A logical framework for configuration software

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

A logical framework for configuration software

Full text

Pdf (426 KB)

Source

International Conference on Principles and Practice of Declarative Programming archive
Proceedings of the 11th ACM SIGPLAN conference on Principles and practice of declarative programming table of contents

Coimbra, Portugal

SESSION: Expressive logics table of contents

Pages 141-148

Year of Publication: 2009

ISBN:978-1-60558-568-0

Authors

Hanne Vlaeminck	Katholieke Universiteit Leuven, Heverlee, Belgium
Joost Vennekens	Katholieke Universiteit Leuven, Heverlee, Belgium
Marc Denecker	Katholieke Universiteit Leuven, Heverlee, Belgium

Sponsors

SIGPLAN: ACM Special Interest Group on Programming Languages
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 7, Downloads (12 Months): 7, Citation Count: 0

Additional Information:

abstract references index terms

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1599410.1599428 What is a DOI?

ABSTRACT

There are many reasons why software can be hard to implement. For important classes of applications, the main source of complexity is the domain knowledge that is involved. One such class is that of configuration software, which serves to assist a user in making choices in accordance with certain constraints. For instance, consider an application that helps students compose a study program that complies with all relevant university regulations. The reason why this may be difficult to implement is that these regulations can get quite complicated, making them hard to handle, at least for imperative programming methods. A better approach might be to follow the paradigm of a knowledge base system: explicitly represent the domain knowledge in a declarative way, and implement the behavior of the application by performing various logical inference methods on it. Doing this well, however, requires that a number of different components be got right. Most importantly, we need an expressive and purely declarative knowledge representation language, together with a set of useful inference methods. In this paper, we present a framework for implementing this kind of software, based on a rich extension of first-order logic.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Tomas Axling and Seif Haridi. A tool for developing interactive configuration applications. Journal of Logic Programming, 19, 1994.
	2	M. Balduccini, M. Gelfond, and M. Nogueira. Answer set based design of knowledge systems. Annals of Mathematics and Artificial Intelligence, 2006.
	3	C. Baral. Knowledge representation, reasoning and declarative problem solving. Cambridge university press, 2003.
	4	Hasan Davulcu, Michael Kifer, C.R. Ramakrishnan, and I.V. Ramakrishnan. Logic based modeling and analysis of workflows. In PODS '98, 1998.
	5	Marc Denecker. Extending classical logic with inductive definitions. In NMR, 2000.
	6	Marc Denecker and Joost Vennekens. Well-founded semantics and the algebraic theory of non-monotone inductive definitions. In C. Baral, G. Brewka, and J. Schlipf, editors, Ninth International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR, volume LNAI 4483 of Lecture Notes in Artificial Intelligence, pages 84--96. Springer, 2007.
	7	M. Gelfond and V. Lifschitz. Classical negation in logic programs and disjunctive databases. New generation computing, 9:365--385, 1991.
	8	G. Knolmayer, R. Endl, and M. Pfahrer. Modeling processes and workflows by business rules. In Business Process Management, 2000.
	9	N. Leone, G. Pfeifer, W. Faber, T. Eiter, G. Gottlob, S. Perri, and F. Scarcello. The DLV system for knowledge representation and reasoning. ACM Transactions on Computational Logic, 7(3):499--562, 2006.
	10	L. Libkin. Elements of finite model theory. 2004.
	11	M. Mariën, J. Wittocx, and M. Denecker. The IDP framework for declarative problem solving. In LASH, 2006.
	12	C. Nikolopoulus. Expert Systems: Introduction to First and Second Generation and Hybrid Knowledge Based Systems. 1997.
	13	Timo Soininen and Ilkka Niemelä. Developing a declarative rule language for applications in product configuration. In PADL, 1999.
	14	S. Subbarayan, R.M. Jensen, T. Hadzic, H.R. Andersen, H. Hulgaard, and J. Møller. Comparing two implementations of a complete and backtrackfree interactive configurator. In Proc. of Workshop on CSP Techniques with Immediate Application, CP04, 2004.
	15	Wil M.P. van der Aalst and Arthur H.M. ter Hofstede. Yawl: yet another workflow language. Inf. Syst., 30(4):245--275, 2005.
	16	M. Vanden Bossche, P. Ross, I. MacLarty, B. Van Nuffelen, and N. Pelov. Ontology driven software engineering for real life applications. In SWESE, 2007.
	17	J. Wittocx, H. Vlaeminck, and M. Denecker. Debugging for model expansion. In ICLP 09, 2009.
	18	Johan Wittocx, Maarten Mariën, and Marc Denecker. Approximate reasoning in first-order logic theories. In KR, pages 103--112, 2008.