@inproceedings{46311, abstract = {{In this work we examine the inner mechanisms of the recently developed sophisticated local search procedure SOMOGSA. This method solves multimodal single-objective continuous optimization problems by first expanding the problem with an additional objective (e.g., a sphere function) to the bi-objective space, and subsequently exploiting local structures and ridges of the resulting landscapes. Our study particularly focusses on the sensitivity of this multiobjectivization approach w.r.t. (i) the parametrization of the artificial second objective, as well as (ii) the position of the initial starting points in the search space. As SOMOGSA is a modular framework for encapsulating local search, we integrate Gradient and Nelder-Mead local search (as optimizers in the respective module) and compare the performance of the resulting hybrid local search to their original single-objective counterparts. We show that the SOMOGSA framework can significantly boost local search by multiobjectivization. Combined with more sophisticated local search and metaheuristics this may help in solving highly multimodal optimization problems in future.}}, author = {{Aspar, Pelin and Kerschke, Pascal and Steinhoff, Vera and Trautmann, Heike and Grimme, Christian}}, booktitle = {{Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings}}, editor = {{et al. Ishibuchi, H.}}, pages = {{311–322}}, publisher = {{Springer}}, title = {{{Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization}}}, doi = {{10.1007/978-3-030-72062-9_25}}, year = {{2021}}, } @article{46317, abstract = {{One of the most significant recent technological developments concerns the development and implementation of ‘intelligent machines’ that draw on recent advances in artificial intelligence (AI) and robotics. However, there are growing tensions between human freedoms and machine controls. This article reports the findings of a workshop that investigated the application of the principles of human freedom throughout intelligent machine development and use. Forty IS researchers from ten different countries discussed four contemporary AI and humanity issues and the most relevant IS domain challenges. This article summarizes their experiences and opinions regarding four AI and humanity themes: Crime & conflict, Jobs, Attention, and Wellbeing. The outcomes of the workshop discussions identify three attributes of humanity that need preservation: a critique of the design and application of AI, and the intelligent machines it can create; human involvement in the loop of intelligent machine decision-making processes; and the ability to interpret and explain intelligent machine decision-making processes. The article provides an agenda for future AI and humanity research.}}, author = {{Coombs, Crispin and Stacey, Patrick and Kawalek, Peter and Simeonova, Boyka and Becker, Jörg and Bergener, Katrin and Carvalho, João Álvaro and Fantinato, Marcelo and Garmann-Johnsen, Niels F. and Grimme, Christian and Stein, Armin and Trautmann, Heike}}, journal = {{International Journal of Information Management}}, title = {{{What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective}}}, doi = {{10.1016/j.ijinfomgt.2021.102311}}, volume = {{58}}, year = {{2021}}, } @inproceedings{21272, author = {{Ködding, Patrick and Dumitrescu, Roman}}, booktitle = {{Digitalisisierung souverän gestalten}}, editor = {{Hartmann, Ernst A.}}, pages = {{pp. 59--73}}, publisher = {{Springer Vieweg}}, title = {{{Forschungsfelder für Künstliche Intelligenz in der strategischen Produktplanung}}}, year = {{2021}}, } @inproceedings{48286, author = {{Habernal, Ivan}}, booktitle = {{Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing}}, publisher = {{Association for Computational Linguistics}}, title = {{{When differential privacy meets NLP: The devil is in the detail}}}, doi = {{10.18653/v1/2021.emnlp-main.114}}, year = {{2021}}, } @article{24456, abstract = {{One objective of current research in explainable intelligent systems is to implement social aspects in order to increase the relevance of explanations. In this paper, we argue that a novel conceptual framework is needed to overcome shortcomings of existing AI systems with little attention to processes of interaction and learning. Drawing from research in interaction and development, we first outline the novel conceptual framework that pushes the design of AI systems toward true interactivity with an emphasis on the role of the partner and social relevance. We propose that AI systems will be able to provide a meaningful and relevant explanation only if the process of explaining is extended to active contribution of both partners that brings about dynamics that is modulated by different levels of analysis. Accordingly, our conceptual framework comprises monitoring and scaffolding as key concepts and claims that the process of explaining is not only modulated by the interaction between explainee and explainer but is embedded into a larger social context in which conventionalized and routinized behaviors are established. We discuss our conceptual framework in relation to the established objectives of transparency and autonomy that are raised for the design of explainable AI systems currently.