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 - 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 - 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 - Hoos, Holger A1 - Lindauer, Marius A1 - Schaub, Torsten H. T1 - claspfolio 2 BT - advances in algorithm selection for answer set programming JF - Theory and practice of logic programming N2 - Building on the award-winning, portfolio-based ASP solver claspfolio, we present claspfolio 2, a modular and open solver architecture that integrates several different portfolio-based algorithm selection approaches and techniques. The claspfolio 2 solver framework supports various feature generators, solver selection approaches, solver portfolios, as well as solver-schedule-based pre-solving techniques. The default configuration of claspfolio 2 relies on a light-weight version of the ASP solver clasp to generate static and dynamic instance features. The flexible open design of claspfolio 2 is a distinguishing factor even beyond ASP. As such, it provides a unique framework for comparing and combining existing portfolio-based algorithm selection approaches and techniques in a single, unified framework. Taking advantage of this, we conducted an extensive experimental study to assess the impact of different feature sets, selection approaches and base solver portfolios. In addition to gaining substantial insights into the utility of the various approaches and techniques, we identified a default configuration of claspfolio 2 that achieves substantial performance gains not only over clasp's default configuration and the earlier version of claspfolio, but also over manually tuned configurations of clasp. Y1 - 2014 U6 - https://doi.org/10.1017/S1471068414000210 SN - 1471-0684 SN - 1475-3081 VL - 14 SP - 569 EP - 585 PB - Cambridge Univ. Press CY - New York ER - TY - JOUR A1 - Gebser, Martin A1 - Sabuncu, Orkunt A1 - Schaub, Torsten H. T1 - An incremental answer set programming based system for finite model computation JF - AI communications : AICOM ; the European journal on artificial intelligence N2 - We address the problem of Finite Model Computation (FMC) of first-order theories and show that FMC can efficiently and transparently be solved by taking advantage of a recent extension of Answer Set Programming (ASP), called incremental Answer Set Programming (iASP). The idea is to use the incremental parameter in iASP programs to account for the domain size of a model. The FMC problem is then successively addressed for increasing domain sizes until an answer set, representing a finite model of the original first-order theory, is found. We implemented a system based on the iASP solver iClingo and demonstrate its competitiveness by showing that it slightly outperforms the winner of the FNT division of CADE's 2009 Automated Theorem Proving (ATP) competition on the respective benchmark collection. KW - Incremental answer set programming KW - finite model computation Y1 - 2011 U6 - https://doi.org/10.3233/AIC-2011-0496 SN - 0921-7126 VL - 24 IS - 2 SP - 195 EP - 212 PB - IOS Press 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 - TY - JOUR A1 - Gebser, Martin A1 - Schaub, Torsten H. A1 - Thiele, Sven A1 - Veber, Philippe T1 - Detecting inconsistencies in large biological networks with answer set programming JF - Theory and practice of logic programming N2 - We introduce an approach to detecting inconsistencies in large biological networks by using answer set programming. To this end, we build upon a recently proposed notion of consistency between biochemical/genetic reactions and high-throughput profiles of cell activity. We then present an approach based on answer set programming to check the consistency of large-scale data sets. Moreover, we extend this methodology to provide explanations for inconsistencies by determining minimal representations of conflicts. In practice, this can be used to identify unreliable data or to indicate missing reactions. KW - answer set programming KW - bioinformatics KW - consistency KW - diagnosis Y1 - 2011 U6 - https://doi.org/10.1017/S1471068410000554 SN - 1471-0684 VL - 11 IS - 5-6 SP - 323 EP - 360 PB - Cambridge Univ. Press CY - New York ER - TY - JOUR A1 - Gebser, Martin A1 - Kaminski, Roland A1 - Schaub, Torsten H. T1 - Complex optimization in answer set programming JF - Theory and practice of logic programming N2 - Preference handling and optimization are indispensable means for addressing nontrivial