TY - CONF AB - Dynamic optimization problems have gained significant attention in evolutionary computation as evolutionary algorithms (EAs) can easily adapt to changing environments. We show that EAs can solve the graph coloring problem for bipartite graphs more efficiently by using dynamic optimization. In our approach the graph instance is given incrementally such that the EA can reoptimize its coloring when a new edge introduces a conflict. We show that, when edges are inserted in a way that preserves graph connectivity, Randomized Local Search (RLS) efficiently finds a proper 2-coloring for all bipartite graphs. This includes graphs for which RLS and other EAs need exponential expected time in a static optimization scenario. We investigate different ways of building up the graph by popular graph traversals such as breadth-first-search and depth-first-search and analyse the resulting runtime behavior. We further show that offspring populations (e. g. a (1 + {$\lambda$}) RLS) lead to an exponential speedup in {$\lambda$}. Finally, an island model using 3 islands succeeds in an optimal time of {$\Theta$}(m) on every m-edge bipartite graph, outperforming offspring populations. This is the first example where an island model guarantees a speedup that is not bounded in the number of islands. AU - Bossek, Jakob AU - Neumann, Frank AU - Peng, Pan AU - Sudholt, Dirk ID - 48847 KW - dynamic optimization KW - evolutionary algorithms KW - running time analysis KW - theory SN - 978-1-4503-7128-5 T2 - Proceedings of the Genetic and Evolutionary Computation Conference TI - More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization ER - TY - JOUR AU - Moritzer, Elmar AU - Mühlhoff, Frederik Marvin AU - Krampe, Erhard AU - Böhnke, Birte ID - 24250 JF - Kunststoffe international TI - More Material Combinations, Lower Costs ER - TY - JOUR AU - Lünne, Steffen AU - Schnell, Susanne AU - Biehler, Rolf ID - 35701 IS - 5 JF - European Journal of Teacher Education KW - Education SN - 0261-9768 TI - Motivation of out-of-field teachers for participating in professional development courses in mathematics VL - 44 ER - TY - JOUR AB - Im Artikel werden drei verschiedene Lernzugänge (kom-petenzorientiertes, ästhetisches und biographisches Lernen) vorgestellt und aus theoretischer Perspektive deren motivierender Gehalt für selbstreguliertes Lernen in Praxisphasen des Lehramtsstudiumsherausgearbeitet. Als theoretische Grund-lage dient die Selbstbestimmungstheorie als zentrale motivationale Theorie zur Erklärung selbstbestimmten Handelns. AU - Caruso, Carina AU - Adammek, Christine AU - Bonanati, Sabrina AU - Wiescholek, Sybille ID - 35298 IS - 1 JF - Herausforderung Lehrer*innenbildung - Zeitschrift Zur Konzeption, Gestaltung Und Diskussion KW - ästhetische Forschung KW - Biographiearbeit KW - Praxissemester KW - Professionalisierung KW - selbstreguliertes Lernen KW - Motivation / aesthetic research KW - biographical work KW - long-term internship KW - profes-sionalization KW - self-regulated learning KW - motivation SN - 2625-0675 TI - Motivierende Lernzugänge als Ausgangspunkt der Professionalisierung angehender Lehrer_innen VL - 3 ER - TY - GEN AB - We consider a resource-aware variant of the classical multi-armed bandit problem: In each round, the learner selects an arm and determines a resource limit. It then observes a corresponding (random) reward, provided the (random) amount of consumed resources remains below the limit. Otherwise, the observation is censored, i.e., no reward is obtained. For this problem setting, we introduce a measure of regret, which incorporates the actual amount of allocated resources of each learning round as well as the optimality of realizable rewards. Thus, to minimize regret, the learner needs to set a resource limit and choose an arm in such a way that the chance to realize a high reward within the predefined resource limit is high, while the resource limit itself should be kept as low as possible. We derive the theoretical lower bound on the cumulative regret and propose a learning algorithm having a regret upper bound that matches the lower bound. In a simulation study, we show that our learning algorithm outperforms straightforward extensions of standard multi-armed bandit algorithms. AU - Bengs, Viktor AU - Hüllermeier, Eyke ID - 21536 T2 - arXiv:2011.00813 TI - Multi-Armed Bandits with Censored Consumption of Resources ER - TY - GEN AB - We consider a resource-aware variant of the classical multi-armed bandit problem: In each round, the learner selects an arm and determines a resource limit. It then observes a corresponding (random) reward, provided the (random) amount of consumed resources remains below the limit. Otherwise, the observation is censored, i.e., no reward is obtained. For this problem setting, we introduce a measure of regret, which incorporates the actual amount of allocated resources of each learning round as well as the optimality of realizable rewards. Thus, to minimize regret, the learner needs to set a resource limit and choose an arm in such a way that the chance to realize a high reward within the predefined resource limit is high, while the resource limit itself should be kept as low as possible. We derive the theoretical lower bound on the cumulative regret and propose a learning algorithm having a regret upper bound that matches the lower bound. In a simulation study, we show that our learning algorithm outperforms straightforward extensions of standard multi-armed bandit algorithms. AU - Bengs, Viktor AU - Hüllermeier, Eyke ID - 32242 T2 - arXiv:2011.00813 TI - Multi-Armed Bandits with Censored Consumption of Resources ER - TY - JOUR AB - Assigning bands of the wireless spectrum as resources to users