Passino, K. M. and Antsaklis, P. J.: Fault detection and identification in an intelligent restructurable controller,J. Intelligent and Robotic Systems 1 (1988), 145–161.

    Google Scholar 

  135. Passino, K. M. and Antsaklis, P. J.: On the optimal control of discreete event systems, inProc. 28th IEEE Conf. Decision and Control, Tampa, FL, 1989, pp. 2713–2718.

  136. Passino, K. M. and Antsaklis, P. J.: A system and control theoretic perspective on artificial intelligence planning systems,J. Applied Artificial Intelligence 3 (1989), 1–32.

    Google Scholar 

  137. Passino, K. M. and Antsaklis, P. J.: Event rates and aggregation in hierarchical discrete event systems,J. Discrete Event Dynamic Systems: Theory and Applications 1(3) (1992), 271–287.

    Google Scholar 

  138. Passino, K. M. and Ozguner, U.: Modeling and analysis of hybrid systems: Examples, inProc. 1991 IEEE Int. Symp. Intelligent Control, Arlington, VA, 1991, pp. 251–256.

  139. Peek, M. D. and Antsaklis, P. J.: Parameter learning for performance adaptation,IEEE Control Systems Magazine 10 (1990), 3–11.

    Google Scholar 

  140. Pelavin, R. and Allen, J. F.: A formal logic of plans in temporally rich domains,Proc. IEEE, Special Issue on Knowledge Representation 74(10) (1986), 1364–1382.

    Google Scholar 

  141. Peterson, J. L.:Petri Net Theory and the Modeling of Systems, Prentice-Hall, 1981.

  142. Pollack, P. R.: Plans as complex mental attitudes, in P. R. Cohen, J. Morgan and M. E. Pollack (eds),Intentions in Communication, MIT Press, Cambridge, MA, 1990.

    Google Scholar 

  143. Porter, B.W. and Kibler, D. F.: Experimental goal regression: A method for learning problem-solving heuristics,Machine Learning 1(3) (1986), 249–286.

    Google Scholar 

  144. Pratt, V. R.: Semantical considerations on floyd-hoare logic, inProc. 17th IEEE Symposium on Foundations of Computer Science, 1976, pp. 108–121.

  145. Quilan, J. R.: Learning efficient classification procedures and their application to chess and games, in R. S. Michalski, J. G. Carbonell and T. M. Mitchell (eds),Machine Learning: An Artificial Intelligent Approach, Morgan Kaufmann, 1983.

  146. Rescher, J. and Urquhart, A.:Temporal Logic, Springer-Verlag, 1971.

  147. Rivest, R. L. and Remmele, W.: Machine learning: The human connection, inSiemens Review, Vol. 2, 1988, pp. 33–37.

    Google Scholar 

  148. Rosenschein, J. S.: Synchronization of multi-agent plans, inProc. 2nd Nat. Conf. Artificial Intelligence, 1982, pp. 115–119.

  149. Sacerdoti, E. D.:A Structure for Plans and Behavior, North-Holland, Amsterdam, 1974.

    Google Scholar 

  150. Sandewall, H. and Ronnquist, R.: A representation of action structures, inProc. 5th Nat. Conf. Artificial Intelligence, 1986, pp. 89–97.

  151. Saridis, G. N.:Self-Organizing Control of Stochastic Systems, Marcel Dekker, New York, NY, 1977.

    Google Scholar 

  152. Saridis, G. N.: Toward the realization of intelligent controls,Proc. IEEE 67(8) (1979), 1115–1133.

    Google Scholar 

  153. Saridis, G. N.: Intelligent robotic control,IEEE Transaction on Automatic Control 28(5) (1983), 547–556.

    Google Scholar 

  154. Saridis, G. N.: Foundations of the theory of intelligent controls, inProc. IEEE Workshop on Intelligent Control, 1985, pp. 23–28.

  155. Saridis, G. N.: Knowledge implementation: Structures of intelligent control systems, inProc. IEEE Int. Symp. Intelligent Control, 1987, pp. 9–17.

  156. Saridis, G. N.: Analytic formulation of the principle of increasing precision with decreasing intelligence for intelligent machines,Automatica 25(3) (1989), 461–467.

