Solving and Learning Soft Temporal Constraints; Ceteris Paribus Statements Represented as Soft Constraints Problems

Authors:
Kristen B. Venable
Affiliations:
-
Venue:
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Year:
2002

Citing 3
Cited 0

Semiring-based constraint satisfaction and optimization

Journal of the ACM (JACM)
Learning and Solving Soft Temporal Constraints: An Experimental Study

CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Reasoning with conditional ceteris paribus preference statements

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Soft temporal constraints problems (TCSPs) allow to describe in a natural way scenarios where events happen over time and preferences are associated to event distances and durations. However, sometimes such local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. The Constraint Satisfaction framework combined with Machine learning techniques can be useful in this respect. Soft constraints are useful in general for manipulating preferences. In particular it is possible to approximate CP nets, a graphical representation of ceteris paribus conditional preference statements, with semiring based soft constraints problems.