is a common problem in wireless networks. Typically, frequency bands were assumed to be available in a stable manner. Nevertheless, in recent scenarios where wireless networks may be deployed in unknown environments, spectrum competition is considered, making it uncertain whether a frequency band is available at all or at what quality. To fully exploit such resources with uncertain availability, the multi-armed bandit (MAB) method, a representative online learning technique, has been applied to design spectrum scheduling algorithms. This article surveys such proposals. We describe the following three aspects: how to model spectrum scheduling problems within the MAB framework, what the main thread is following which prevalent algorithms are designed, and how to evaluate algorithm performance and complexity. We also give some promising directions for future research in related fields. AU - Li, Feng AU - Yu, Dongxiao AU - Yang, Huan AU - Yu, Jiguo AU - Karl, Holger AU - Cheng, Xiuzhen ID - 16280 JF - IEEE Wireless Communications SN - 1536-1284 TI - Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey ER - TY - JOUR AB - In software engineering, the imprecise requirements of a user are transformed to a formal requirements specification during the requirements elicitation process. This process is usually guided by requirements engineers interviewing the user. We want to partially automate this first step of the software engineering process in order to enable users to specify a desired software system on their own. With our approach, users are only asked to provide exemplary behavioral descriptions. The problem of synthesizing a requirements specification from examples can partially be reduced to the problem of grammatical inference, to which we apply an active coevolutionary learning approach. However, this approach would usually require many feedback queries to be sent to the user. In this work, we extend and generalize our active learning approach to receive knowledge from multiple oracles, also known as proactive learning. The ‘user oracle’ represents input received from the user and the ‘knowledge oracle’ represents available, formalized domain knowledge. We call our two-oracle approach the ‘first apply knowledge then query’ (FAKT/Q) algorithm. We compare FAKT/Q to the active learning approach and provide an extensive benchmark evaluation. As result we find that the number of required user queries is reduced and the inference process is sped up significantly. Finally, with so-called On-The-Fly Markets, we present a motivation and an application of our approach where such knowledge is available. AU - Wever, Marcel Dominik AU - van Rooijen, Lorijn AU - Hamann, Heiko ID - 15025 IS - 2 JF - Evolutionary Computation TI - Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets VL - 28 ER - TY - CONF AB - Recently, the source separation performance was greatly improved by time-domain audio source separation based on dual-path recurrent neural network (DPRNN). DPRNN is a simple but effective model for a long sequential data. While DPRNN is quite efficient in modeling a sequential data of the length of an utterance, i.e., about 5 to 10 second data, it is harder to apply it to longer sequences such as whole conversations consisting of multiple utterances. It is simply because, in such a case, the number of time steps consumed by its internal module called inter-chunk RNN becomes extremely large. To mitigate this problem, this paper proposes a multi-path RNN (MPRNN), a generalized version of DPRNN, that models the input data in a hierarchical manner. In the MPRNN framework, the input data is represented at several (>_ 3) time-resolutions, each of which is modeled by a specific RNN sub-module. For example, the RNN sub-module that deals with the finest resolution may model temporal relationship only within a phoneme, while the RNN sub-module handling the most coarse resolution may capture only the relationship between utterances such as speaker information. We perform experiments using simulated dialogue-like mixtures and show that MPRNN has greater model capacity, and it outperforms the current state-of-the-art DPRNN framework especially in online processing scenarios. AU - Kinoshita, Keisuke AU - von Neumann, Thilo AU - Delcroix, Marc AU - Nakatani, Tomohiro AU - Haeb-Umbach, Reinhold ID - 20766 T2 - Proc. Interspeech 2020 TI - Multi-Path RNN for Hierarchical Modeling of Long Sequential Data and its Application to Speaker Stream Separation ER - TY - CONF AB - Most approaches to multi-talker overlapped speech separation and recognition assume that the number of simultaneously active speakers is given, but in realistic situations, it is typically unknown. To cope with this, we extend an iterative speech extraction system with mechanisms to count the number of sources and combine it with a single-talker speech recognizer to form the first end-to-end multi-talker automatic speech recognition system for an unknown number of active speakers. Our experiments show very promising performance in counting accuracy, source separation and speech recognition on simulated clean mixtures from WSJ0-2mix and WSJ0-3mix. Among others, we set a new state-of-the-art word error rate on the WSJ0-2mix database. Furthermore, our system generalizes well to a larger number of speakers than it ever saw during training, as shown in experiments with the WSJ0-4mix database. AU - von Neumann, Thilo AU - Boeddeker, Christoph AU - Drude, Lukas AU - Kinoshita, Keisuke AU - Delcroix, Marc AU - Nakatani, Tomohiro AU - Haeb-Umbach, Reinhold ID - 20764 T2 - Proc. Interspeech 2020 TI - Multi-Talker ASR for an Unknown Number of Sources: Joint Training of Source Counting, Separation and ASR ER -