@inproceedings{17653, author = {{Polevoy, Gleb and de Weerdt, M.M.}}, booktitle = {{Proceedings of the 29th Benelux Conference on Artificial Intelligence}}, keywords = {{interaction, reciprocation, contribute, shared effort, curbing, convergence, threshold, Nash equilibrium, social welfare, efficiency, price of anarchy, price of stability}}, publisher = {{Springer}}, title = {{{Reciprocation Effort Games}}}, year = {{2017}}, } @inproceedings{17654, author = {{Polevoy, Gleb and de Weerdt, M.M.}}, booktitle = {{Proceedings of the 29th Benelux Conference on Artificial Intelligence}}, keywords = {{agents, projects, contribute, shared effort game, competition, quota, threshold, Nash equilibrium, social welfare, efficiency, price of anarchy, price of stability}}, publisher = {{Springer}}, title = {{{Competition between Cooperative Projects}}}, year = {{2017}}, } @unpublished{17811, abstract = {{We consider a swarm of $n$ autonomous mobile robots, distributed on a 2-dimensional grid. A basic task for such a swarm is the gathering process: All robots have to gather at one (not predefined) place. A common local model for extremely simple robots is the following: The robots do not have a common compass, only have a constant viewing radius, are autonomous and indistinguishable, can move at most a constant distance in each step, cannot communicate, are oblivious and do not have flags or states. The only gathering algorithm under this robot model, with known runtime bounds, needs $\mathcal{O}(n^2)$ rounds and works in the Euclidean plane. The underlying time model for the algorithm is the fully synchronous $\mathcal{FSYNC}$ model. On the other side, in the case of the 2-dimensional grid, the only known gathering algorithms for the same time and a similar local model additionally require a constant memory, states and "flags" to communicate these states to neighbors in viewing range. They gather in time $\mathcal{O}(n)$. In this paper we contribute the (to the best of our knowledge) first gathering algorithm on the grid that works under the same simple local model as the above mentioned Euclidean plane strategy, i.e., without memory (oblivious), "flags" and states. We prove its correctness and an $\mathcal{O}(n^2)$ time bound in the fully synchronous $\mathcal{FSYNC}$ time model. This time bound matches the time bound of the best known algorithm for the Euclidean plane mentioned above. We say gathering is done if all robots are located within a $2\times 2$ square, because in $\mathcal{FSYNC}$ such configurations cannot be solved.}}, author = {{Fischer, Matthias and Jung, Daniel and Meyer auf der Heide, Friedhelm}}, booktitle = {{arXiv:1702.03400}}, title = {{{Gathering Anonymous, Oblivious Robots on a Grid}}}, year = {{2017}}, } @inproceedings{79, abstract = {{Consider a problem in which $n$ jobs that are classified into $k$ types arrive over time at their release times and are to be scheduled on a single machine so as to minimize the maximum flow time.The machine requires a setup taking $s$ time units whenever it switches from processing jobs of one type to jobs of a different type.We consider the problem as an online problem where each job is only known to the scheduler as soon as it arrives and where the processing time of a job only becomes known upon its completion (non-clairvoyance).We are interested in the potential of simple ``greedy-like'' algorithms.We analyze a modification of the FIFO strategy and show its competitiveness to be $\Theta(\sqrt{n})$, which is optimal for the considered class of algorithms.For $k=2$ types it achieves a constant competitiveness.Our main insight is obtained by an analysis of the smoothed competitiveness.If processing times $p_j$ are independently perturbed to $\hat p_j = (1+X_j)p_j$, we obtain a competitiveness of $O(\sigma^{-2} \log^2 n)$ when $X_j$ is drawn from a uniform or a (truncated) normal distribution with standard deviation $\sigma$.The result proves that bad instances are fragile and ``practically'' one might expect a much better performance than given by the $\Omega(\sqrt{n})$-bound.}}, author = {{Mäcker, Alexander and Malatyali, Manuel and Meyer auf der Heide, Friedhelm and Riechers, Sören}}, booktitle = {{Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA)}}, pages = {{207--222}}, publisher = {{Springer}}, title = {{{Non-Clairvoyant Scheduling to Minimize Max Flow Time on a Machine with Setup Times}}}, doi = {{10.1007/978-3-319-89441-6}}, volume = {{10787}}, year = {{2017}}, } @inproceedings{82, abstract = {{Many graph problems such as maximum cut, chromatic number, hamiltonian cycle, and edge dominating set are known to be fixed-parameter tractable (FPT) when parameterized by the treewidth of the input graphs, but become W-hard with respect to the clique-width parameter. Recently, Gajarský et al. proposed a new parameter called modular-width using the notion of modular decomposition of graphs. They showed that the chromatic number problem and the partitioning into paths problem, and hence hamiltonian path and hamiltonian cycle, are FPT when parameterized by this parameter. In this paper, we study modular-width in parameterized parallel complexity and show that the weighted maximum clique problem and the maximum matching problem are fixed-parameter parallel-tractable (FPPT) when parameterized by this parameter.