@inproceedings{61492, abstract = {{This paper deals with the development and results of a prediction framework for traffic light control systems as well as the usage and benefits of such predictions in green light optimal speed advisory (GLOSA) scenarios. Various machine learning methods like support vector machines, neural networks or reinforcement learning were evaluated for their applicability in the prediction context and compared based on their efficiency and most importantly accuracy. The resulting prediction framework uses decision tree ensemble models combined with certain model knowledge to forecast different control strategies. This method was chosen due to its best performance in various test scenarios. Very high accuracy and fidelity were achieved for standard control methods like fixed-time, time-of-day-based and 'ordinary' traffic-based programs. Only for the more sophisticated model predictive control which was tested lower accuracies were achieved. For the upcoming GLOSA application the penetration of equipped vehicles was varied for different traffic scenarios and control strategies. Results showcase high potentials for enhancing urban mobility and reducing environmental impact by lower emissions and waiting times. However, it is also clear from the studies presented in this contribution that the coordination of the control strategy with the GLOSA vehicles is of enormous importance.}}, author = {{Malena, Kevin and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)}}, keywords = {{ML, Prediction, Tree Ensembles, GLOSA}}, location = {{Gold Coast (Australia)}}, publisher = {{IEEE}}, title = {{{ML-based Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application}}}, volume = {{28}}, year = {{2026}}, } @inbook{62701, abstract = {{Learning continuous vector representations for knowledge graphs has significantly improved state-of-the-art performances in many challenging tasks. Yet, deep-learning-based models are only post-hoc and locally explainable. In contrast, learning Web Ontology Language (OWL) class expressions in Description Logics (DLs) is ante-hoc and globally explainable. However, state-of-the-art learners have two well-known lim-itations: scaling to large knowledge graphs and handling missing infor-mation. Here, we present a decision-tree-based learner (tDL) to learn Web Ontology Languages (OWLs) class expressions over large knowl-edge graphs, while imputing missing triples. Given positive and negative example individuals, tDL firstly constructs unique OWL expressions in .SHOIN from concise bounded descriptions of individuals. Each OWL class expression is used as a feature in a binary classification problem to represent input individuals. Thereafter, tDL fits a CART decision tree to learn Boolean decision rules distinguishing positive examples from nega-tive examples. A final OWL expression in.SHOIN is built by traversing the built CART decision tree from the root node to leaf nodes for each positive example. By this, tDL can learn OWL class expressions without exploration, i.e., the number of queries to a knowledge graph is bounded by the number of input individuals. Our empirical results show that tDL outperforms the current state-of-the-art models across datasets. Impor-tantly, our experiments over a large knowledge graph (DBpedia with 1.1 billion triples) show that tDL can effectively learn accurate OWL class expressions, while the state-of-the-art models fail to return any results. Finally, expressions learned by tDL can be seamlessly translated into natural language explanations using a pre-trained large language model and a DL verbalizer.}}, author = {{Demir, Caglar and Yekini, Moshood and Röder, Michael and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032060655}}, issn = {{0302-9743}}, keywords = {{Decision Tree, OWL Class Expression Learning, Description Logic, Knowledge Graph, Large Language Model, Verbalizer}}, location = {{Porto, Portugal}}, publisher = {{Springer Nature Switzerland}}, title = {{{Tree-Based OWL Class Expression Learner over Large Graphs}}}, doi = {{10.1007/978-3-032-06066-2_29}}, year = {{2025}}, } @inproceedings{29220, abstract = {{Modern services often comprise several components, such as chained virtual network functions, microservices, or machine learning functions. Providing such services requires to decide how often to instantiate each component, where to place these instances in the network, how to chain them and route traffic through them. To overcome limitations of conventional, hardwired heuristics, deep reinforcement learning (DRL) approaches for self-learning network and service management have emerged recently. These model-free DRL approaches are more flexible but typically learn tabula rasa, i.e., disregard existing understanding of networks, services, and their coordination. Instead, we propose FutureCoord, a novel model-based AI approach that leverages existing understanding of networks and services for more efficient and effective coordination without time-intensive training. FutureCoord combines Monte Carlo Tree Search with a stochastic traffic model. This allows FutureCoord to estimate the impact of future incoming traffic and effectively optimize long-term effects, taking fluctuating demand and Quality of Service (QoS) requirements into account. Our extensive evaluation based on real-world network topologies, services, and traffic traces indicates that FutureCoord clearly outperforms state-of-the-art model-free and model-based approaches with up to 51% higher flow success ratios.