Artificial Intelligence Review

, Volume 15, Issue 1–2, pp 63–78 | Cite as

Diagrammatic Reasoning: An Artificial Intelligence Perspective

  • Patrick Olivier


A common motivation for developing computationalframeworks for diagrammatic reasoning is the hope thatthey might serve as re-configurable tools for studyinghuman problem solving performance. Despite the ongoingdebate as to the precise mechanisms by which diagrams,or any other external representation, are used inhuman problem solving, there is little doubt thatdiagrammatic representations considerably help humanssolve certain classes of problems. In fact, there area host of applications of diagrams and diagrammaticrepresentations in computing, from data presentationto visual programming languages. In contrast to boththe use of diagrams in human problem solving and theubiquitous use of diagrams in the computing industry,the topic of this review is the use of diagrammaticrepresentations in automated problem solving. Wetherefore investigate the common, and often implicit,assumption that if diagrams are so useful for humanproblem solving and are so apparent in humanendeavour, then there must be analogous computationaldevices of similar utility.

diagrammatic reasoning knowledge representation and reasoning 


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  1. Anderson, M. and McCartney, R. Diagrammatic Reasoning and Cases. In Proceedings of the 13th National Conference on Artificial Intelligence. Portland, Oregon.Google Scholar
  2. Aurnague, M. & Vieu, L. (1993). A Three-Level Approach to the Semantics of Space. In Zelinsky-Wibbelt, C. (ed.) The Semantics of Prepositions: From Mental Processing to Natural Language Processing, 393–440. Mouton de Gruyter: Berlin.Google Scholar
  3. Davis, E. (1990). Representations of Commonsense Knowledge. Morgan Kaufmann Publishers: San Mateo, CA.Google Scholar
  4. Decuyper, J., Keymeulen, D. & Steels, L. (1995). A Hybrid Architecture for Modeling Liquid Behavior. In Glasgow, J., Narayanan, N.H. & Chandrasekaran, B. (eds.) Diagrammtic Reasoning: Cognitive and Computational Perspectives, 731–751. AAAI Press: Menlo Park, CA.Google Scholar
  5. Funt, B.F. (1980). Problem Solving with Diagrammatic Representations. Artificial Intelligence 13: 201–230.Google Scholar
  6. Furnas, G.W. (1992). Reasoning With Diagrams Only. In Reasoning with Diagrammatic Representations: Papers from the 1992 Spring Symposium. Technical Report SS–92–02, 115–120. AAAI Press: Menlo Park, CA.Google Scholar
  7. Gardin, F. & Meltzer, B. (1989). Analogical representations for Naive Physics. Artificial Intelligence 38: 139–159.Google Scholar
  8. Gelernter, H. (1963). Realization of a Geometry Theorem Proving Machine. In Feigenbaum, E.A. & Feldman, J. (eds.) Computers and Thought, 134–152. McGraw-Hill: New York.Google Scholar
  9. Glasgow, J. (1993). Imagery: Computational and Cognitive Perspectives. Computational Intelligence 9(4): 309–333.Google Scholar
  10. Gordon, I.E. (1989). Theories of Visual Perception. Wiley: London.Google Scholar
  11. Herskovits, A. (1986). Language and Spatial Cognition: An Interdisciplinary Study of the Prepositions in English. Cambridge University Press: New York.Google Scholar
  12. Ioerger, T. (1994). The Manipulation of Images to Handle Indeterminacy in Spatial Reasoning. Cognitive Science 18: 551–593.Google Scholar
  13. Koedinger, K.R. & Anderson, J.R. (1992). Abstract Planning and Perceptual Chunks: Elements of Expertise in Geometry. Cognitive Science 14(4): 511–550.Google Scholar
