TY - GEN
T1 - Learned Constraint Ordering for Consistency Based Direct Diagnosis
AU - Polat Erdeniz, Seda
AU - Felfernig, Alexander
AU - Atas, Müslüm
PY - 2019
Y1 - 2019
N2 - Configuration systems must be able to deal with inconsistencies which can occur in different contexts. Especially in interactive settings, where users specify requirements and a constraint solver has to identify solutions, inconsistencies may more often arise. In inconsistency situations, there is a need of diagnosis methods that support the identification of minimal sets of constraints that have to be adapted or deleted in order to restore consistency. A diagnosis algorithm’s performance can be evaluated in terms of time to find a diagnosis (runtime) and diagnosis quality. Runtime efficiency of diagnosis is especially crucial in real-time scenarios such as production scheduling, robot control, and communication networks. However, there is a trade off between diagnosis quality and the runtime efficiency of diagnostic reasoning. In this paper, we deal with solving the quality-runtime performance trade off problem of direct diagnosis. In this context, we propose a novel learning approach for constraint ordering in direct diagnosis. We show that our approach improves the runtime performance and diagnosis quality at the same time.
AB - Configuration systems must be able to deal with inconsistencies which can occur in different contexts. Especially in interactive settings, where users specify requirements and a constraint solver has to identify solutions, inconsistencies may more often arise. In inconsistency situations, there is a need of diagnosis methods that support the identification of minimal sets of constraints that have to be adapted or deleted in order to restore consistency. A diagnosis algorithm’s performance can be evaluated in terms of time to find a diagnosis (runtime) and diagnosis quality. Runtime efficiency of diagnosis is especially crucial in real-time scenarios such as production scheduling, robot control, and communication networks. However, there is a trade off between diagnosis quality and the runtime efficiency of diagnostic reasoning. In this paper, we deal with solving the quality-runtime performance trade off problem of direct diagnosis. In this context, we propose a novel learning approach for constraint ordering in direct diagnosis. We show that our approach improves the runtime performance and diagnosis quality at the same time.
U2 - 10.1007/978-3-030-22999-3_31
DO - 10.1007/978-3-030-22999-3_31
M3 - Conference paper
SN - 978-3-030-22998-6
T3 - Lecture Notes in Computer Science
SP - 347
EP - 359
BT - Advances and Trends in Artificial Intelligence
PB - Springer
CY - Cham
T2 - 32nd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems
Y2 - 9 July 2019 through 11 July 2019
ER -