M. Alviano, C. Dodaro, W. Faber, N. Leone & F. Ricca (2013):
WASP: A Native ASP Solver Based on Constraint Learning.
In: Pedro Cabalar & Tran Cao Son: Proceedings of the 12th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR-2013,
LNCS 8148.
Springer,
pp. 54–66,
doi:10.1007/978-3-642-40564-8_6.
M. Bichler, M. Morak & S. Woltran (2016):
lpopt: A Rule Optimization Tool for Answer Set Programming.
In: Manuel V. Hermenegildo & Pedro López-García: Proceedings of the 26th International Symposium on Logic-Based Program Synthesis and Transformation, LOPSTR-2016,
LNCS 10184.
Springer,
pp. 114–130,
doi:10.1007/978-3-319-63139-4_7.
G. Brewka, T. Eiter & M. Truszczynski (2011):
Answer set programming at a glance.
Commun. ACM 54(12),
pp. 92–103,
doi:10.1145/2043174.2043195.
M. Buddenhagen & Y. Lierler (2015):
Performance Tuning in Answer Set Programming.
In: Francesco Calimeri, Giovambattista Ianni & Miroslaw Truszczynski: Proceedings of the 13th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR-2015,
LNCS 9345.
Springer,
pp. 186–198,
doi:10.1007/978-3-319-23264-5_17.
M. Denecker, J. Vennekens, S. Bond, M. Gebser & M. Truszczynski (2009):
The Second Answer Set Programming Competition.
LPNMR 2009,
pp. 637–654,
doi:10.1007/978-3-642-04238-6_75.
E. Erdem, M. Gelfond & N. Leone (2016):
Applications of Answer Set Programming.
AI Magazine 37(3),
pp. 53–68,
doi:10.1609/aimag.v37i3.2678.
M. Gebser, R. Kaminski, B. Kaufmann & T. Schaub (2012):
Answer Set Solving in Practice.
Synthesis Lectures on Artificial Intelligence and Machine Learning.
Morgan&Claypool Publishers,
doi:10.2200/S00457ED1V01Y201211AIM019.
M. Gebser, L. Liu, G. Namasivayam, A. Neumann, T. Schaub & M. Truszczynski (2007):
The First Answer Set Programming System Competition.
LPNMR 2007,
pp. 3–17,
doi:10.1007/978-3-540-72200-7_3.
M. Gebser, M. Maratea & F. Ricca (2017):
The Design of the Seventh Answer Set Programming Competition.
In: Marcello Balduccini & Tomi Janhunen: Proceedings of the 14th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR-2017,
LNCS 10377.
Springer,
pp. 3–9,
doi:10.1007/978-3-319-61660-5_1.
C. P. Gomes & B. Selman (2001):
Algorithm portfolios.
Artif. Intell. 126(1-2),
pp. 43–62,
doi:10.1016/S0004-3702(00)00081-3.
N. Hippen & Y. Lierler (2019):
Automatic Program Rewriting in Non-Ground Answer Set Programs.
In: José Júlio Alferes & Moa Johansson: Proceedings of PADL 2019,
LNCS 11372.
Springer,
pp. 19–36,
doi:10.1007/978-3-030-05998-9_2.
H. H. Hoos, M. T. Lindauer & T. Schaub (2014):
claspfolio 2: Advances in Algorithm Selection for Answer Set Programming.
Theory and Practice of Logic Programming 14(4-5),
pp. 569–585,
doi:10.1017/S1471068414000210.
F. Hutter, H. H. Hoos, K. Leyton-Brown & T. Stützle (2009):
ParamILS: An Automatic Algorithm Configuration Framework.
J. Artif. Intell. Res. 36,
pp. 267–306,
doi:10.1613/jair.2861.
P. Kerschke, H. H. Hoos, F. Neumann & H. Trautmann (2019):
Automated Algorithm Selection: Survey and Perspectives.
Evolutionary Computation 27(1),
pp. 3–45,
doi:10.1162/evco_a_00242.
N. Leone, G. Pfeifer, W. Faber, T. Eiter, G. Gottlob, S. Perri & F. Scarcello (2006):
The DLV system for knowledge representation and reasoning.
ACM Trans. Comput. Log. 7(3),
pp. 499–562,
doi:10.1145/1149114.1149117.
M. Maratea, L. Pulina & F. Ricca (2014):
A Multi-engine Approach to Answer-set Programming.
Theory and Practice of Logic Programming 14(6),
pp. 841–868,
doi:10.1017/S1471068413000094.
J. R. Rice (1976):
The Algorithm Selection Problem.
Advances in Computers 15,
pp. 65–118,
doi:10.1016/S0065-2458(08)60520-3.
B. Selman, D. G. Mitchell & H. J. Levesque (1996):
Generating Hard Satisfiability Problems.
Artif. Intell. 81(1-2),
pp. 17–29,
doi:10.1016/0004-3702(95)00045-3.
L. Xu, F. Hutter, H. H. Hoos & K. Leyton-Brown (2008):
SATzilla: Portfolio-based Algorithm Selection for SAT.
J. Artif. Intell. Res. 32,
pp. 565–606,
doi:10.1613/jair.2490.
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