}}, author = {{Werner, Stefan and Schneider, Stefan Balthasar and Karl, Holger}}, booktitle = {{IEEE/IFIP Network Operations and Management Symposium (NOMS)}}, keywords = {{network management, service management, AI, Monte Carlo Tree Search, model-based, QoS}}, location = {{Budapest}}, publisher = {{IEEE}}, title = {{{Use What You Know: Network and Service Coordination Beyond Certainty}}}, year = {{2022}}, } @inproceedings{48860, abstract = {{In the area of evolutionary computation the calculation of diverse sets of high-quality solutions to a given optimization problem has gained momentum in recent years under the term evolutionary diversity optimization. Theoretical insights into the working principles of baseline evolutionary algorithms for diversity optimization are still rare. In this paper we study the well-known Minimum Spanning Tree problem (MST) in the context of diversity optimization where population diversity is measured by the sum of pairwise edge overlaps. Theoretical results provide insights into the fitness landscape of the MST diversity optimization problem pointing out that even for a population of {$\mu$} = 2 fitness plateaus (of constant length) can be reached, but nevertheless diverse sets can be calculated in polynomial time. We supplement our theoretical results with a series of experiments for the unconstrained and constraint case where all solutions need to fulfill a minimal quality threshold. Our results show that a simple ({$\mu$} + 1)-EA can effectively compute a diversified population of spanning trees of high quality.}}, author = {{Bossek, Jakob and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-8350-9}}, keywords = {{evolutionary algorithms, evolutionary diversity optimization, minimum spanning tree, runtime analysis}}, pages = {{198–206}}, publisher = {{Association for Computing Machinery}}, title = {{{Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem}}}, doi = {{10.1145/3449639.3459363}}, year = {{2021}}, } @inproceedings{48895, abstract = {{Evolutionary algorithms (EAs) are general-purpose problem solvers that usually perform an unbiased search. This is reasonable and desirable in a black-box scenario. For combinatorial optimization problems, often more knowledge about the structure of optimal solutions is given, which can be leveraged by means of biased search operators. We consider the Minimum Spanning Tree (MST) problem in a single- and multi-objective version, and introduce a biased mutation, which puts more emphasis on the selection of edges of low rank in terms of low domination number. We present example graphs where the biased mutation can significantly speed up the expected runtime until (Pareto-)optimal solutions are found. On the other hand, we demonstrate that bias can lead to exponential runtime if "heavy" edges are necessarily part of an optimal solution. However, on general graphs in the single-objective setting, we show that a combined mutation operator which decides for unbiased or biased edge selection in each step with equal probability exhibits a polynomial upper bound - as unbiased mutation - in the worst case and benefits from bias if the circumstances are favorable.}}, author = {{Roostapour, Vahid and Bossek, Jakob and Neumann, Frank}}, booktitle = {{Proceedings of the 2020 Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-7128-5}}, keywords = {{biased mutation, evolutionary algorithms, minimum spanning tree problem, runtime analysis}}, pages = {{551–559}}, publisher = {{Association for Computing Machinery}}, title = {{{Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem}}}, doi = {{10.1145/3377930.3390168}}, year = {{2020}}, } @inproceedings{48840, abstract = {{Research has shown that for many single-objective graph problems where optimum solutions are composed of low weight sub-graphs, such as the minimum spanning tree problem (MST), mutation operators favoring low weight edges show superior performance. Intuitively, similar observations should hold for multi-criteria variants of such problems. In this work, we focus on the multi-criteria MST problem. A thorough experimental study is conducted where we estimate the probability of edges being part of non-dominated spanning trees as a function of the edges’ non-domination level or domination count, respectively. Building on gained insights, we propose several biased one-edge-exchange mutation operators that differ in the used edge-selection probability distribution (biased towards edges of low rank). Our empirical analysis shows that among different graph types (dense and sparse) and edge weight types (both uniformly random and combinations of Euclidean and uniformly random) biased edge-selection strategies perform superior in contrast to the baseline uniform edge-selection. Our findings are in particular strong for dense graphs.