Making AI systems more creative: The IPC-model

Knowledge-Based Systems

Volumn 9, Issue 6, 1996, Pages 385-397

  Schmid, Klaus ^a  

^a FRAUNHOFER IESE   (Germany)

Author keywords

Creative systems; Discovery; IPC model; Psychology based model

Indexed keywords

COGNITIVE SYSTEMS; DATA PROCESSING; MODELS; PSYCHOLOGY COMPUTING;

CREATIVE SYSTEMS; IPC MODEL; PSYCHOLOGY BASED MODEL;

ARTIFICIAL INTELLIGENCE;

EID: 0030258421     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0950-7051(96)01049-0     Document Type: Article

References (48)

1. D.B. Lenat, AM: Discovery in mathematics as heuristic search, in Knowledge-Based Systems in AI, Advanced Computer Science Series, McGraw-Hill, 1982.
2. M. Yazdani, A computational model of creativity, in Richard Forsyth, (ed), Machine Learning: Principles and Techniques, Chapter 9, Chapman and Hall, 1989, pp. 171-184.
3. G. Ulman, Kreativität, Pädagogisches Zentrum, Veröffentlichungen Reihe C: Berichte, Band 11. Verlag Julius Beltz, Weinheim, Berlin, Basel, 1968.
4. I. Seiffge-Krenke, Probleme und Ergebnisse der Kreativitätsforschung, Verlag Hans Huber, Bern, 1974.
5. D.N. Perkins, The Mind's Best Work, 1st edn, Harvard University Press, 1981.
6. J.S. Gero and M.L. Maher, Introduction, in J.S. Gero and M.L. Maher (eds.) Modeling Creativity and Knowledge-Based Creative Design, Lawrence Erlbaum Associates, Hillsdale, NJ, 1992, pp. 1-6.
7. R.W. Weisberg, Kreativität und Begabung, Spektrum der Wissenschaft Verlagsgesellschaft, Heidelberg, 1989.
8. J.R. Anderson, A theory of the origins of human knowledge. Artif. Intell., 40 (1989) pp. 313-351.
9. D. Klahr and K. Dunbar, Dual space search during scientific discovery. Cogn. Sci., 12 (1988) 1-48.
10. K. Schmid, Creative Problem Solving and Automated Discovery - An Analysis of Psychological and AI Research, Technical Memo TM-95-04, DFKI GmbH, September 1995.
11. E. Roth, W.D. Oswald and K. Daumenlang, Intelligenz: Aspekte, Probleme, Perspektiven, W. Kohlhammer, Stuttgart, 4th edn, 1980.
12. D. Navin Chandra, Innovative design systems, where are we and where do we go from here? Part I: Design by association, Knowl. Eng. Rev., 7 (1992) 183-213.
13. R.V. Guha and D.B. Lenat, Language, representation, and contexts. J. Inf. Proc., 15 (1992) 340-349.
14. J.R. Anderson, Cognitive Psychology and Its Implications, W.H. Freeman, 2nd edn., 1985.
15. M. Pazzani, Learning causal patterns: Making a transition from data-driven to theory-driven learning. Mach. Learn., 11 (1993) 173-194.
16. M.A. Boden, The Creative Mind: Myths and Mechnisms, Basic Books, 1991.
17. B.L. Richards and R.J. Mooney, Automated refinement of first-order horn-clause domain theories, Mach. Learn., 19 (April 1995).
18. O. Hoffmann and M. Kollingbaum, Creativity as transformation: Multi-agent systems and human cognition, in L. Candy and E.A. Edmonds (eds.) Second International Symposium: Creativity and Cognition 1996, Proceedings, Loughborough, UK, 1996, pp. 19-26.
19. L. Candy, Understanding creativity: An empirical approach, in L. Candy and E.A. Edmonds (eds.) Second International Symposium: Creativity and Cognition 1996, Proceedings, Loughborough, UK, 1996, pp. 45-54.
20. E.A. Edmonds, L. Candy, R. Jones and B. Scafi, Support for collaborative design: Agents and emergence, Comm. ACM, 37 (1994) pp. 41-47.
21. A.E. Prieditis, Machine discovery of effective admissable heuristics. Mach. Learn., 12 (1993) 117-141.
22. K. Haase, Automated discovery, in R. Forsyth (ed), Machine Learning: Principles and Techniques, Chapter 7, Chapman and Hall, 1989, pp. 127-155.
23. D.B. Lenat, The role of heuristics in learning by discovery: Three case studies, in R.S. Michalski, J.G. Carbonell and T.M. Mitchell (eds.) Machine Learning, Chapter 9, Tioga Publishing Co., 1983, pp. 243-306.
24. D. Navin Chandra, Innovative design systems, where are we and where do we go from here? Part II: Design by exploration. Knowl. Eng. Rev., 7 (1992) 345-362.
