Controllability of Control Argumentation Frameworks

被引：0
|
作者：
Niskanen, Andreas [1 ]
Neugebauer, Daniel [2 ]
Jaervisalo, Matti [1 ]
机构：
[1] Univ Helsinki, Dept Comp Sci, HIIT, Helsinki, Finland
[2] Heinrich Heine Univ Dusseldorf, Inst Informat, Dusseldorf, Germany
来源：
PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2020年
基金：
芬兰科学院;
关键词：
SAT;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Control argumentation frameworks (CAFs) allow for modeling uncertainties inherent in various argumentative settings. We establish a complete computational complexity map of the central computational problem of controllability in CAFs for five key semantics. We also develop Boolean satisfiability based counterexample-guided abstraction refinement algorithms and direct encodings of controllability as quantified Boolean formulas, and empirically evaluate their scalability on a range of NP-hard variants of controllability.
引用
收藏
页码：1855 / 1861
页数：7
相关论文
共 50 条
  • [1] Possible Controllability of Control Argumentation Frameworks
    Mailly, Jean-Guy
    [J]. COMPUTATIONAL MODELS OF ARGUMENT (COMMA 2020), 2020, 326 : 283 - 294
  • [2] Control Argumentation Frameworks
    Dimopoulos, Yannis
    Mailly, Jean-Guy
    Moraitis, Pavlos
    [J]. THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2018, : 4678 - 4685
  • [3] Complexity of Nonemptiness in Control Argumentation Frameworks
    Neugebauer, Daniel
    Rothe, Joerg
    Skiba, Kenneth
    [J]. SYMBOLIC AND QUANTITATIVE APPROACHES TO REASONING WITH UNCERTAINTY, ECSQARU 2021, 2021, 12897 : 117 - 129
  • [4] Audiences in argumentation frameworks
    Bench-Capon, Trevor J. M.
    Doutre, Sylvie
    Dunne, Paul E.
    [J]. ARTIFICIAL INTELLIGENCE, 2007, 171 (01) : 42 - 71
  • [5] Compact Argumentation Frameworks
    Baumann, Ringo
    Dvorak, Wolfgang
    Linsbichler, Thomas
    Strass, Hannes
    Woltran, Stefan
    [J]. 21ST EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE (ECAI 2014), 2014, 263 : 69 - +
  • [6] Uniform Argumentation Frameworks
    Atkinson, Katie
    Bench-Capon, Trevor
    Dunne, Paul E.
    [J]. Computational Models of Argument, 2012, 245 : 165 - 176
  • [7] On the Aggregation of Argumentation Frameworks
    Delobelle, Jerome
    Konieczny, Sebastien
    Vesic, Srdjan
    [J]. PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), 2015, : 2911 - 2917
  • [8] Merging Argumentation Frameworks
    Leite, Lucas
    Alves, Thiago
    Alcantara, Joao
    [J]. 2015 BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS 2015), 2015, : 110 - 115
  • [9] Symmetric argumentation frameworks
    Coste-Marquis, S
    Devred, C
    Marquis, P
    [J]. SYMBOLIC AND QUANTITATIVE APPROACHES TO REASONING WITH UNCERTAINTY, PROCEEDINGS, 2005, 3571 : 317 - 328
  • [10] Ordering Argumentation Frameworks
    Sakama, Chiaki
    Inoue, Katsumi
    [J]. SYMBOLIC AND QUANTITATIVE APPROACHES TO REASONING WITH UNCERTAINTY, ECSQARU 2019, 2019, 11726 : 87 - 98
12345