}}, author = {{Abu-Khzam, Faisal N. and Li, Shouwei and Markarian, Christine and Meyer auf der Heide, Friedhelm and Podlipyan, Pavel}}, booktitle = {{Proceedings of the 11th International Workshop on Frontiers in Algorithmics (FAW)}}, pages = {{139--150}}, title = {{{Modular-Width: An Auxiliary Parameter for Parameterized Parallel Complexity}}}, doi = {{10.1007/978-3-319-59605-1_13}}, year = {{2017}}, } @inproceedings{59, abstract = {{We consider a scheduling problem on $m$ identical processors sharing an arbitrarily divisible resource. In addition to assigning jobs to processors, the scheduler must distribute the resource among the processors (e.g., for three processors in shares of 20\%, 15\%, and 65\%) and adjust this distribution over time. Each job $j$ comes with a size $p_j \in \mathbb{R}$ and a resource requirement $r_j > 0$. Jobs do not benefit when receiving a share larger than $r_j$ of the resource. But providing them with a fraction of the resource requirement causes a linear decrease in the processing efficiency. We seek a (non-preemptive) job and resource assignment minimizing the makespan.Our main result is an efficient approximation algorithm which achieves an approximation ratio of $2 + 1/(m-2)$. It can be improved to an (asymptotic) ratio of $1 + 1/(m-1)$ if all jobs have unit size. Our algorithms also imply new results for a well-known bin packing problem with splittable items and a restricted number of allowed item parts per bin.Based upon the above solution, we also derive an approximation algorithm with similar guarantees for a setting in which we introduce so-called tasks each containing several jobs and where we are interested in the average completion time of tasks (a task is completed when all its jobs are completed).}}, author = {{Kling, Peter and Mäcker, Alexander and Riechers, Sören and Skopalik, Alexander}}, booktitle = {{Proceedings of the 29th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA)}}, pages = {{123----132}}, title = {{{Sharing is Caring: Multiprocessor Scheduling with a Sharable Resource}}}, doi = {{10.1145/3087556.3087578}}, year = {{2017}}, } @inproceedings{70, author = {{Feldkord, Björn and Markarian, Christine and Meyer auf der Heide, Friedhelm}}, booktitle = {{Proceedings of the 11th Annual International Conference on Combinatorial Optimization and Applications (COCOA)}}, pages = {{17 -- 31}}, title = {{{Price Fluctuations in Online Leasing}}}, doi = {{10.1007/978-3-319-71147-8_2}}, year = {{2017}}, } @phdthesis{703, author = {{Podlipyan, Pavel}}, publisher = {{Universität Paderborn}}, title = {{{Local Algorithms for the Continuous Gathering Problem}}}, doi = {{10.17619/UNIPB/1-230}}, year = {{2017}}, } @phdthesis{704, author = {{Riechers, Sören}}, publisher = {{Universität Paderborn}}, title = {{{Scheduling with Scarce Resources}}}, doi = {{10.17619/UNIPB/1-231}}, year = {{2017}}, } @article{706, author = {{Mäcker, Alexander and Malatyali, Manuel and Meyer auf der Heide, Friedhelm and Riechers, Sören}}, journal = {{Journal of Combinatorial Optimization}}, number = {{4}}, pages = {{1168--1194}}, publisher = {{Springer}}, title = {{{Cost-efficient Scheduling on Machines from the Cloud}}}, doi = {{10.1007/s10878-017-0198-x}}, volume = {{36}}, year = {{2017}}, } @inproceedings{97, abstract = {{Bridging the gap between informal, imprecise, and vague user requirements descriptions and precise formalized specifications is the main task of requirements engineering. Techniques such as interviews or story telling are used when requirements engineers try to identify a user's needs. The requirements specification process is typically done in a dialogue between users, domain experts, and requirements engineers. In our research, we aim at automating the specification of requirements. The idea is to distinguish between untrained users and trained users, and to exploit domain knowledge learned from previous runs of our system. We let untrained users provide unstructured natural language descriptions, while we allow trained users to provide examples of behavioral descriptions. In both cases, our goal is to synthesize formal requirements models similar to statecharts. From requirements specification processes with trained users, behavioral ontologies are learned which are later used to support the requirements specification process for untrained users. Our research method is original in combining natural language processing and search-based techniques for the synthesis of requirements specifications. Our work is embedded in a larger project that aims at automating the whole software development and deployment process in envisioned future software service markets.