Towards Constructive Abduction - Solving Abductive Problems with Constraint Programming

Antoni Ligęza

Abstract

Abduction can be considered as a principal way of reasoning for problem solving. Abductive inference consists in generation of hypotheses which explain — or logically imply — the phenomenon under investigation in view of accessible background knowledge and are consistent with all other observations. Looking for such hypotheses is typically performed with a spectrum of trial-and-error or search methods and tools. In case of purely logical statements the hypotheses take the form of a set of facts, both positive and negative ones. For example, in case of model based diagnostic reasoning, such diagnostic hypotheses can be generated by consistency based reasoning with minimal search effort. In more complex cases, where values of certain variables are to be found, pure backtracking search becomes inefficient. In this paper we attempt to put forward such abductive inference into a formal framework of Constraint Programming in order to enable the use of constraint propagation techniques. The main idea behind this approach is to make abduction more constructive. The discussion is illustrated with a diagnostic example of a multiplier-adder system.

References

  1. Cordier, M.-O. and et al. (2000a). Ai and automatic control approaches of model-based diagnosis: Links and underlying hypotheses. In Edelmayer, A. M., editor, Preprints: SAFEPROCESS 2000, 4th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, pages 274-279. IFAC.
  2. Cordier, M.-O. and et al. (2000b). A comparative analysis of ai and control theory approaches to model-based diagnosis. In Horn, W., editor, ECAI'2000. 14th European Conference on Artificial Intelligence , pages 136-140. IOS Press.
  3. Davis, R. and Hamscher, W. (1992). Model-Based Reasoning: Troubleshooting, pages 3-24. Morgan Kaufmann Publishers, San Mateo, CA.
  4. Dechter, R. (2003). Constraint Processing. Elsevier Science.
  5. Hamscher, W., Console, L., and de Kleer, J., editors (1992). Readings in Model-Based Diagnosis. Morgan Kaufmann, San Mateo, CA.
  6. Korbicz, J., Koscielny, J., Kowalczuk, Z., and Cholewa, W., editors (2004). Fault Diagnosis. Models, Artificial Intelligence, Applications. Springer-Verlag, Berlin.
  7. Lige?za, A. (2004). Selected Methods of Knowledge Engineering in System Diagnosis, chapter 16, pages 633- 668. In: (Korbicz et al., 2004). Springer-Verlag.
  8. Lige?za, A. (2006). Logical Foundations for Rule-Based Systems. Springer-Verlag, Berlin, Heidelberg.
  9. Lige?za, A. (2009). A Constraint Satisfaction Framework for Diagnostic Problems, pages 255-262. Control and Computer Science. Information Technology, Control Theory, Fault and System Diagnosis. Pomeranian Science and Technology Publisher PWNT, GdaÁsk.
  10. Lige?za, A. and Fuster-Parra, P. (1997). And/or/not causal graphs - a model for diagnostic reasoning. Applied Mathematics and Computer Science, Vol. 7, No. 1:185-203.
  11. Lige?za, A. and Koscielny, J. M. (2008). A new approach to multiple fault diagnosis. combination of diagnostic matrices, graphs, algebraic and rule-based models. the case of two-layer models. Int. J. Appl. Math. Comput. Sci., 18(4):465-476.
  12. Reiter, R. (1987). A theory of diagnosis from first principles. Artificial Intelligence , 32:57-95.
  13. Travé-Massuyès, L. (2014). Bridges between diagnosis theories from control and ai perspectives. In Korbicz, J. and Kowal, M., editors, Intelligent Systems in Technical and medila Diagnosis, pages 3-28. SpringerVerlag.
Download


Paper Citation


in Harvard Style

Ligęza A. (2015). Towards Constructive Abduction - Solving Abductive Problems with Constraint Programming . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015) ISBN 978-989-758-158-8, pages 352-357. DOI: 10.5220/0005625603520357


in Bibtex Style

@conference{keod15,
author={Antoni Ligęza},
title={Towards Constructive Abduction - Solving Abductive Problems with Constraint Programming},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015)},
year={2015},
pages={352-357},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005625603520357},
isbn={978-989-758-158-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015)
TI - Towards Constructive Abduction - Solving Abductive Problems with Constraint Programming
SN - 978-989-758-158-8
AU - Ligęza A.
PY - 2015
SP - 352
EP - 357
DO - 10.5220/0005625603520357