Mixed Constraint Satisfaction: A Framework for Decision Problems Under Incomplete Knowledge (1996)
| Venue: | In Proc. of the 13th National Conference on Artificial Intelligence (AAAI-96 |
| Citations: | 31 - 3 self |
BibTeX
@INPROCEEDINGS{Fargier96mixedconstraint,
author = {Helene Fargier and Jerome Lang and Jero Me Lang and Thomas Schiex},
title = {Mixed Constraint Satisfaction: A Framework for Decision Problems Under Incomplete Knowledge},
booktitle = {In Proc. of the 13th National Conference on Artificial Intelligence (AAAI-96},
year = {1996},
pages = {175--180}
}
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Abstract
Constraint satisfaction is a powerful tool for representing and solving decision problems with complete knowledge about the world. We extend the CSP framework so as to represent decision problems under incomplete knowledge. The basis of the extension consists in a distinction between controllable and uncontrollable variables --- hence the terminology "mixed CSP" --- and a "solution" gives actually a conditional decision. We study the complexity of deciding the consistency of a mixed CSP. As the problem is generally intractable, we propose an algorithm for finding an approximate solution.
Citations
| 918 | Consistency in networks of relations - Mackworth - 1977 |
| 792 | Symbolic boolean manipulation with ordered binary decision diagrams - Bryant - 1992 |
| 3 | Influence Diagrams, Influence Diagrams - Howard, Matheson - 1984 |
| 1 | Helene Fargier, Jero me Lang, Roger Martin-Clouaire and Thomas Schiex, A constraint satisfaction framework for decision under uncertainty - IRIT-96-06, Sabatier - 1996 |
