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Soft constraints for KnowLang

Published: 27 June 2012 Publication History

Abstract

Constraints are widely used in information technologies and research fields such as programming languages, artificial intelligence, databases, information security, web technologies, etc. In this paper, we present our preliminary steps of using soft constraints for knowledge representation. We integrate soft constraints in KnowLang, a formal language for knowledge representation in self-adaptive systems. KnowLang allows for efficient and comprehensive knowledge structuring where ontologies are integrated with rules and Bayesian networks. The approach targets at a technique where knowledge can be represented as special restrictive rules that may require full or partial satisfaction, i.e., restrictions are represented as some sort of good-to-have properties.

References

[1]
ASCENS. ASCENS - Autonomic Service-Component Ensembles, 2010. http://www.ascens-ist.eu/, last viewed April 2012.
[2]
A. Biso, F. Rossi, and A. Sperduti. Experimental results on learning soft constraints. In Proceedings of KR'2000, pages 435--444. SRI International, Menlo Park, California, USA, 2000.
[3]
S. Bistarelli, U. Montanari, and F. Rossi. Semiring-based constraint satisfaction and optimization. Journal of the ACM, 44(2):201--236, 1997.
[4]
S. Bistarelli, U. Montanari, and F. Rossi. Semiring-based constraint logic programming: Syntax and semantics. ACM Trans. Program. Lang. Syst., 23(1):1--29, 2001.
[5]
S. Bistarelli, U. Montanari, and F. Rossi. Soft concurrent constraint programming. ACM Trans. Comput. Log., 7(3):563--589, 2006.
[6]
M. Bonani, V. Longchamp, S. Magnenat, P. Retornaz, D. Burnier, G. Roulet, F. Vaussard, H. Bleuler, and F. Mondada. The marxbot, a miniature mobile robot opening new perspectives for the collective-robotic research. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010). IEEE/RSJ, 2010.
[7]
M. G. Buscemi and U. Montanari. Cc-pi: A constraint-based language for specifying service level agreements. In R. D. Nicola, editor, ESOP 2007, LNCS 4421, pages 18--32. Springer, 2007.
[8]
European Commission - CORDIS. Seventh Framework Program (FP7), 2012. http://cordis.europa.eu/fp7/home_en.html, last viewed April 2012.
[9]
U. Montanari. Networks of constraints: Fundamental properties and application to picture processing. Information Science, (7):95--132, 1974.
[10]
R. Neapolitan. Learning Bayesian Networks. Prentice Hall, 2003.
[11]
E. Vassev and M. Hinchey. Knowledge representation and reasoning for intelligent software systems. IEEE Computer, 44(8):96--99, 2011.
[12]
E. Vassev and M. Hinchey. Towards a formal language for knowledge representation in autonomic service-component ensembles. In Proceedings of the 3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMiA 2011), pages 228--235. NASNIT, ICMIA, IEEE, 2011.
[13]
E. Vassev and M. Hinchey. Knowledge representation for cognitive robotic systems. In Proceedings of the 15th IEEE International Symposium on Object/Component/Service-oriented Real-time Distributed Computing Workshops (ISCORCW 2012), pages 156--163. IEEE Computer Society, 2012.

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cover image ACM Other conferences
C3S2E '12: Proceedings of the Fifth International C* Conference on Computer Science and Software Engineering
June 2012
139 pages
ISBN:9781450310840
DOI:10.1145/2347583
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • University of Limerick: University of Limerick
  • BytePress
  • Concordia University: Concordia University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 June 2012

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Author Tags

  1. KnowLang
  2. knowledge representation
  3. soft-constraints

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  • Short-paper

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C3S2E '12
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  • University of Limerick
  • Concordia University

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Overall Acceptance Rate 12 of 42 submissions, 29%

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