}}, author = {{Rohlfing, Katharina J. and Cimiano, Philipp and Scharlau, Ingrid and Matzner, Tobias and Buhl, Heike M. and Buschmeier, Hendrik and Esposito, Elena and Grimminger, Angela and Hammer, Barbara and Haeb-Umbach, Reinhold and Horwath, Ilona and Hüllermeier, Eyke and Kern, Friederike and Kopp, Stefan and Thommes, Kirsten and Ngonga Ngomo, Axel-Cyrille and Schulte, Carsten and Wachsmuth, Henning and Wagner, Petra and Wrede, Britta}}, issn = {{2379-8920}}, journal = {{IEEE Transactions on Cognitive and Developmental Systems}}, keywords = {{Explainability, process ofexplaining andunderstanding, explainable artificial systems}}, number = {{3}}, pages = {{717--728}}, title = {{{Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems}}}, doi = {{10.1109/tcds.2020.3044366}}, volume = {{13}}, year = {{2021}}, } @inproceedings{48853, abstract = {{In practise, it is often desirable to provide the decision-maker with a rich set of diverse solutions of decent quality instead of just a single solution. In this paper we study evolutionary diversity optimization for the knapsack problem (KP). Our goal is to evolve a population of solutions that all have a profit of at least (1 - {$ϵ$}) {$\cdot$} OPT, where OPT is the value of an optimal solution. Furthermore, they should differ in structure with respect to an entropy-based diversity measure. To this end we propose a simple ({$\mu$} + 1)-EA with initial approximate solutions calculated by a well-known FPTAS for the KP. We investigate the effect of different standard mutation operators and introduce biased mutation and crossover which puts strong probability on flipping bits of low and/or high frequency within the population. An experimental study on different instances and settings shows that the proposed mutation operators in most cases perform slightly inferior in the long term, but show strong benefits if the number of function evaluations is severely limited.}}, author = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-8350-9}}, keywords = {{evolutionary algorithms, evolutionary diversity optimization, knapsack problem, tailored operators}}, pages = {{556–564}}, publisher = {{Association for Computing Machinery}}, title = {{{Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms}}}, doi = {{10.1145/3449639.3459364}}, year = {{2021}}, } @inproceedings{48855, abstract = {{Computing sets of high quality solutions has gained increasing interest in recent years. In this paper, we investigate how to obtain sets of optimal solutions for the classical knapsack problem. We present an algorithm to count exactly the number of optima to a zero-one knapsack problem instance. In addition, we show how to efficiently sample uniformly at random from the set of all global optima. In our experimental study, we investigate how the number of optima develops for classical random benchmark instances dependent on their generator parameters. We find that the number of global optima can increase exponentially for practically relevant classes of instances with correlated weights and profits which poses a justification for the considered exact counting problem.}}, author = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Learning and Intelligent Optimization}}, isbn = {{978-3-030-92120-0}}, keywords = {{Dynamic programming, Exact counting, Sampling, Zero-one knapsack problem}}, pages = {{40–54}}, publisher = {{Springer-Verlag}}, title = {{{Exact Counting and~Sampling of Optima for the Knapsack Problem}}}, doi = {{10.1007/978-3-030-92121-7_4}}, year = {{2021}}, } @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}}, } @inbook{48862, abstract = {{Most runtime analyses of randomised search heuristics focus on the expected number of function evaluations to find a unique global optimum. We ask a fundamental question: if additional search points are declared optimal, or declared as desirable target points, do these additional optima speed up evolutionary algorithms? More formally, we analyse the expected hitting time of a target set OPT {$\cup$} S where S is a set of non-optimal search points and OPT is the set of optima and compare it to the expected hitting time of OPT. We show that the answer to our question depends on the number and placement of search points in S. For all black-box algorithms and all fitness functions we show that, if additional optima are placed randomly, even an exponential number of optima has a negligible effect on the expected optimisation time. Considering Hamming balls around all global optima gives an easier target for some algorithms and functions and can shift the phase transition with respect to offspring population sizes in the (1,{$\lambda$}) EA on One-Max. Finally, on functions where search trajectories typically join in a single search point, turning one search point into an optimum drastically reduces the expected optimisation time.