Trap Avoidance in Local Search Using Pseudo-Conflict Learning

Paper


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.
Paper/Presentation Title

Trap Avoidance in Local Search Using Pseudo-Conflict Learning

Presentation TypePaper
AuthorsPham, Duc Nghia (Author), Duong, Thach-Thao (Author) and Sattar, Abdul (Author)
Journal or Proceedings TitleProceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI 2012)
ERA Conference ID43933
Journal Citation242, pp. 542-548
Number of Pages7
Year2012
Place of PublicationUnited States
ISBN9781577355687
Web Address (URL) of Paperhttps://dl.acm.org/doi/abs/10.5555/2900728.2900806
Conference/Event26th AAAI Conference on Artificial Intelligence (AAAI 2012)
AAAI Conference on Artificial Intelligence
Event Details
AAAI Conference on Artificial Intelligence
AAAI
Rank
A
A
A
A
A
Event Details
26th AAAI Conference on Artificial Intelligence (AAAI 2012)
Event Date
22 to end of 26 Jul 2012
Event Location
Toronto, Canada
Abstract

A key challenge in developing efficient local search solvers is to effectively minimise search stagnation (i.e. avoiding traps or local minima). A majority of the state-of-the-art local search solvers perform random and/or Novelty-based walks to overcome search stagnation. Although such strategies are effective in diversifying a search from its current local minimum, they do not actively prevent the search from visiting previously encountered local minima. In this paper, we propose a new preventative strategy to effectively minimise search stagnation using pseudo-conflict learning. We define a pseudo-conflict as a derived path from the search trajectory that leads to a local minimum. We then introduce a new variable selection scheme that penalises variables causing those pseudo-conflicts. Our experimental results show that the new preventative approach significantly improves the performance of local search solvers on a wide range of structured and random benchmarks.

KeywordsLocal minimums; Local search; Trap avoidances; Variable selection
ANZSRC Field of Research 2020460210. Satisfiability and optimisation
Byline AffiliationsGriffith University
Institution of OriginUniversity of Southern Queensland
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https://research.usq.edu.au/item/q711z/trap-avoidance-in-local-search-using-pseudo-conflict-learning

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