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 - Lindauer, Marius A1 - Hoos, Holger H. A1 - Hutter, Frank A1 - Schaub, Torsten H. T1 - An automatically configured algorithm selector JF - The journal of artificial intelligence research N2 - Algorithm selection (AS) techniques - which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently - have substantially improved the state of the art in solving many prominent AI problems, such as SAT, CSP, ASP, MAXSAT and QBF. Although several AS procedures have been introduced, not too surprisingly, none of them dominates all others across all AS scenarios. Furthermore, these procedures have parameters whose optimal values vary across AS scenarios. This holds specifically for the machine learning techniques that form the core of current AS procedures, and for their hyperparameters. Therefore, to successfully apply AS to new problems, algorithms and benchmark sets, two questions need to be answered: (i) how to select an AS approach and (ii) how to set its parameters effectively. We address both of these problems simultaneously by using automated algorithm configuration. Specifically, we demonstrate that we can automatically configure claspfolio 2, which implements a large variety of different AS approaches and their respective parameters in a single, highly-parameterized algorithm framework. Our approach, dubbed AutoFolio, allows researchers and practitioners across a broad range of applications to exploit the combined power of many different AS methods. We demonstrate AutoFolio can significantly improve the performance of claspfolio 2 on 8 out of the 13 scenarios from the Algorithm Selection Library, leads to new state-of-the-art algorithm selectors for 7 of these scenarios, and matches state-of-the-art performance (statistically) on all other scenarios. Compared to the best single algorithm for each AS scenario, AutoFolio achieves average speedup factors between 1.3 and 15.4. Y1 - 2015 SN - 1076-9757 SN - 1943-5037 VL - 53 SP - 745 EP - 778 PB - AI Access Foundation CY - Marina del Rey ER - TY - JOUR A1 - Hoos, Holger A1 - Kaminski, Roland A1 - Lindauer, Marius A1 - Schaub, Torsten H. T1 - aspeed: Solver scheduling via answer set programming JF - Theory and practice of logic programming N2 - Although Boolean Constraint Technology has made tremendous progress over the last decade, the efficacy of state-of-the-art solvers is known to vary considerably across different types of problem instances, and is known to depend strongly on algorithm parameters. This problem was addressed by means of a simple, yet effective approach using handmade, uniform, and unordered schedules of multiple solvers in ppfolio, which showed very impressive performance in the 2011 Satisfiability Testing (SAT) Competition. Inspired by this, we take advantage of the modeling and solving capacities of Answer Set Programming (ASP) to automatically determine more refined, that is, nonuniform and ordered solver schedules from the existing benchmarking data. We begin by formulating the determination of such schedules as multi-criteria optimization problems and provide corresponding ASP encodings. The resulting encodings are easily customizable for different settings, and the computation of optimum schedules can mostly be done in the blink of an eye, even when dealing with large runtime data sets stemming from many solvers on hundreds to thousands of instances. Also, the fact that our approach can be customized easily enabled us to swiftly adapt it to generate parallel schedules for multi-processor machines. KW - algorithm schedules KW - answer set programming KW - portfolio-based solving Y1 - 2015 U6 - https://doi.org/10.1017/S1471068414000015 SN - 1471-0684 SN - 1475-3081 VL - 15 SP - 117 EP - 142 PB - Cambridge Univ. Press CY - New York ER -