  14. Kulpa, Z. (1994). Diagrammatic Representation and Reasoning. Machine GRAPHICS & VISION 3(1/2): 77–103.Google Scholar
  15. Langacker, R.W. (1988). A View of Linguistic Semantics. In Rudzka-Ostyn, B. (ed.) Topics in Cognitive Linguistics, 49–90, Benjamins: Amsterdam.Google Scholar
  16. Larkin, J.H. & Simon, A.S. (1987). Why a Diagram Is (Sometimes) Worth Ten Thousand Words. Cognitive Science 11: 65–99.Google Scholar
  17. Latecki, L. & Pribbenow, S. (1992). On Hybrid Reasoning for Processing Spatial Expression. In Proceedings of the European Conference on Artificial Intelligence (ECAI-92), 389–393.Google Scholar
  18. Levinson, S.C. (1996). Frames of Reference and Molyneux's Question: Crosslinguistic Evidence, In Bloom, P., Peterson, M.A., Nadel, L. & Garrett, M.F. (eds.) Language and Space. MIT Press: Cambridge, MA.Google Scholar
  19. Ludlow, N.D. (1992). Pictorial Representation of Text: Converting Text to Pictures. Ph.D. Thesis, Department of Artificial Intelligence, University of Edinburgh, August, 1992.Google Scholar
  20. McDougal, T. & Hammond, K. (1992). A Recognition Model of Geometry Theorem Proving. In Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society, 106–111. Lawrence Erlbaum Associates: Hillsdale, N. J.Google Scholar
  21. Narayanan, A., Ford, L., Manuel, D., Tallis, D. & Yazdani, M. (1994). Animating Language. Workshop on Integrating Natural Language and Vision, 12th National Conference on Artificial Intelligence (AAAI-94), Seattle, USA.Google Scholar
  22. Narayanan, N.H., Suwa, M. & Motoda, H. (1995). Diagram-Based Problem Solving: The Case of an Impossible Problem. Proceedings of the 17th Annual Conference of the Cognitive Science Society, Lawrence Erlbaum Associates, 206–211.Google Scholar
  23. Olivier, P. & Gapp, K.-P. (eds.) (1998). Representation and Processing of Spatial Expressions. Lawrence Erlbaum Associates: Hillsdale, N.J.Google Scholar
  24. Olivier, P., Nakata, K. & Ormsby, A.R.T. (1996). Occupancy Array-Based Kinematic Reasoning. Engineering Applications of Artificial Intelligence 9(5): 541–549.Google Scholar
  25. Olivier, P., Maeda, T. & Tsujii, J. (1994). Automatic Depiction of Spatial Descriptions. In Proceedings of the National Conference on Artificial Intelligence (AAAI-94), 1405–1410. July, Seattle, WA.Google Scholar
  26. Retz-Schmidt, G. (1988). Various Views of Spatial Prepositions. AI Magazine (summer) 9(2): 95–105.Google Scholar
  27. Schober, M.F. (1995). Speakers, Addressees and Frames of Reference: Whose Effort is Minimised in Conversations About Locations? Discourse Processes 20: 219–247.Google Scholar
  28. Schwartz, D.L. & Black, J.B. (1996). Analog Imagery inMentalModel Reasoning-Depictive Models. Cognitive Psychology 30(2): 154–219.Google Scholar
  29. Tessler, S., Iwasaki, Y. & Law, K. (1995). Qualitative Structural Analysis Using Diagrammatic Reasoning. In Proceedings of 14th International Joint Conference on Artificial Intelligence, 885–891. Montreal, Canada.Google Scholar
  30. Waltz, D.L. & Boggess, L. (1979). Visual Analog Representations for Natural Language Understanding. In Proceedings of the 6th International Joint Conference on Artificial Intelligence (IJCAI-79), 926–934. Tokyo, Japan.Google Scholar
  31. Yamada, A. (1993). Studies on Spatial Description Understanding Based on Geometric Constraint Satisfaction. Ph.D. Thesis, Department of Information Science, Kyoto University, January.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Patrick Olivier
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK

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