    Google Scholar 

  157. Schmidt, C. F., Sridharan, N. S. and Goodson, J. L.: The plan recognition problem: An intersection of artificial intelligence and psychology,Artificial Intelligence 10(1) (1978).

  158. Sidner, C. L.: Plan parsing for intended response recognition in discourse,Computational Intelligence 1(1) (1985).

  159. Simon, H. A.: Why should machines learn?, in R. S. Michalski, J. G. Carbonell and T. M. Mitchell (eds),Machine Learning, Morgan Kaufmann, 1983.

  160. Smith, R. G.: A framework for distributed problem solving, inProc. 6th Int. Joint Conf. Artificial Intelligence, 1979, pp. 836–841.

  161. Smith, R. G.: The contract-net protocol: High level communication and control in a distributed problem solver,IEEE Transactions on Computers 29(12) (1980), 1104–1113.

    Google Scholar 

  162. Smith, R. G. and Davis, R.: Framework for cooperation in distributed problem solving,IEEE Trans. Systems, Man and Cybernetics 11(1) (1981), 61–70.

    Google Scholar 

  163. Smith, S. F., Kempf, K. G. and Kempf, N. K.: Exploiting local flexibility during execution of pre-computed schedules, in A. Famili, D. S. Nau and S. H. Kim (eds),AI Applications in Manufacturing, AAAI Press, 1992.

  164. Steeb, R., Cammarata, S., Hayes-Roth, F. A., Thorndyke, P. W. and Wesson, R. B.: Distributed intelligence for fleet control,RAND Corp. Rep. R-2728-ARPA, 1981.

  165. Steeb, R., Cammarata, S., Narain, S., Rothenberg, J. and Giarla, W.: Cooperative intelligence for remotely piloted vehicle fleet control,RAND Corp. Rep. R-3408-ARPA, 1986.

  166. Stefik, M.: Planning and meta-planning MOLGEN: Part II,Artificial Intelligence 16 (1981), 141–169.

    Google Scholar 

  167. Stefik, M.: Planning with constrains,Artificial Intelligence 16 (1981), 111–140.

    Google Scholar 

  168. Stiver, J. M. and Antsaklis, P. J.: A novel discrete event system approach to modeling and analysis of hybrid control systems, inProc. 29th Allerton Conference on Communication, Control and Computing, University of Illinois at Urbana-Champaign, 1991.

  169. Stuart, C.: An implementation of a multi-agent plan synchronizer, inProc. 9th Int. Joint Conf. Artificial Intelligence, 1985, pp. 1031–1033.

  170. Stuart, C.: An implementation of a multi-agent plan synchronizer using a temporal logic theorem prover,SRI AI Center, 1985.

  171. Sussman, G. J.:A Computational Model of Skill Acquisition, Technical Report 297 MIT AI Lab, 1973.

  172. Tate, A.: Generating projects networks, inProc. 5th Int. Joint Conf. Artificial Intelligence, pp. 888–893, 1977.

  173. Tennenholtz, M. and Moses, Y.:On Cooperation in a Multi-Entity Model, Technical Report, Weismann Institute, 1989.

  174. Tennenholtz, M. and Moses, Y.: On cooperation in a multi-entity model (preleminary report), inProc. 11th Int. Joint Conf. Artificial Intelligence, 1989, pp. 918–923.

  175. Thorndyke, P. W., McArthur, D. and Cammarata, C.: AUTOPILOT a distributed planner for air fleet control, inProc. 9th Int. Joint Conf. Artificial Intelligence, 1983, pp. 171–177.

  176. Turner, P. R. et al.:Autonomous Systems: Architecture and Implementation, Jet Propulsion Laboratories, Report No. JPLD-1656, 1984.

  177. Ozguner, U.: Decentralized and distributed control approaches and algorithms, inProc. 28th IEEE Conf. Decision and Control, Tampa, FL, 1989, pp. 1289–1294.

  178. Valavanis, K. P.:The Analytical Design of Intelligent Machines, PhD Thesis, Electrical and Computer Engineering Department, Rensselaer Polytechnic Institute, Troy, NY, 1986.

