Hanne, Thomas
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Hanne, Thomas
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- PublikationA game teaching population based optimization using teaching-learning-based optimization(2019) Pustulka, Elzbieta; Hanne, Thomas; Richard, Wetzel; Egemen, Kaba; Benjamin, Adriaensen; Stefan, Eggenschwiler; Adriaensen, Benjamin [in: GSGS'19. 4th Gamification & Serious Game Symposium]We want to lower the entry barrier to optimization courses. To that aim, we deployed a game prototype and tested it with students who had no previous optimization experience. We found out that the prototype led to an increased student motivation, an intuitive understanding of the principles of optimization, and a strong interaction in a team. We will build on this experience to develop further games for classroom use.04B - Beitrag Konferenzschrift
- PublikationAn experiment with an optimization game(2019) Pustulka, Elzbieta; Hanne, Thomas; Adriaensen, Benjamin; Eggenschwiler, Stefan; Kaba, Egemen; Wetzel, Richard; Blashki, Katherine; Xiao, Yingcai [in: IADIS International Conference Interfaces and Human Computer Interaction 2019 (part of MCCSIS 2019)]We aim to improve the teaching of the principles of optimization, including computational intelligence (CI), to a mixed audience of business and computer science students. Our students do not always have sufficient programming or mathematics experience and may be put off by the expected difficulty of the course. In this context we are testing the potential of games in teaching. We deployed a game prototype (design probe) and found out that the prototype led to increased student motivation, intuitive understanding of the principles of optimization, and strong interaction in a team. Ultimately, with the future work we sketch out, this novel approach could improve the learning and understanding of optimization algorithms and CI in general, contributing to the future of Explainable AI (XAI).04B - Beitrag Konferenzschrift
- PublikationOptimization of multi-robot sumo fight simulation by a genetic algorithm to identify dominant robot capabilities(2019) Lehner, Joël Enrico; Dornberger, Rolf; Simic, Radovan; Hanne, Thomas [in: 2019 IEEE Congress on Evolutionary Computation (CEC 2019). Proceedings]This paper analyzes the multirobot sumo fight simulation. This simulation is based on a computational model of several sumo fighters, which physically interact while trying to move the opponent out of the arena (lost fight). The problem is optimized using a genetic algorithm (GA), where the capabilities of not only one particular robot but of all robots simultaneously are improved. In this particular problem setup, the problem definition changes depending on the optimization path, because all robots also get better, competing against each other. The influence of different operators of the GA is investigated and compared. This paper raises the questions, which genetically controlled capabilities (e.g. size, speed) are dominant over time and how they can be identified by a sensitivity analysis using a GA. The results shed light on which parameters are dominant. This experiment typically opens up interesting fields of further research, especially about how to address optimization problems, where the optimization process influences the search space and how to eliminate the factor of randomness.04B - Beitrag Konferenzschrift
- PublikationMultilingual Sentiment Analysis for a Swiss Gig(27.08.2018) Pustulka, Elzbieta; Hanne, Thomas; Blumer, Eliane; Frieder, Manuel; Wong, Ka Chun [in: 6th International Symposium on Computational and Business Intelligence (ISCBI 2018)]We are developing a multilingual sentiment analysis solution for a Swiss human resource company working in the gig sector. To examine the feasibility of using machine learning in this context, we carried out three sentiment assignment experiments. As test data we use 963 hand annotated comments made by workers and their employers. Our baseline, machine learning (ML) on Twitter, had an accuracy of 0.77 with the Matthews correlation coefficient (MCC) of 0.32. A hybrid solution, Semantria from Lexalytics, had an accuracy of 0.8 with MCC of 0.42, while a tenfold cross-validation on the gig data yielded the accuracy of 0.87, F1 score 0.91, and MCC 0.65. Our solution did not require language assignment or stemming and used standard ML software. This shows that with more training data and some feature engineering, an industrial strength solution to this problem should be possible.04B - Beitrag Konferenzschrift
- PublikationComparison of a real kilobot robot implementation with its computer simulation focussing on target-searching algorithms(IEEE, 2018) Zhong, Jia; Umamaheshwarappa, Ramya Ramedeverahalli; Dornberger, Rolf; Hanne, Thomas [in: 2018 International Conference on Intelligent Autonomous Systems (ICoIAS’2018)]This paper presents the functionality and quality of the implementation of a search- and target-surrounding swarm robotic algorithm, which was developed and tested in the simulator V-REP, on actually running Kilobots. Ten Kilobots were used for the experiment where one Kilobot acts as target and nine Kilobots act as searchers. The algorithm allows the searchers to disperse to find the target, to avoid collisions with other searchers, to orbit other searchers, which are closer to the target, and to finally surround the target, once it is found. The results of the implementation using actual, real swarm robots are compared with the results of the computer simulation. Differences between simulation and real robot implementation are discussed. In particular, issues associated with the limitation in the Kilobots’ communication capability and their implications on the algorithm are investigated.04B - Beitrag Konferenzschrift
- PublikationOptimal learning rate and neighborhood radius of Kohonen's self-organizing map for solving the travelling salesman problem(2018) Mersiovsky, Tabea; Thekkottil, Abhilash; Hanne, Thomas; Dornberger, Rolf [in: Proceedings of the 2nd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence]The Travelling Salesman Problem (TSP) is one of the well-studied classic combinatorial optimization problems and proved to be a non-deterministic polynomial-time (NP) hard problem. Kohonen's self-organizing map (SOM) is a type of artificial neural network, which can be applied on the TSP. The purpose of the algorithm is to adapt a special network to a set of unorganized and unlabeled data so that it can be used for clustering and simple classification tasks. In this paper, we study the effect of changing the parameters in the SOM algorithm to solve the TSP. The focus of the parameter investigation lies on the influence of changes in the SOM learning rate and neighborhood radius as well as on the number of iterations in TSP problems with varying number of cities. Thus, the investigation is based on various problem instances as well as on different parameter settings of the SOM, which are compared with each other and discussed. The results are additionally compared with the nature inspired ant colony optimization (ACO) algorithm. As a result, it is proved that with the right parameter setting the SOM generated result is improved and that it is superior to the ACO algorithm.04B - Beitrag Konferenzschrift
- PublikationGig work business process improvement(CPS, 2018) Pustulka, Elzbieta; Telesko, Rainer; Hanne, Thomas; Wong, Ka Chun [in: ISCBI 2018. 6th International Symposium on Computational and Business Intelligence. Proceedings]We collaborate with a gig work platform company (GPC) in Switzerland. The project aims to improve the business by influencing process management within the GPC, providing automated matching of jobs to workers, improving worker acquisition and worker commitment, and particularly focusing on the prevention of no shows. One expects to achieve financial, organizational and efficiency gains. As research tools we use a combination of text mining and sentiment analysis, Business Process Modeling and Notation (BPMN), interviews with workers and employers, and the design of sociotechnical improvements to the process, including platform improvements and prototypes. Here, we focus on the successful combination of BPMN modelling with sentiment analysis in the identification of problems and generation of ideas for future modifications to the business processes.04B - