Weight-Based Variable Ordering in the Context of High-Level Consistencies
November 02, 2017 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Robert J. Woodward, Berthe Y. Choueiry
arXiv ID
1711.00909
Category
cs.AI: Artificial Intelligence
Citations
3
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Dom/wdeg is one of the best performing heuristics for dynamic variable ordering in backtrack search [Boussemart et al., 2004]. As originally defined, this heuristic increments the weight of the constraint that causes a domain wipeout (i.e., a dead-end) when enforcing arc consistency during search. "The process of weighting constraints with dom/wdeg is not defined when more than one constraint lead to a domain wipeout [Vion et al., 2011]." In this paper, we investigate how weights should be updated in the context of two high-level consistencies, namely, singleton (POAC) and relational consistencies (RNIC). We propose, analyze, and empirically evaluate several strategies for updating the weights. We statistically compare the proposed strategies and conclude with our recommendations.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Artificial Intelligence
π
π
The Cartographer
R.I.P.
π»
Ghosted
Explanation in Artificial Intelligence: Insights from the Social Sciences
R.I.P.
π»
Ghosted
Federated Machine Learning: Concept and Applications
R.I.P.
π»
Ghosted
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
R.I.P.
π»
Ghosted
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
R.I.P.
π»
Ghosted
Rainbow: Combining Improvements in Deep Reinforcement Learning
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted