TY - GEN A1 - Neubauer, Kai A1 - Haubelt, Christian A1 - Wanko, Philipp A1 - Schaub, Torsten H. T1 - Utilizing quad-trees for efficient design space exploration with partial assignment evaluation T2 - 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC) N2 - Recently, it has been shown that constraint-based symbolic solving techniques offer an efficient way for deciding binding and routing options in order to obtain a feasible system level implementation. In combination with various background theories, a feasibility analysis of the resulting system may already be performed on partial solutions. That is, infeasible subsets of mapping and routing options can be pruned early in the decision process, which fastens the solving accordingly. However, allowing a proper design space exploration including multi-objective optimization also requires an efficient structure for storing and managing non-dominated solutions. In this work, we propose and study the usage of the Quad-Tree data structure in the context of partial assignment evaluation during system synthesis. Out experiments show that unnecessary dominance checks can be avoided, which indicates a preference of Quad-Trees over a commonly used list-based implementation for large combinatorial optimization problems. Y1 - 2018 SN - 978-1-5090-0602-1 U6 - https://doi.org/10.1109/ASPDAC.2018.8297362 SN - 2153-6961 SP - 434 EP - 439 PB - IEEE CY - New York ER - TY - GEN A1 - Neubauer, Kai A1 - Wanko, Philipp A1 - Schaub, Torsten H. A1 - Haubelt, Christian T1 - Exact multi-objective design space exploration using ASPmT T2 - Proceedings of the 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE) N2 - 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. Y1 - 2018 SN - 978-3-9819-2630-9 U6 - https://doi.org/10.23919/DATE.2018.8342014 SN - 1530-1591 SN - 1558-1101 SP - 257 EP - 260 PB - IEEE CY - New York 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 - TY - GEN A1 - Bosser, Anne-Gwenn A1 - Cabalar, Pedro A1 - Dieguez, Martin A1 - Schaub, Torsten H. T1 - Introducing temporal stable models for linear dynamic logic T2 - 16th International Conference on Principles of Knowledge Representation and Reasoning N2 - We propose a new temporal extension of the logic of Here-and-There (HT) and its equilibria obtained by combining it with dynamic logic over (linear) traces. Unlike previous temporal extensions of HT based on linear temporal logic, the dynamic logic features allow us to reason about the composition of actions. For instance, this can be used to exercise fine grained control when planning in robotics, as exemplified by GOLOG. In this paper, we lay the foundations of our approach, and refer to it as Linear Dynamic Equilibrium Logic, or simply DEL. We start by developing the formal framework of DEL and provide relevant characteristic results. Among them, we elaborate upon the relationships to traditional linear dynamic logic and previous temporal extensions of HT. Y1 - 2018 UR - https://www.dc.fi.udc.es/~cabalar/del.pdf SP - 12 EP - 21 PB - ASSOC Association for the Advancement of Artificial Intelligence CY - Palo Alto ER - TY - GEN A1 - Alviano, Mario A1 - Romero Davila, Javier A1 - Schaub, Torsten H. T1 - Preference Relations by Approximation T2 - Sixteenth International Conference on Principles of Knowledge Representation and Reasoning N2 - 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 Y1 - 2018 SP - 2 EP - 11 PB - AAAI Conference on Artificial Intelligence CY - Palo Alto ER - TY - GEN A1 - Schäpers, Björn A1 - Niemueller, Tim A1 - Lakemeyer, Gerhard A1 - Gebser, Martin A1 - Schaub, Torsten H. T1 - ASP-Based Time-Bounded Planning for Logistics Robots T2 - Twenty-Eighth International Conference on Automated Planning and Scheduling (ICAPS 2018) N2 - Manufacturing industries are undergoing a major paradigm shift towards more autonomy. Automated planning and scheduling then becomes a necessity. The Planning and Execution Competition for Logistics Robots in Simulation held at ICAPS is based on this scenario and provides an interesting testbed. However, the posed problem is challenging as also demonstrated by the somewhat weak results in 2017. The domain requires temporal reasoning and dealing with uncertainty. We propose a novel planning system based on Answer Set Programming and the Clingo solver to tackle these problems and incentivize robot cooperation. Our results show a significant performance improvement, both, in terms of lowering computational requirements and better game metrics. Y1 - 2018 SN - 2334-0835 SN - 2334-0843 SP - 509 EP - 517 PB - ASSOC Association for the Advancement of Artificial Intelligence CY - Palo Alto ER - TY - GEN A1 - Brewka, Gerhard A1 - Schaub, Torsten H. A1 - Woltran, Stefan T1 - Interview with Gerhard Brewka T2 - Künstliche Intelligenz N2 - This interview with Gerhard Brewka was conducted by correspondance in May 2018. The question set was compiled by Torsten Schaub and Stefan Woltran. Y1 - 2018 U6 - https://doi.org/10.1007/s13218-018-0549-5 SN - 0933-1875 SN - 1610-1987 VL - 32 IS - 2-3 SP - 219 EP - 221 PB - Springer CY - Heidelberg ER - TY - GEN A1 - Lifschitz, Vladimir A1 - Schaub, Torsten H. A1 - Woltran, Stefan T1 - Interview with Vladimir Lifschitz T2 - Künstliche Intelligenz N2 - This interview with Vladimir Lifschitz was conducted by Torsten Schaub at the University of Texas at Austin in August 2017. The question set was compiled by Torsten Schaub and Stefan Woltran. Y1 - 2018 U6 - https://doi.org/10.1007/s13218-018-0552-x SN - 0933-1875 SN - 1610-1987 VL - 32 IS - 2-3 SP - 213 EP - 218 PB - Springer CY - Heidelberg ER - TY - GEN A1 - Schaub, Torsten H. A1 - Woltran, Stefan T1 - Special issue on answer set programming T2 - Künstliche Intelligenz Y1 - 2018 U6 - https://doi.org/10.1007/s13218-018-0554-8 SN - 0933-1875 SN - 1610-1987 VL - 32 IS - 2-3 SP - 101 EP - 103 PB - Springer CY - Heidelberg ER -