}}, author = {{van Rooijen, Lorijn and Bäumer, Frederik Simon and Platenius, Marie Christin and Geierhos, Michaela and Hamann, Heiko and Engels, Gregor}}, booktitle = {{2017 IEEE 25th International Requirements Engineering Conference Workshops (REW)}}, isbn = {{978-1-5386-3489-9}}, keywords = {{Software, Unified modeling language, Requirements engineering, Ontologies, Search problems, Natural languages}}, location = {{Lisbon, Portugal}}, pages = {{379--385}}, publisher = {{IEEE}}, title = {{{From User Demand to Software Service: Using Machine Learning to Automate the Requirements Specification Process}}}, doi = {{10.1109/REW.2017.26}}, year = {{2017}}, } @inproceedings{55, abstract = {{We introduce the mobile server problem, inspired by current trends to move computational tasks from cloud structures to multiple devices close to the end user. An example for this are embedded systems in autonomous cars that communicate in order to coordinate their actions. Our model is a variant of the classical Page Migration Problem. Moreformally, we consider a mobile server holding a data page.The server can move in the Euclidean space (of arbitrary dimension). In every round, requests for data items from the page pop up at arbitrary points in the space. The requests are served, each at a cost of the distance from the requesting point and the server, and the mobile server may move, at a cost D times the distance traveled for some constant D . We assume a maximum distance m the server is allowed to move per round. We show that no online algorithm can achieve a competitive ratio independent of the length of the input sequence in this setting. Hence we augment the maximum movement distance of the online algorithms to ( 1 + δ) times the maximum distance of the offline solution. We provide a deterministic algorithm which is simple to describe and works for multiple variants of our problem. The algorithm achieves almost tight competitive ratios independent of the length of the input sequence.}}, author = {{Feldkord, Björn and Meyer auf der Heide, Friedhelm}}, booktitle = {{Proceedings of the 29th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA)}}, pages = {{313--319}}, title = {{{The Mobile Server Problem}}}, doi = {{10.1145/3087556.3087575}}, year = {{2017}}, } @inproceedings{66, abstract = {{In budget games, players compete over resources with finite budgets. For every resource, a player has a specific demand and as a strategy, he chooses a subset of resources. If the total demand on a resource does not exceed its budget, the utility of each player who chose that resource equals his demand. Otherwise, the budget is shared proportionally. In the general case, pure Nash equilibria (NE) do not exist for such games. In this paper, we consider the natural classes of singleton and matroid budget games with additional constraints and show that for each, pure NE can be guaranteed. In addition, we introduce a lexicographical potential function to prove that every matroid budget game has an approximate pure NE which depends on the largest ratio between the different demands of each individual player.}}, author = {{Drees, Maximilian and Feldotto, Matthias and Riechers, Sören and Skopalik, Alexander}}, booktitle = {{Proceedings of the 23rd International Computing and Combinatorics Conference (COCOON)}}, pages = {{175----187}}, title = {{{Pure Nash Equilibria in Restricted Budget Games}}}, doi = {{10.1007/978-3-319-62389-4_15}}, year = {{2017}}, } @misc{695, author = {{Nowack, Joshua}}, publisher = {{Universität Paderborn}}, title = {{{On-The-Fly Konstruktion zusammenhängender Straßennetze aus gegebenen Einzelteilen}}}, year = {{2017}}, } @book{16444, author = {{Gausemeier, Jürgen and Bodden, Eric and Dressler, Falko and Dumitrescu, Roman and Meyer auf der Heide, Friedhelm and Scheytt, Christoph and Trächtler, Ansgar}}, pages = {{369}}, title = {{{Wissenschaftsforum Intelligente Technische Systeme (WInTeSys)}}}, year = {{2017}}, } @inbook{16461, author = {{Bemmann, Pascal and Biermeier, Felix and Bürmann, Jan and Kemper, Arne and Knollmann, Till and Knorr, Steffen and Kothe, Nils and Mäcker, Alexander and Malatyali, Manuel and Meyer auf der Heide, Friedhelm and Riechers, Sören and Schaefer, Johannes Sebastian and Sundermeier, Jannik}}, booktitle = {{Structural Information and Communication Complexity}}, isbn = {{9783319720494}}, issn = {{0302-9743}}, title = {{{Monitoring of Domain-Related Problems in Distributed Data Streams}}}, doi = {{10.1007/978-3-319-72050-0_13}}, year = {{2017}}, } @misc{1073, author = {{Nachtigall, Simon}}, publisher = {{Universität Paderborn}}, title = {{{Sortieren dynamischer Daten}}}, year = {{2017}}, } @misc{1074, author = {{Pukrop, Simon}}, publisher = {{Universität Paderborn}}, title = {{{Robuste Optimierung in Congestion Games}}}, year = {{2017}}, } @misc{1080, author = {{Bürmann, Jan}}, publisher = {{Universität Paderborn}}, title = {{{Complexity of Signalling in Routing Games under Uncertainty}}}, year = {{2017}}, } @misc{1081, author = {{Vijayalakshmi, Vipin Ravindran}}, publisher = {{Universität Paderborn}}, title = {{{Bounding the Inefficiency of Equilibria in Congestion Games under Taxation}}}, year = {{2017}}, }