}}, author = {{Bossek, Jakob and Grimme, Christian and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-6111-8}}, keywords = {{biased mutation, combinatorial optimization, minimum spanning tree, multi-objective optimization}}, pages = {{516–523}}, publisher = {{Association for Computing Machinery}}, title = {{{On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem}}}, doi = {{10.1145/3321707.3321818}}, year = {{2019}}, } @inproceedings{17651, abstract = {{Consider mitigating the effects of denial of service or of malicious traffic in networks by deleting edges. Edge deletion reduces the DoS or the number of the malicious flows, but it also inadvertently removes some of the desired flows. To model this important problem, we formulate two problems: (1) remove all the undesirable flows while minimizing the damage to the desirable ones and (2) balance removing the undesirable flows and not removing too many of the desirable flows. We prove these problems are equivalent to important theoretical problems, thereby being important not only practically but also theoretically, and very hard to approximate in a general network. We employ reductions to nonetheless approximate the problem and also provide a greedy approximation. When the network is a tree, the problems are still MAX SNP-hard, but we provide a greedy-based 2l-approximation algorithm, where l is the longest desirable flow. We also provide an algorithm, approximating the first and the second problem within {\$}{\$}2 {\backslash}sqrt{\{} 2{\backslash}left| E {\backslash}right| {\}}{\$}{\$}and {\$}{\$}2 {\backslash}sqrt{\{}2 ({\backslash}left| E {\backslash}right| + {\backslash}left| {\backslash}text {\{}undesirable flows{\}} {\backslash}right| ){\}}{\$}{\$}, respectively, where E is the set of the edges of the network. We also provide a fixed-parameter tractable (FPT) algorithm. Finally, if the tree has a root such that every flow in the tree flows on the path from the root to a leaf, we solve the problem exactly using dynamic programming.}}, author = {{Polevoy, Gleb and Trajanovski, Stojan and Grosso, Paola and de Laat, Cees}}, booktitle = {{Combinatorial Optimization and Applications}}, editor = {{Kim, Donghyun and Uma, R. N. and Zelikovsky, Alexander}}, isbn = {{978-3-030-04651-4}}, keywords = {{flow, Red-Blue Set Cover, Positive-Negative Partial Set Cover, approximation, tree, MAX SNP-hard, root, leaf, dynamic programming, FPT}}, pages = {{217--232}}, publisher = {{Springer International Publishing}}, title = {{{Removing Undesirable Flows by Edge Deletion}}}, year = {{2018}}, } @inproceedings{10598, abstract = {{Approximate computing has become a very popular design strategy that exploits error resilient computations to achieve higher performance and energy efficiency. Automated synthesis of approximate circuits is performed via functional approximation, in which various parts of the target circuit are extensively examined with a library of approximate components/transformations to trade off the functional accuracy and computational budget (i.e., power). However, as the number of possible approximate transformations increases, traditional search techniques suffer from a combinatorial explosion due to the large branching factor. In this work, we present a comprehensive framework for automated synthesis of approximate circuits from either structural or behavioral descriptions. We adapt the Monte Carlo Tree Search (MCTS), as a stochastic search technique, to deal with the large design space exploration, which enables a broader range of potential possible approximations through lightweight random simulations. The proposed framework is able to recognize the design Pareto set even with low computational budgets. Experimental results highlight the capabilities of the proposed synthesis framework by resulting in up to 61.69% energy saving while maintaining the predefined quality constraints.}}, author = {{Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan and Platzner, Marco}}, booktitle = {{26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)}}, keywords = {{Approximate computing, High-level synthesis, Accuracy, Monte-Carlo tree search, Circuit simulation}}, pages = {{219--224}}, title = {{{An MCTS-based Framework for Synthesis of Approximate Circuits}}}, doi = {{10.1109/VLSI-SoC.2018.8645026}}, year = {{2018}}, } @inproceedings{37037, abstract = {{Today we can identify a big gap between requirement specification and the generation of test environments. This article extends the Classification Tree Method for Embedded Systems (CTM/ES) to fill this gap by new concepts for the precise specification of stimuli for operational ranges of continuous control systems. It introduces novel means for continuous acceptance criteria definition and for functional coverage definition.}}, author = {{Krupp, Alexander and Müller, Wolfgang}}, booktitle = {{Proceedings of DATE’10}}, keywords = {{System testing, Automatic testing, Object oriented modeling, Classification tree analysis, Automotive engineering, Mathematical model, Embedded system, Control systems, Electronic equipment testing, Software testing}}, location = {{Dresden}}, publisher = {{IEEE}}, title = {{{A Systematic Approach to Combined HW/SW System Test}}}, doi = {{10.1109/DATE.2010.5457186}}, year = {{2010}}, } @inproceedings{38784, abstract = {{This article presents the classification tree method for functional verification to close the gap from the specification of a test plan to SystemVerilog (Chandra and Chakrabarty, 2001) test bench generation. Our method supports the systematic development of test configurations and is based on the classification tree method for embedded systems (CTM/ES) (Chakrabarty et al., 2000) extending CTM/ES for random test generation as well as for functional coverage and property specification}}, author = {{Krupp, Alexander and Müller, Wolfgang}}, booktitle = {{Proceedings of the Design Automation & Test in Europe Conference}}, isbn = {{3-9810801-1-4}}, keywords = {{Classification tree analysis, System testing, Embedded system, Safety, Automatic testing, Automation}}, publisher = {{IEEE}}, title = {{{Classification Trees for Functional Coverage and Random Test Generation}}}, doi = {{10.1109/DATE.2006.243902}}, year = {{2006}}, }