    Google Scholar 

  179. Valavanis, K. P. and Carello, S. J.: An efficient planning technique for robotic assemblies and intelligent robotic systems,J. Intelligent and Robotic Systems 3(4) (1990), 321–347.

    Google Scholar 

  180. Valavanis, K. P. and Saridis, G. N.: Information theoretic modeling of intelligent robotic systems, Part I: The organization level, inProc. 26th Conf. Decision and Control, Los Angeles, CA, 1987, pp. 619–626.

  181. Valavanis, K. P. and Saridis, G. N.: Information theoretic modeling of intelligent robotic systems, Part II: The coordination and execution levels, inProc. 26th Conf. Decision and Control, pp. 627–633, Los Angeles, CA, 1987.

  182. Valavanis, K. P. and Stellakis, H. M.: A general organizer model for robotic assemblies and intelligent robotic systems,IEEE Trans. System, Man and Cybernetics 21(2) (1991), 302–316.

    Google Scholar 

  183. Weyrauch, R. W.: Prolegomena to a theory of mechanized formal reasoning,Artificial Intelligence 13 (1980), 133–170.

    Google Scholar 

  184. Wilenskey, R.: Meta-planning: Representing and using knowledge about planning in problem solving and natural language understanding,Cognitive Science 5 (1981), 197–233.

    Google Scholar 

  185. Wilkins, D. E.:Practical Planning: Extending the Classical AI Planning Paradigm, Morgan Kaufmann, Los Altos, CA, 1988.

    Google Scholar 

  186. Woods, S.: Dynamic world simulation for planning with multiple agents, inProc. 8th Int. Joint Conf. Artificial Intelligence, 1983, pp. 69–71.

  187. Wos, L.:Automated Reasoning: 33 Basic Research Problems, Prentice Hall, NJ, 1988.

    Google Scholar 

  188. Zaghoul, M. E. et al.: A machine-learning classification approach for IC manufacturing control based on test structure measurements,IEEE Transactions on Semiconductor Manufacturing 2(2) (1989), 47–53.

    Google Scholar 

  189. Zeigler, B. P.: Knowledge representation from Newton to Minsky and beyond,J. Applied Artificial Intelligence 1 (1987), 87–107.

    Google Scholar 

  190. Zeigler, B. P.: DEVS representation of dynamical systems: Event based intelligent control,Proc. IEEE 77(1) (1989), 72–80.

    Google Scholar 

  191. Zeigler, B. P. and Chi, S. D.: Model-based concepts for autonomous systems, inProc. IEEE Int. Symp. Intelligent Control, Philadelphia, PA, 1990, pp. 27–32.

  192. Zeigler, B. P. and Sungdo, C.: Model-based architecture concepts for autonomous systems design and simulation, inAn Introduction to Intelligent and Autonomous Control, Kluwer Academic Publishers, 1993.

  193. Zhong, H. and Wonham, W. M.: On the consistency of hierarchical supervision in discrete-event systems,IEEE Transactions on Automatic Control 35(10) (1990), 1125–1134.

    Google Scholar 

  194. Zhu, D. and Latombe, J. C.: New heuristics algorithms for efficient hierarchical path planning,IEEE Transactions on Robotics and Automation 7(1) (1991), 9–20.

    Google Scholar 

  195. Zweben, M., Deale, M. and Gargan, R.: Anytime rescheduling, inProc. DARPA Workshop on Innovative Approaches to Planning and Scheduling, 1990.

  196. Zytkow J. M. and Simon, H. A.: A theory of historical discovery: The construction of computential models,Machine Learning 1 (1986), 107–137.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Robotics and Automation Laboratory, The Center for Advanced Computer Studies & A-CIM Center, The University of Southwestern Louisiana, 70504-4330, Lafayette, LA, USA

    A. I. Kokkinaki & K. P. Valavanis

Authors
  1. A. I. Kokkinaki
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. K. P. Valavanis
    View author publications

    You can also search for this author inPubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kokkinaki, A.I., Valavanis, K.P. On the comparison of AI and DAI based planning techniques for automated manufacturing systems. J Intell Robot Syst 13, 201–245 (1995). https://doi.org/10.1007/BF01424008

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01424008

Key words