}}, author = {{Bossek, Jakob and Sudholt, Dirk}}, booktitle = {{Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}}, isbn = {{978-1-4503-8352-3}}, keywords = {{evolutionary algorithms, pseudo-boolean functions, runtime analysis, theory}}, pages = {{1–11}}, publisher = {{Association for Computing Machinery}}, title = {{{Do Additional Optima Speed up Evolutionary Algorithms?}}}, year = {{2021}}, } @inbook{48881, abstract = {{Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) a k-nearest neighbor graph (NNG) transformation of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-NNGs of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. Eventually, a proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.}}, author = {{Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}}, booktitle = {{Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}}, isbn = {{978-1-4503-8352-3}}, keywords = {{automated algorithm selection, graph theory, instance features, normalization, traveling salesperson problem (TSP)}}, pages = {{1–15}}, publisher = {{Association for Computing Machinery}}, title = {{{On the Potential of Normalized TSP Features for Automated Algorithm Selection}}}, year = {{2021}}, } @inproceedings{48876, abstract = {{In recent years, Evolutionary Algorithms (EAs) have frequently been adopted to evolve instances for optimization problems that pose difficulties for one algorithm while being rather easy for a competitor and vice versa. Typically, this is achieved by either minimizing or maximizing the performance difference or ratio which serves as the fitness function. Repeating this process is useful to gain insights into strengths/weaknesses of certain algorithms or to build a set of instances with strong performance differences as a foundation for automatic per-instance algorithm selection or configuration. We contribute to this branch of research by proposing fitness-functions to evolve instances that show large performance differences for more than just two algorithms simultaneously. As a proof-of-principle, we evolve instances of the multi-component Traveling Thief Problem (TTP) for three incomplete TTP-solvers. Our results point out that our strategies are promising, but unsurprisingly their success strongly relies on the algorithms’ performance complementarity.}}, author = {{Bossek, Jakob and Wagner, Markus}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-8351-6}}, keywords = {{evolutionary algorithms, evolving instances, fitness function, instance hardness, traveling thief problem (TTP)}}, pages = {{1423–1432}}, publisher = {{Association for Computing Machinery}}, title = {{{Generating Instances with Performance Differences for More than Just Two Algorithms}}}, doi = {{10.1145/3449726.3463165}}, year = {{2021}}, } @inproceedings{48893, abstract = {{Computing diverse sets of high-quality solutions has gained increasing attention among the evolutionary computation community in recent years. It allows practitioners to choose from a set of high-quality alternatives. In this paper, we employ a population diversity measure, called the high-order entropy measure, in an evolutionary algorithm to compute a diverse set of high-quality solutions for the Traveling Salesperson Problem. In contrast to previous studies, our approach allows diversifying segments of tours containing several edges based on the entropy measure. We examine the resulting evolutionary diversity optimisation approach precisely in terms of the final set of solutions and theoretical properties. Experimental results show significant improvements compared to a recently proposed edge-based diversity optimisation approach when working with a large population of solutions or long segments.}}, author = {{Nikfarjam, Adel and Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-8350-9}}, keywords = {{evolutionary algorithms, evolutionary diversity optimisation, high-order entropy, traveling salesperson problem}}, pages = {{600–608}}, publisher = {{Association for Computing Machinery}}, title = {{{Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem}}}, doi = {{10.1145/3449639.3459384}}, year = {{2021}}, } @inproceedings{48891, abstract = {{Submodular functions allow to model many real-world optimisation problems. This paper introduces approaches for computing diverse sets of high quality solutions for submodular optimisation problems with uniform and knapsack constraints. We first present diversifying greedy sampling approaches and analyse them with respect to the diversity measured by entropy and the approximation quality of the obtained solutions. Afterwards, we introduce an evolutionary diversity optimisation (EDO) approach to further improve diversity of the set of solutions. We carry out experimental investigations on popular submodular benchmark problems and analyse trade-offs in terms of solution quality and diversity of the resulting solution sets.