applications in Answer Set Programming (ASP). However, their implementation becomes difficult whenever they bring about a significant increase in computational complexity. As a consequence, existing ASP systems do not offer complex optimization capacities, supporting, for instance, inclusion-based minimization or Pareto efficiency. Rather, such complex criteria are typically addressed by resorting to dedicated modeling techniques, like saturation. Unlike the ease of common ASP modeling, however, these techniques are rather involved and hardly usable by ASP laymen. We address this problem by developing a general implementation technique by means of meta-prpogramming, thus reusing existing ASP systems to capture various forms of qualitative preferences among answer sets. In this way, complex preferences and optimization capacities become readily available for ASP applications. KW - Answer Set Programming KW - Preference Handling KW - Complex optimization KW - Meta-Programming Y1 - 2011 U6 - https://doi.org/10.1017/S1471068411000329 SN - 1471-0684 VL - 11 IS - 3 SP - 821 EP - 839 PB - Cambridge Univ. Press CY - New York ER - TY - JOUR A1 - Durzinsky, Markus A1 - Marwan, Wolfgang A1 - Ostrowski, Max A1 - Schaub, Torsten H. A1 - Wagler, Annegret T1 - Automatic network reconstruction using ASP JF - Theory and practice of logic programming N2 - Building biological models by inferring functional dependencies from experimental data is an important issue in Molecular Biology. To relieve the biologist from this traditionally manual process, various approaches have been proposed to increase the degree of automation. However, available approaches often yield a single model only, rely on specific assumptions, and/or use dedicated, heuristic algorithms that are intolerant to changing circumstances or requirements in the view of the rapid progress made in Biotechnology. Our aim is to provide a declarative solution to the problem by appeal to Answer Set Programming (ASP) overcoming these difficulties. We build upon an existing approach to Automatic Network Reconstruction proposed by part of the authors. This approach has firm mathematical foundations and is well suited for ASP due to its combinatorial flavor providing a characterization of all models explaining a set of experiments. The usage of ASP has several benefits over the existing heuristic algorithms. First, it is declarative and thus transparent for biological experts. Second, it is elaboration tolerant and thus allows for an easy exploration and incorporation of biological constraints. Third, it allows for exploring the entire space of possible models. Finally, our approach offers an excellent performance, matching existing, special-purpose systems. Y1 - 2011 U6 - https://doi.org/10.1017/S1471068411000287 SN - 1471-0684 VL - 11 SP - 749 EP - 766 PB - Cambridge Univ. Press CY - New York ER - TY - JOUR A1 - Mileo, Alessandra A1 - Schaub, Torsten H. A1 - Merico, Davide A1 - Bisiani, Roberto T1 - Knowledge-based multi-criteria optimization to support indoor positioning JF - Annals of mathematics and artificial intelligence N2 - Indoor position estimation constitutes a central task in home-based assisted living environments. Such environments often rely on a heterogeneous collection of low-cost sensors whose diversity and lack of precision has to be compensated by advanced techniques for localization and tracking. Although there are well established quantitative methods in robotics and neighboring fields for addressing these problems, they lack advanced knowledge representation and reasoning capacities. Such capabilities are not only useful in dealing with heterogeneous and incomplete information but moreover they allow for a better inclusion of semantic information and more general homecare and patient-related knowledge. We address this problem and investigate how state-of-the-art localization and tracking methods can be combined with Answer Set Programming, as a popular knowledge representation and reasoning formalism. We report upon a case-study and provide a first experimental evaluation of knowledge-based position estimation both in a simulated as well as in a real setting. KW - Knowledge representation KW - Answer Set Programming KW - Wireless Sensor Networks KW - Localization KW - Tracking Y1 - 2011 U6 - https://doi.org/10.1007/s10472-011-9241-2 SN - 1012-2443 SN - 1573-7470 VL - 62 IS - 3-4 SP - 345 EP - 370 PB - Springer CY - Dordrecht ER -