25. S. McLaughlin, Emergent value in creative products: some implications for creative processes, in J.S. Gero and M.L. Maher (eds.) Modeling creativity and knowledge-based creative design, Chapter 4, Lawrence Erlbaum, Hillsdale, NJ, 1992, pp. 43-89.
26. J.L. Kolodner and L.M. Wills, Case-based creative design. AISB Q., 85 (1993) 50-57.
27. A.I. Miller, Scientific creativity: A comparative study of Henri Poincaré and Albert Einstein. Creativ. Res. J., 5 (1992) 385-418.
28. D. Navin Chandra, Exploration and Innovation in Design: Towards a Computational Model, Springer, New York, 1991.
29. S. Oshuga, How can knowledge-based systems solve large-scale problems?: Model-based decomposition and problem solving, Knowl.-Based Syst., 6 (1993) 38-62.
30. J.L. Kolodner and T.L. Penberthy, A case-based approach to creativity in problem solving, in Proc. 12th Annual Conf. of the Cognitive Science Society, Cambridge, MA, August 1990, pp. 978-985.
31. P.S. Rosenbloom, J.E. Laird, A. Newell and R. McCarl, A preliminary analysis of the SOAR architecture as a basis for general intelligence. Artif. Intell., 47 (1991) 289-325.
32. J.L. Kolodner, Case-Based Reasoning, Morgan Kaufmann, 1993.
33. G. Antoniou and I. Wachsmuth, Structuring and modules for knowledge bases: Motivation for a new model. Knowl.-Based Syst., 7 (1994) pp. 49-51.
34. D.B. Lenat, Eurisko: A program that learns new heuristics and domain concepts, the nature of heuristics III: Program design and results. Artif. Intell., 21 (1983) 61-98.
35. M.T. Cox and M. Freed, Using knowledge of cognitive behaviour to learn from failure, in J.W. Braham and G.E. Lasker (eds.) Proceedings of the 7th International Conference on Systems Research, Informatics, and Cybernetics; Baden-Baden, Germany, August '94, 1994, pp. 142-147.
36. P.N. Johnson-Laird and R.M.J. Byrne, Deduction, Lawrence Erlbaum, Hove, UK, 1991.
37. G. Holmes and C.G. Nevill-Manning, Feature selection via the discovery of simple classification rules, in G.E. Lasker and X. Liu (eds.) Advances in Intelligent Data Analysis, Vol. 1, 1995, pp. 75-79.
38. E.J. Wisniewski and D.L. Medin, Harpoons and long sticks: The interaction of theory and similarity in rule induction, in D.H. Fisher, M.J. Pazzani and P. Langley (eds.) Concept Formation: Knowledge and Experience in Unsupervised Learning, Chapter 9, Morgan Kaufmann, 1991, pp. 237-278.
39. W.M. Shen, Discovery as autonomous learning from the environment. Mach. Learn., 12 (1993) 143-165.
40. C.M. Seifert, K.J. Hammond, H.M. Johnson, T.M. Converse, T.F. McDougal and S.W. Vanderstoep, Case-based learning: Predictive features in indexing. Mach. Learn., 16 (1994) 37-56.
41. J. Clement, Observed methods for generating analogies in scientific problem solving. Cogn. Sci., 12 (1988) 563-586.
42. J. Wnek and R.S. Michalski, Hypothesis-driven constructive induction in AQ17-HCI: A method and experiments, Mach. Learn., 14 (1994) 139-168.
43. S. Wrobel, Concept formation during interactive theory revision. Mach. Learn., 14 (1994) 169-191.
44. R. Bergmann and W. Wilke, Building and refining abstract planning cases by change of representation language. J. Artif. Intell. Res., 3 (1995) 53-118.
45. M.M. Veloso and J.G. Carbonell, Learning by analogical replay in PRODIGY: First results, in Machine Learning - EWSL-91, European Working Session on Learning, Porto, Portugal, Springer Verlag, March 1991, pp. 375-390.
46. J. Denzinger and S. Schulz, Learning domain knowledge to improve theorem proving, in Proc. 13th CADE, New Brunswick, LNAI, Springer, 1996, to appear.
47. J. Ambach and A. Repenning, Puppeteers and directors: Supporting artistic design by combining direct-manipulation and delegation, in L. Candy and E.A. Edmonds (eds.) Second International Symposium: Creativity and Cognition 1996, Proceedings, Loughborough, UK, 1996, pp. 67-76.
48. E. Edmonds and L. Candy, Supporting the creative user: A criteria-based approach to interaction design, in L. Candy and E.A. Edmonds (eds.) Second International Symposium: Creativity and Cognition 1996, Proceedings, Loughborough, UK, 1996, pp. 57-66.