Hostname: page-component-76fb5796d-25wd4 Total loading time: 0 Render date: 2024-04-26T05:40:09.185Z Has data issue: false hasContentIssue false
  • English
  • Français

Learning to cope with an open world

Published online by Cambridge University Press:  27 February 2009

Boi Faltings
Boi Faltings
Affiliation:
Artificial Intelligence Laboratory (LIA), Swiss Federal Institute of Technology (EPFL), IN-Ecublens, 1015 Lausanne, Switzerland
Get access Rights & Permissions [Opens in a new window]

Abstract

Science has developed detailed and well-founded theories for analyzing the behavior of artifacts. For example, Boeing was able to correctly verify an entirely new airplane, the Boeing 777, before any prototype was even built. However, there are few theories, and no computer systems, that would allow us to design structures with a similar degree of automation.

Type
Research Abstracts
Information
AI EDAM , Volume 10 , Issue 2: Special Issue: Machine learning in design , April 1996 , pp. 143 - 145
Copyright
Copyright © Cambridge University Press 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Faltings, B. (1992). Supporting creativity in symbolic computation. Proc. Second Int. Conf, on Computational Models of Creative Design, (Gero, J. & Sudweeks, I., Ed.), pp. 191–205.Google Scholar
Faltings, B., & Sun, K. (1996). FAMING: Supporting innovative mechanism shape design. Computer-Aided Design, (in press).CrossRefGoogle Scholar
Faltings, B., & Sun, K. (1995). Computer-aided creative mechanism design. Int. Joint Conf. Artif Intell., pp. 2055–2056. Morgan Kaufmann, San Mateo, California.Google Scholar
Lenat, D. (1984). The role of heuristics in learning by discovery: Three case studies. In Machine Learning: An Artificial Intelligence Approach. Springer-Verlag, NY.Google Scholar