TY  - JOUR
A1  - Brewka, Gerhard
A1  - Ellmauthaler, Stefan
A1  - Kern-Isberner, Gabriele
A1  - Obermeier, Philipp
A1  - Ostrowski, Max
A1  - Romero, Javier
A1  - Schaub, Torsten H.
A1  - Schieweck, Steffen
T1  - Advanced solving technology for dynamic and reactive applications
JF  - Künstliche Intelligenz
Y1  - 2018
U6  - https://doi.org/10.1007/s13218-018-0538-8
SN  - 0933-1875
SN  - 1610-1987
VL  - 32
IS  - 2-3
SP  - 199
EP  - 200
PB  - Springer
CY  - Heidelberg
ER  - 
TY  - JOUR
A1  - Cabalar, Pedro
A1  - Fandinno, Jorge
A1  - Garea, Javier
A1  - Romero, Javier
A1  - Schaub, Torsten H.
T1  - Eclingo
BT  - a solver for epistemic logic programs
JF  - Theory and practice of logic programming
N2  - We describe eclingo, a solver for epistemic logic programs under Gelfond 1991 semantics built upon the Answer Set Programming system clingo. The input language of eclingo uses the syntax extension capabilities of clingo to define subjective literals that, as usual in epistemic logic programs, allow for checking the truth of a regular literal in all or in some of the answer sets of a program. The eclingo solving process follows a guess and check strategy. It first generates potential truth values for subjective literals and, in a second step, it checks the obtained result with respect to the cautious and brave consequences of the program. This process is implemented using the multi-shot functionalities of clingo. We have also implemented some optimisations, aiming at reducing the search space and, therefore, increasing eclingo 's efficiency in some scenarios. Finally, we compare the efficiency of eclingo with two state-of-the-art solvers for epistemic logic programs on a pair of benchmark scenarios and show that eclingo generally outperforms their obtained results.
KW  - Answer Set Programming
KW  - Epistemic Logic Programs
KW  - Non-Monotonic
KW  - Reasoning
KW  - Conformant Planning
Y1  - 2020
U6  - https://doi.org/10.1017/S1471068420000228
SN  - 1471-0684
SN  - 1475-3081
VL  - 20
IS  - 6
SP  - 834
EP  - 847
PB  - Cambridge Univ. Press
CY  - New York
ER  - 
TY  - JOUR
A1  - Fandinno, Jorge
A1  - Laferriere, Francois
A1  - Romero, Javier
A1  - Schaub, Torsten H.
A1  - Son, Tran Cao
T1  - Planning with incomplete information in quantified answer set programming
JF  - Theory and practice of logic programming
N2  - We present a general approach to planning with incomplete information in Answer Set Programming (ASP). More precisely, we consider the problems of conformant and conditional planning with sensing actions and assumptions. We represent planning problems using a simple formalism where logic programs describe the transition function between states, the initial states and the goal states. For solving planning problems, we use Quantified Answer Set Programming (QASP), an extension of ASP with existential and universal quantifiers over atoms that is analogous to Quantified Boolean Formulas (QBFs). We define the language of quantified logic programs and use it to represent the solutions different variants of conformant and conditional planning. On the practical side, we present a translation-based QASP solver that converts quantified logic programs into QBFs and then executes a QBF solver, and we evaluate experimentally the approach on conformant and conditional planning benchmarks.
KW  - answer set programming
KW  - planning
KW  - quantified logics
Y1  - 2021
U6  - https://doi.org/10.1017/S1471068421000259
SN  - 1471-0684
SN  - 1475-3081
VL  - 21
IS  - 5
SP  - 663
EP  - 679
PB  - Cambridge University Press
CY  - Cambridge
ER  - 
TY  - GEN
A1  - Razzaq, Misbah
A1  - Kaminski, Roland
A1  - Romero, Javier
A1  - Schaub, Torsten H.
A1  - Bourdon, Jeremie
A1  - Guziolowski, Carito
T1  - Computing diverse boolean networks from phosphoproteomic time series data
T2  - Computational Methods in Systems Biology
N2  - Logical modeling has been widely used to understand and expand the knowledge about protein interactions among different pathways. Realizing this, the caspo-ts system has been proposed recently to learn logical models from time series data. It uses Answer Set Programming to enumerate Boolean Networks (BNs) given prior knowledge networks and phosphoproteomic time series data. In the resulting sequence of solutions, similar BNs are typically clustered together. This can be problematic for large scale problems where we cannot explore the whole solution space in reasonable time. Our approach extends the caspo-ts system to cope with the important use case of finding diverse solutions of a problem with a large number of solutions. We first present the algorithm for finding diverse solutions and then we demonstrate the results of the proposed approach on two different benchmark scenarios in systems biology: (1) an artificial dataset to model TCR signaling and (2) the HPN-DREAM challenge dataset to model breast cancer cell lines.
KW  - Diverse solution enumeration
KW  - Answer set programming
KW  - Boolean Networks
KW  - Model checking
KW  - Time series data
Y1  - 2018
SN  - 978-3-319-99429-1
SN  - 978-3-319-99428-4
U6  - https://doi.org/10.1007/978-3-319-99429-1_4
SN  - 0302-9743
SN  - 1611-3349
VL  - 11095
SP  - 59
EP  - 74
PB  - Springer
CY  - Berlin
ER  -