@article{BanbaraInoueKaufmannetal.2018,
  author    = {Banbara, Mutsunori and Inoue, Katsumi and Kaufmann, Benjamin and Okimoto, Tenda and Schaub, Torsten H. and Soh, Takehide and Tamura, Naoyuki and Wanko, Philipp},
  title     = {teaspoon},
  series = {Annals of operation research},
  volume    = {275},
  journal   = {Annals of operation research},
  number    = {1},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {0254-5330},
  doi       = {10.1007/s10479-018-2757-7},
  pages     = {3 -- 37},
  year      = {2018},
  abstract  = {Answer Set Programming (ASP) is an approach to declarative problem solving, combining a rich yet simple modeling language with high performance solving capacities. We here develop an ASP-based approach to curriculum-based course timetabling (CB-CTT), one of the most widely studied course timetabling problems. The resulting teaspoon system reads a CB-CTT instance of a standard input format and converts it into a set of ASP facts. In turn, these facts are combined with a first-order encoding for CB-CTT solving, which can subsequently be solved by any off-the-shelf ASP systems. We establish the competitiveness of our approach by empirically contrasting it to the best known bounds obtained so far via dedicated implementations. Furthermore, we extend the teaspoon system to multi-objective course timetabling and consider minimal perturbation problems.},
  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}
}
@article{RoostapourNeumannNeumannetal.2022,
  author    = {Roostapour, Vahid and Neumann, Aneta and Neumann, Frank and Friedrich, Tobias},
  title     = {Pareto optimization for subset selection with dynamic cost constraints},
  series = {Artificial intelligence},
  volume    = {302},
  journal   = {Artificial intelligence},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0004-3702},
  doi       = {10.1016/j.artint.2021.103597},
  pages     = {17},
  year      = {2022},
  abstract  = {We consider the subset selection problem for function f with constraint bound B that changes over time. Within the area of submodular optimization, various greedy approaches are commonly used. For dynamic environments we observe that the adaptive variants of these greedy approaches are not able to maintain their approximation quality. Investigating the recently introduced POMC Pareto optimization approach, we show that this algorithm efficiently computes a phi=(alpha(f)/2)(1 - 1/e(alpha)f)-approximation, where alpha(f) is the submodularity ratio of f, for each possible constraint bound b <= B. Furthermore, we show that POMC is able to adapt its set of solutions quickly in the case that B increases. Our experimental investigations for the influence maximization in social networks show the advantage of POMC over generalized greedy algorithms. We also consider EAMC, a new evolutionary algorithm with polynomial expected time guarantee to maintain phi approximation ratio, and NSGA-II with two different population sizes as advanced multi-objective optimization algorithm, to demonstrate their challenges in optimizing the maximum coverage problem. Our empirical analysis shows that, within the same number of evaluations, POMC is able to perform as good as NSGA-II under linear constraint, while EAMC performs significantly worse than all considered algorithms in most cases.},
  language  = {en}
}