@misc{AlvianoRomeroDavilaSchaub2018, author = {Alviano, Mario and Romero Davila, Javier and Schaub, Torsten H.}, title = {Preference Relations by Approximation}, series = {Sixteenth International Conference on Principles of Knowledge Representation and Reasoning}, journal = {Sixteenth International Conference on Principles of Knowledge Representation and Reasoning}, publisher = {AAAI Conference on Artificial Intelligence}, address = {Palo Alto}, pages = {2 -- 11}, year = {2018}, abstract = {Declarative languages for knowledge representation and reasoning provide constructs to define preference relations over the set of possible interpretations, so that preferred models represent optimal solutions of the encoded problem. We introduce the notion of approximation for replacing preference relations with stronger preference relations, that is, relations comparing more pairs of interpretations. Our aim is to accelerate the computation of a non-empty subset of the optimal solutions by means of highly specialized algorithms. We implement our approach in Answer Set Programming (ASP), where problems involving quantitative and qualitative preference relations can be addressed by ASPRIN, implementing a generic optimization algorithm. Unlike this, chains of approximations allow us to reduce several preference relations to the preference relations associated with ASP's native weak constraints and heuristic directives. In this way, ASPRIN can now take advantage of several highly optimized algorithms implemented by ASP solvers for computing optimal solutions}, language = {en} } @article{BobdaYongaGebseretal.2018, author = {Bobda, Christophe and Yonga, Franck and Gebser, Martin and Ishebabi, Harold and Schaub, Torsten H.}, title = {High-level synthesis of on-chip multiprocessor architectures based on answer set programming}, series = {Journal of Parallel and Distributed Computing}, volume = {117}, journal = {Journal of Parallel and Distributed Computing}, publisher = {Elsevier}, address = {San Diego}, issn = {0743-7315}, doi = {10.1016/j.jpdc.2018.02.010}, pages = {161 -- 179}, year = {2018}, abstract = {We present a system-level synthesis approach for heterogeneous multi-processor on chip, based on Answer Set Programming(ASP). Starting with a high-level description of an application, its timing constraints and the physical constraints of the target device, our goal is to produce the optimal computing infrastructure made of heterogeneous processors, peripherals, memories and communication components. Optimization aims at maximizing speed, while minimizing chip area. Also, a scheduler must be produced that fulfills the real-time requirements of the application. Even though our approach will work for application specific integrated circuits, we have chosen FPGA as target device in this work because of their reconfiguration capabilities which makes it possible to explore several design alternatives. This paper addresses the bottleneck of problem representation size by providing a direct and compact ASP encoding for automatic synthesis that is semantically equivalent to previously established ILP and ASP models. We describe a use-case in which designers specify their applications in C/C++ from which optimum systems can be derived. We demonstrate the superiority of our approach toward existing heuristics and exact methods with synthesis results on a set of realistic case studies. (C) 2018 Elsevier Inc. All rights reserved.}, language = {en} } @misc{NeubauerWankoSchaubetal.2018, author = {Neubauer, Kai and Wanko, Philipp and Schaub, Torsten H. and Haubelt, Christian}, title = {Exact multi-objective design space exploration using ASPmT}, series = {Proceedings of the 2018 Design, Automation \& Test in Europe Conference \& Exhibition (DATE)}, journal = {Proceedings of the 2018 Design, Automation \& Test in Europe Conference \& Exhibition (DATE)}, publisher = {IEEE}, address = {New York}, isbn = {978-3-9819-2630-9}, issn = {1530-1591}, doi = {10.23919/DATE.2018.8342014}, pages = {257 -- 260}, year = {2018}, abstract = {An efficient Design Space Exploration (DSE) is imperative for the design of modern, highly complex embedded systems in order to steer the development towards optimal design points. The early evaluation of design decisions at system-level abstraction layer helps to find promising regions for subsequent development steps in lower abstraction levels by diminishing the complexity of the search problem. In recent works, symbolic techniques, especially Answer Set Programming (ASP) modulo Theories (ASPmT), have been shown to find feasible solutions of highly complex system-level synthesis problems with non-linear constraints very efficiently. In this paper, we present a novel approach to a holistic system-level DSE based on ASPmT. To this end, we include additional background theories that concurrently guarantee compliance with hard constraints and perform the simultaneous optimization of several design objectives. We implement and compare our approach with a state-of-the-art preference handling framework for ASP. Experimental results indicate that our proposed method produces better solutions with respect to both diversity and convergence to the true Pareto front.}, language = {en} } @article{GebserKaminskiKaufmannetal.2018, author = {Gebser, Martin and Kaminski, Roland and Kaufmann, Benjamin and Schaub, Torsten H.}, title = {Multi-shot ASP solving with clingo}, series = {Theory and practice of logic programming}, volume = {19}, journal = {Theory and practice of logic programming}, number = {1}, publisher = {Cambridge Univ. Press}, address = {New York}, issn = {1471-0684}, doi = {10.1017/S1471068418000054}, pages = {27 -- 82}, year = {2018}, abstract = {We introduce a new flexible paradigm of grounding and solving in Answer Set Programming (ASP), which we refer to as multi-shot ASP solving, and present its implementation in the ASP system clingo. Multi-shot ASP solving features grounding and solving processes that deal with continuously changing logic programs. In doing so, they remain operative and accommodate changes in a seamless way. For instance, such processes allow for advanced forms of search, as in optimization or theory solving, or interaction with an environment, as in robotics or query answering. Common to them is that the problem specification evolves during the reasoning process, either because data or constraints are added, deleted, or replaced. This evolutionary aspect adds another dimension to ASP since it brings about state changing operations. We address this issue by providing an operational semantics that characterizes grounding and solving processes in multi-shot ASP solving. This characterization provides a semantic account of grounder and solver states along with the operations manipulating them. The operative nature of multi-shot solving avoids redundancies in relaunching grounder and solver programs and benefits from the solver's learning capacities. clingo accomplishes this by complementing ASP's declarative input language with control capacities. On the declarative side, a new directive allows for structuring logic programs into named and parameterizable subprograms. The grounding and integration of these subprograms into the solving process is completely modular and fully controllable from the procedural side. To this end, clingo offers a new application programming interface that is conveniently accessible via scripting languages. By strictly separating logic and control, clingo also abolishes the need for dedicated systems for incremental and reactive reasoning, like iclingo and oclingo, respectively, and its flexibility goes well beyond the advanced yet still rigid solving processes of the latter.}, language = {en} } @misc{RazzaqKaminskiRomeroetal.2018, author = {Razzaq, Misbah and Kaminski, Roland and Romero, Javier and Schaub, Torsten H. and Bourdon, Jeremie and Guziolowski, Carito}, title = {Computing diverse boolean networks from phosphoproteomic time series data}, series = {Computational Methods in Systems Biology}, volume = {11095}, journal = {Computational Methods in Systems Biology}, publisher = {Springer}, address = {Berlin}, isbn = {978-3-319-99429-1}, issn = {0302-9743}, doi = {10.1007/978-3-319-99429-1_4}, pages = {59 -- 74}, year = {2018}, abstract = {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.}, language = {en} }