}}, author = {{Neumann, Aneta and 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 optimisation, sub-modular functions}}, pages = {{261–269}}, publisher = {{Association for Computing Machinery}}, title = {{{Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions}}}, doi = {{10.1145/3449639.3459385}}, year = {{2021}}, } @inbook{48892, abstract = {{Evolutionary algorithms based on edge assembly crossover (EAX) constitute some of the best performing incomplete solvers for the well-known traveling salesperson problem (TSP). Often, it is desirable to compute not just a single solution for a given problem, but a diverse set of high quality solutions from which a decision maker can choose one for implementation. Currently, there are only a few approaches for computing a diverse solution set for the TSP. Furthermore, almost all of them assume that the optimal solution is known. In this paper, we introduce evolutionary diversity optimisation (EDO) approaches for the TSP that find a diverse set of tours when the optimal tour is known or unknown. We show how to adopt EAX to not only find a high-quality solution but also to maximise the diversity of the population. The resulting EAX-based EDO approach, termed EAX-EDO is capable of obtaining diverse high-quality tours when the optimal solution for the TSP is known or unknown. A comparison to existing approaches shows that they are clearly outperformed by EAX-EDO.}}, author = {{Nikfarjam, Adel and Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms}}, isbn = {{978-1-4503-8352-3}}, keywords = {{edge assembly crossover (EAX), evolutionary algorithms, evolutionary diversity optimisation (EDO), traveling salesperson problem (TSP)}}, pages = {{1–11}}, publisher = {{Association for Computing Machinery}}, title = {{{Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation}}}, year = {{2021}}, } @article{48854, abstract = {{We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical vertex coloring problem on graphs and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. The (1+1) Evolutionary Algorithm and RLS operate in a setting where the number of colors is bounded and we are minimizing the number of conflicts. Iterated local search algorithms use an unbounded color palette and aim to use the smallest colors and, consequently, the smallest number of colors. We identify classes of bipartite graphs where reoptimization is as hard as or even harder than optimization from scratch, i.e., starting with a random initialization. Even adding a single edge can lead to hard symmetry problems. However, graph classes that are hard for one algorithm turn out to be easy for others. In most cases our bounds show that reoptimization is faster than optimizing from scratch. We further show that tailoring mutation operators to parts of the graph where changes have occurred can significantly reduce the expected reoptimization time. In most settings the expected reoptimization time for such tailored algorithms is linear in the number of added edges. However, tailored algorithms cannot prevent exponential times in settings where the original algorithm is inefficient.}}, author = {{Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}}, issn = {{0178-4617}}, journal = {{Algorithmica}}, keywords = {{Dynamic optimization, Evolutionary algorithms, Running time analysis}}, number = {{10}}, pages = {{3148–3179}}, title = {{{Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem}}}, doi = {{10.1007/s00453-021-00838-3}}, volume = {{83}}, year = {{2021}}, } @inproceedings{29937, author = {{Karp, Martin and Podobas, Artur and Jansson, Niclas and Kenter, Tobias and Plessl, Christian and Schlatter, Philipp and Markidis, Stefano}}, booktitle = {{2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS)}}, publisher = {{IEEE}}, title = {{{High-Performance Spectral Element Methods on Field-Programmable Gate Arrays : Implementation, Evaluation, and Future Projection}}}, doi = {{10.1109/ipdps49936.2021.00116}}, year = {{2021}}, } @inproceedings{52927, author = {{Gil, Oliver Fernández and Turhan, Anni-Yasmin}}, booktitle = {{Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021}}, pages = {{6340–6348}}, publisher = {{AAAI Press}}, title = {{{Answering Regular Path Queries Under Approximate Semantics in Lightweight Description Logics}}}, doi = {{10.1609/AAAI.V35I7.16787}}, year = {{2021}}, } @article{52926, author = {{Dubslaff, Clemens and Koopmann, Patrick and Turhan, Anni-Yasmin}}, journal = {{Formal Aspects Comput.}}, number = {{6}}, pages = {{885–921}}, title = {{{Enhancing Probabilistic Model Checking with Ontologies}}}, doi = {{10.1007/S00165-021-00549-0}}, volume = {{33}}, year = {{2021}}, } @inproceedings{22285, author = {{Biermeier, Kai and Yigitbas, Enes and Weidmann, Nils and Engels, Gregor}}, booktitle = {{Proceedings of the International Workshop on Human-Centered Software Engineering for Changing Contexts of Use }}, title = {{{Ensuring User Interface Adaptation Consistency through Triple Graph Grammers}}}, year = {{2021}}, } @inproceedings{46194, author = {{Kenter, Tobias and Shambhu, Adesh and Faghih-Naini, Sara and Aizinger, Vadym}}, booktitle = {{Proceedings of the Platform for Advanced Scientific Computing Conference (PASC)}}, publisher = {{ACM}}, title = {{{Algorithm-hardware co-design of a discontinuous Galerkin shallow-water model for a dataflow architecture on FPGA}}}, doi = {{10.1145/3468267.3470617}}, year = {{2021}}, }