Abstract
The presented system COSIMA learns floorplanning rules from structural descriptions incrementally, using a number of cooperating machine learning strategies: Selective inductive generalization generates most specific generalizations using predicate weights to select the best one heuristically. The predicate weights are adjusted statistically. Inductive specialization eliminates overgeneralizations. Constructive induction improves the learning process in several ways. The system is organized as a learning apprentice system. It provides an interactive design tool and can automate single floorplanning steps.
Chapter PDF
Similar content being viewed by others
References
Anderson, J.R. (1986). Knowledge Compilation. In Machine Learning: An Artificial Intelligence Approach Vol II, eds. R. S. Michalski, J. G. Carbonell, T. M. Mitchell, 289–310. Morgan Kaufmann.
Bergadano, F., Giordana, A. & Saitta, L. (1988). Automated Concept Acquisition in Noisy Domains. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Bisson, G. (1992). Conceptual Clustering in a First Order Logic Representation. Proc. 10th ECAI, August 3–7, Vienna.
Dietterich, T.G., & Michalski, R.S. (1983). A Comparative Review of Selected Methods for Learning from Examples. In Machine Learning: An Artificial Intelligence Approach, eds. R. S. Michalski, J. G. Carbonell, T. M. Mitchell, pp 41–81. Palo Alto: Tioga Press.
Elio, R., & Watanabe, L. (1991). An Incremental Deductive Strategy for Controlling Constructive Induction in Learning from Examples. Machine Learning Journal. Vol 7, 7–44, Boston: Kluver Academic Publishers.
Gunsch, G.H. (1991). Opportunistic Constructive Induction: Using Fragments of Domain Knowledge to Guide Construction. PhD Thesis, University of Illinois at Urbana-Champaign.
Haussler, D. (1989). Learning Conjunctive Concepts in Structural Domains. Machine Learning Journal. Vol 4, 7–40, Boston: Kluver Academic Publishers.
Hayes-Roth, F., & McDermott, J. (1977). Knowledge Acquisition from Structural Descriptions. Proc. 5th IJCAI, 356–362.
Herrmann, J., & Beckmann, R. (1992). LEFT — A Learning Tool for Early Floorplanning. Proc. 18th Euromicro Conference, pp 587–594, September 14–17, Paris.
Herrmann, J., & Beckmann, R. (1994). LEFT — A System that Learns Rules about VLSI-Design from Structural Descriptions. (to appear) In Y. Kodratoff (Guest Ed.), Applied Artificial Intelligence, Special Issue on Real-World Applications of Machine Learning Techniques. London: Taylor and Francis Ltd.
Kietz, J.U., & Wrobel, S. (1992). Controlling the Complexity of Learning in Logic Though Syntactic and Task-Oriented Models. Arbeitspapiere der GMD Nr. 503, GMD, Schloß Birlinghoven.
Kodratoff, Y., & Langley, P. (Eds.), (1993). Real-World Applications of Machine Learning. Workshop Notes on the ECML-93 Workshop. Vienna.
Michalski, R.S. (1983). A Theory and Methodology of Inductive Learning. In R.S. Michalski, T.M. Mitchell and J.G. Carbonell (eds.), Machine Learning: An Artificial Intelligence Approach. Palo Alto, CA: Tioga Publishing.
Michalski, R.S. (1993). Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning. Machine Learning Journal. Vol 11, 111–152, Boston: Kluver Academic Publishers.
Mitchell, T.M., Mahadevan, S., & Steinberg, L. (1985). LEAP — A Learning Apprentice for VLSI Design. Proc. 9th IJCAI, pp 573–580, August 18–23, Los Angeles.
Morik, K. (1992). Applications of Machine Learning. Proceedings of the Sixth European Knowledge Acquisition Workshop (pp 9–13). Springer.
Morik, K. (1993). Balanced Cooperative Modeling. Machine Learning Journal. Vol 11, 217–236, Boston: Kluver Academic Publishers.
S. Muggleton (1987). DUCE, an Oracle Based Approach to Constructive Induction, Proc of the 10th Int. Joint Conference on Artificial Intelligence, IJCAI87
Pazzani, M., & Kibler, D. (1992). The Utility of Knowledge in Inductive Learning. Machine Learning Journal. Vol 9, 57–95, Boston: Kluver Academic Publishers.
Reipa, D. (1993). Konstruktive Induktion für eine strukturelle Beschreibungssprache. Diploma Thesis, University of Dortmund.
Saitta, L., Botta, M., & Neri, F. (1993). Multistrategy Learning and Theory Revision. Machine Learning Journal. Vol 11, 153–172, Boston: Kluver Academic Publishers.
van Someren, M. (Ed.) (1993). Learning and Problem Solving. Workshop Notes on the MLnet Workshop. Blanes.
Wirth, R., & O'Rourke, P.O. (1991). Constraints for Predicate Invention. Proc. of the 8th Int. Machine Learning Conference, Evanston: Morgan Kaufmann.
Watanabe, H. (1987). FLUTE — An Expert Floorplanner for Full-Custom VLSI Design. IEEE Design & Test, pp 32–41. New York: Computer Society of the IEEE.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Herrmann, J., Ackermann, R., Peters, J., Reipa, D. (1994). A multistrategy learning system and its integration into an interactive floorplanning tool. In: Bergadano, F., De Raedt, L. (eds) Machine Learning: ECML-94. ECML 1994. Lecture Notes in Computer Science, vol 784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57868-4_55
Download citation
DOI: https://doi.org/10.1007/3-540-57868-4_55
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-57868-0
Online ISBN: 978-3-540-48365-6
eBook Packages: Springer Book Archive