Diversify Intensification Phases in Local Search for SAT with a New Probability Distribution

Paper


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
Paper/Presentation Title

Diversify Intensification Phases in Local Search for SAT with a New Probability Distribution

Presentation TypePaper
AuthorsDuong, Thach-Thao (Author), Pham, Duc-Nghia (Author) and Sattar, Abdul (Author)
EditorsCranefield, Stephen and Nayak, Abhaya
Journal or Proceedings TitleLecture Notes in Artificial Intelligence (Book series)
ERA Conference ID42498
Journal Citation8272, pp. 166-177
Number of Pages12
Year2013
Place of PublicationSwitzerland
ISBN9783319036793
9783319036809
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-319-03680-9_18
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007/978-3-319-03680-9_18
Conference/Event26th Australasian Joint Conference on Artificial Intelligence (AI 2013)
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
26th Australasian Joint Conference on Artificial Intelligence (AI 2013)
Event Date
01 to end of 06 Dec 2013
Event Location
Dunedin, New Zealand
Abstract

A key challenge in developing efficient local search solvers is to intelligently balance diversification and intensification. This study proposes a heuristic that integrates a new dynamic scoring function and two different diversification criteria: variable weights and stagnation weights. Our new dynamic scoring function is formulated to enhance the diversification capability in intensification phases using a user-defined diversification parameter. The formulation of the new scoring function is based on a probability distribution to adjust the selecting priorities of the selection between greediness on scores and diversification on variable properties. The probability distribution of variables on greediness is constructed to guarantee the synchronization between the probability distribution functions and score values. Additionally, the new dynamic scoring function is integrated with the two diversification criteria. The experiments show that the new heuristic is efficient on verification benchmark, crafted and random instances.

KeywordsArtificial intelligence; Distribution functions
ANZSRC Field of Research 2020460210. Satisfiability and optimisation
Byline AffiliationsGriffith University
Institution of OriginUniversity of Southern Queensland
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