TY  - JOUR
A1  - Gebser, Martin
A1  - Kaufmann, Benjamin
A1  - Schaub, Torsten H.
T1  - Conflict-driven answer set solving: From theory to practice
JF  - Artificial intelligence
N2  - We introduce an approach to computing answer sets of logic programs, based on concepts successfully applied in Satisfiability (SAT) checking. The idea is to view inferences in Answer Set Programming (ASP) as unit propagation on nogoods. This provides us with a uniform constraint-based framework capturing diverse inferences encountered in ASP solving. Moreover, our approach allows us to apply advanced solving techniques from the area of SAT. As a result, we present the first full-fledged algorithmic framework for native conflict-driven ASP solving. Our approach is implemented in the ASP solver clasp that has demonstrated its competitiveness and versatility by winning first places at various solver contests.
KW  - Answer set programming
KW  - Logic programming
KW  - Nonmonotonic reasoning
Y1  - 2012
U6  - https://doi.org/10.1016/j.artint.2012.04.001
SN  - 0004-3702
VL  - 187
IS  - 8
SP  - 52
EP  - 89
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Videla, Santiago
A1  - Guziolowski, Carito
A1  - Eduati, Federica
A1  - Thiele, Sven
A1  - Gebser, Martin
A1  - Nicolas, Jacques
A1  - Saez-Rodriguez, Julio
A1  - Schaub, Torsten H.
A1  - Siegel, Anne
T1  - Learning Boolean logic models of signaling networks with ASP
JF  - Theoretical computer science
N2  - Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefore, automated inference of Boolean logical networks from experimental data is a fundamental question in this field. This paper addresses the problem consisting of learning from a prior knowledge network describing causal interactions and phosphorylation activities at a pseudo-steady state, Boolean logic models of immediate-early response in signaling transduction networks. The underlying optimization problem has been so far addressed through mathematical programming approaches and the use of dedicated genetic algorithms. In a recent work we have shown severe limitations of stochastic approaches in this domain and proposed to use Answer Set Programming (ASP), considering a simpler problem setting. Herein, we extend our previous work in order to consider more realistic biological conditions including numerical datasets, the presence of feedback-loops in the prior knowledge network and the necessity of multi-objective optimization. In order to cope with such extensions, we propose several discretization schemes and elaborate upon our previous ASP encoding. Towards real-world biological data, we evaluate the performance of our approach over in silico numerical datasets based on a real and large-scale prior knowledge network. The correctness of our encoding and discretization schemes are dealt with in Appendices A-B. (C) 2014 Elsevier B.V. All rights reserved.
KW  - Answer set programming
KW  - Signaling transduction networks
KW  - Boolean logic models
KW  - Combinatorial multi-objective optimization
KW  - Systems biology
Y1  - 2015
U6  - https://doi.org/10.1016/j.tcs.2014.06.022
SN  - 0304-3975
SN  - 1879-2294
VL  - 599
SP  - 79
EP  - 101
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Gebser, Martin
A1  - Kaufmann, Benjamin
A1  - Kaminski, Roland
A1  - Ostrowski, Max
A1  - Schaub, Torsten H.
A1  - Schneider, Marius
T1  - Potassco the Potsdam answer set solving collection
JF  - AI communications : AICOM ; the European journal on artificial intelligence
N2  - This paper gives an overview of the open source project Potassco, the Potsdam Answer Set Solving Collection, bundling tools for Answer Set Programming developed at the University of Potsdam.
KW  - Answer set programming
KW  - declarative problem solving
Y1  - 2011
U6  - https://doi.org/10.3233/AIC-2011-0491
SN  - 0921-7126
VL  - 24
IS  - 2
SP  - 107
EP  - 124
PB  - IOS Press
CY  - Amsterdam
ER  -