Using cost distributions to guide weight decay in local search for SAT

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5351 LNAI, Issue , 2008, Pages 405-416

  Thornton, John ^a   Pham, Duc Nghia ^a  

^a GRIFFITH UNIVERSITY   (Australia)

Author keywords

Automated parameter tuning; Clause weighting; Local search; Satisfiability

Indexed keywords

ARTIFICIAL INTELLIGENCE; AUTOMATION; BIONICS; COSTS; IMAGE SEGMENTATION; LEARNING ALGORITHMS; TUNING; WEIGHING;

AUTOMATED APPROACHES; AUTOMATED PARAMETER TUNING; CLAUSE WEIGHTING; COST DISTRIBUTIONS; DEFAULT SETTINGS; EMPIRICAL STUDIES; FUTURE PERFORMANCES; LOCAL SEARCH; LOCAL SEARCH ALGORITHMS; LOCAL SEARCH TECHNIQUES; PARAMETER SETTINGS; SATISFIABILITY; SELF-TUNING; SENSITIVE PARAMETERS; STATISTICAL PROPERTIES; TWO STATES; WEIGHT DECAYS;

PARAMETER ESTIMATION;

EID: 58349106771     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-89197-0_38     Document Type: Conference Paper

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