Reinforcement Learning and Reactive Search: an adaptive MAX-SAT solver

被引：1
|
作者：
Battiti, Roberto [1 ]
Campigotto, Paolo [1 ]
机构：
[1] Univ Trento, Dipartimento Ingn & Sci Informaz, Trento, Italy
来源：
ECAI 2008, PROCEEDINGS | 2008年 / 178卷
关键词：
D O I：
10.3233/978-1-58603-891-5-909
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
引用
收藏
页码：909 / +
页数：2
相关论文
共 50 条
  • [41] Report on SAT competition and Max-SAT evaluation
    Nabeshima, Hidetomo
    Koshimura, Miyuki
    Banbara, Mutsunori
    Computer Software, 2012, 29 (04) : 9 - 14
  • [42] Learning Nobetter Clauses in Max-SAT Branch and Bound Solvers
    Abrame, Andre
    Habet, Djamal
    2016 IEEE 28TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2016), 2016, : 452 - 459
  • [43] A Stochastic Local Search Algorithm for the Partial Max-SAT Problem Based on Adaptive Tuning and Variable Depth Neighborhood Search
    Alkasem, Haifa Hamad
    Menai, Mohamed El Bachir
    IEEE ACCESS, 2021, 9 (09): : 49806 - 49843
  • [44] On the Relative Merits of Simple Local Search Methods for the MAX-SAT Problem
    Pankratov, Denis
    Borodin, Allan
    THEORY AND APPLICATIONS OF SATISFIABILITY TESTING - SAT 2010, PROCEEDINGS, 2010, 6175 : 223 - +
  • [45] Scaling and probabilistic smoothing: Dynamic local search for unweighted MAX-SAT
    Tompkins, DAD
    Hoos, HH
    ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2003, 2671 : 145 - 159
  • [46] Learning MAX-SAT from contextual examples for combinatorial optimisation
    Kumar, Mohit
    Kolb, Samuel
    Teso, Stefano
    De Raedt, Luc
    ARTIFICIAL INTELLIGENCE, 2023, 314
  • [47] Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation
    Kumar, Mohit
    Kolb, Samuel
    Teso, Stefano
    De Raedt, Luc
    THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2020, 34 : 4493 - 4500
  • [48] Three Truth Values for the SAT and MAX-SAT Problems
    Lardeux, Frederic
    Saubion, Frederic
    Hao, Jin-Kao
    19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05), 2005, : 187 - 192
  • [49] Search space features underlying the performance of stochastic local search algorithms for MAX-SAT
    Hoos, HH
    Smyth, K
    Stützle, T
    PARALLEL PROBLEM SOLVING FROM NATURE - PPSN VIII, 2004, 3242 : 51 - 60
  • [50] Discrete Lagrangian-based search for solving MAX-SAT problems
    Wah, BW
    Shang, Y
    IJCAI-97 - PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2, 1997, : 378 - 383
12345