A Method to Avoid Duplicative Flipping in Local Search for SAT

Paper


Duong, Thach-Thao, Pham, Duc Nghia and Sattar, Abdul. 2012. "A Method to Avoid Duplicative Flipping in Local Search for SAT." Thielscher, Michael and Zhang, Dongmo (ed.) 25th Australasian Joint Conference on Artificial Intelligence (AI 2012). Sydney, Australia 04 - 07 Dec 2012 Berlin. https://doi.org/10.1007/978-3-642-35101-3_19
Paper/Presentation Title

A Method to Avoid Duplicative Flipping in Local Search for SAT

Presentation TypePaper
AuthorsDuong, Thach-Thao (Author), Pham, Duc Nghia (Author) and Sattar, Abdul (Author)
EditorsThielscher, Michael and Zhang, Dongmo
Journal or Proceedings TitleLecture Notes in Artificial Intelligence (Book series)
ERA Conference ID42498
Journal Citation7691, pp. 218-229
Number of Pages12
Year2012
Place of PublicationBerlin
ISBN9783642351006
9783642351013
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-642-35101-3_19
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007/978-3-642-35101-3_19
Conference/Event25th Australasian Joint Conference on Artificial Intelligence (AI 2012)
Australasian Joint Conference on Artificial Intelligence
Event Details
Australasian Joint Conference on Artificial Intelligence
AI
Rank
B
B
B
B
B
B
B
Event Details
25th Australasian Joint Conference on Artificial Intelligence (AI 2012)
Event Date
04 to end of 07 Dec 2012
Event Location
Sydney, Australia
Abstract

Stochastic perturbation on variable flipping is the key idea of local search for SAT. Observing that variables are flipped several times in an attempt to escape from a local minimum, this paper presents a duplication learning mechanism in stagnation stages to minimise duplicative variable flipping. The heuristic incorporates the learned knowledge into a variable weighting scheme to effectively prevent the search from selecting duplicative variables. Additionally, probability-based and time window smoothing techniques are adopted to eliminate the effects of redundant information. The integration of the heuristic and gNovelty +  was compared with the original solvers and other state-of-the-art local search solvers. The experimental results showed that the new solver outperformed other solvers on the full set of SAT 2011 competition instances and three sets of real-world verification problems.

KeywordsLearning mechanism; Local minimums; Local search; Redundant informations; Smoothing techniques; Stochastic perturbations; Time windows; Variable weighting; Verification problems
ANZSRC Field of Research 2020460210. Satisfiability and optimisation
Byline AffiliationsGriffith University
Institution of OriginUniversity of Southern Queensland
Permalink -

https://research.usq.edu.au/item/q7119/a-method-to-avoid-duplicative-flipping-in-local-search-for-sat

  • 56
    total views
  • 2
    total downloads
  • 1
    views this month
  • 0
    downloads this month

Export as

Related outputs

Moving Objects Segmentation in Video Sequence based on Bayesian network
Duong, Thach-Thao and Duong, Anh-Duc. 2010. "Moving Objects Segmentation in Video Sequence based on Bayesian network." 8th IEEE-RIVF International Conference on Computing and Communication Technologies: Research, Innovation and Vision for the Future (RIVF 2010). Hanoi, Vietnam 01 - 04 Nov 2010 https://doi.org/10.1109/RIVF.2010.5633458
Trap Avoidance in Local Search Using Pseudo-Conflict Learning
Pham, Duc Nghia, Duong, Thach-Thao and Sattar, Abdul. 2012. "Trap Avoidance in Local Search Using Pseudo-Conflict Learning." 26th AAAI Conference on Artificial Intelligence (AAAI 2012). Toronto, Canada 22 - 26 Jul 2012 United States.
Weight-Enhanced Diversification in Stochastic Local Search for Satisfiability
Duong, Thach-Thao, Pham, Duc Nghia, Sattar, Abdul and Newton, M. A. Hakim. 2013. "Weight-Enhanced Diversification in Stochastic Local Search for Satisfiability." Rossi, Francesca (ed.) 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013). Beijing, China 03 - 09 Aug 2013
Integrated data envelopment analysis: Linear vs. nonlinear model
Mahdiloo, Mahdi, Toloo, Mehdi, Duong, Thach-Thao, Saen, Reza Farzipoor and Tatham, Peter. 2018. "Integrated data envelopment analysis: Linear vs. nonlinear model." European Journal of Operational Research. 268 (1), pp. 255-267. https://doi.org/10.1016/j.ejor.2018.01.008
Some comments on improving discriminating power in data envelopment models based on deviation variables framework
Mahdiloo, Mahdi, Lim, Sungmook, Duong, Thach-Thao and Harvie, Charles. 2021. "Some comments on improving discriminating power in data envelopment models based on deviation variables framework." European Journal of Operational Research. 295 (1), pp. 394-397. https://doi.org/10.1016/j.ejor.2021.02.056
Trap escape for local search by backtracking and conflict reverse
Duong, Huu-Phuoc, Duong, Thach-Thao, Pham, Duc Nghia, Sattar, Abdul and Duong, Anh Duc. 2013. "Trap escape for local search by backtracking and conflict reverse." Jaeger, Manfred, Nielsen, Thomas Dyhre and Viappiani, Paolo (ed.) 12th Scandinavian Conference on Artificial Intelligence (SCAI 2013). Aalborg, Denmark 20 - 22 Nov 2013 Amsterdam. https://doi.org/10.3233/978-1-61499-330-8-85
Diversify Intensification Phases in Local Search for SAT with a New Probability Distribution
Duong, Thach-Thao, Pham, Duc-Nghia and Sattar, Abdul. 2013. "Diversify Intensification Phases in Local Search for SAT with a New Probability Distribution." Cranefield, Stephen and Nayak, Abhaya (ed.) 26th Australasian Joint Conference on Artificial Intelligence (AI 2013). Dunedin, New Zealand 01 - 06 Dec 2013 Switzerland. https://doi.org/10.1007/978-3-319-03680-9_18
A Study of Local Minimum Avoidance Heuristics for SAT
Duong, Thach-Thao, Pham, Duc Nghia and Sattar, Abdul. 2012. "A Study of Local Minimum Avoidance Heuristics for SAT." De Raedt, Luc, Bessiere, Christian, Dubois, Didier, Doherty, Patrick, Frasconi, Paolo, Heintz, Fredrik and Lucas, Peter (ed.) 20th European Conference on Artificial Intelligence (ECAI 2012). Montpellier, France 27 - 31 Aug 2012 Netherlands. https://doi.org/10.3233/978-1-61499-098-7-300
Image retrieval based on visual information concepts and automatic image annotation
Ly, Quoc Ngoc, Duong, Anh Doc, Duong, Thach Thao and Ngo, Duc Thanh. 2006. "Image retrieval based on visual information concepts and automatic image annotation." Duc, Duong Anh, Dong, Thuy Thi Bich, Ho, Tu-Bao and Nguyen, Dinh Thuc (ed.) 1st International Conference on Theories and Applications of Computer Science (ICTACS 2006). Ho Chi Minh City, Vietnam 03 - 05 Aug 2006 United States. https://doi.org/10.1142/9789812772671_0006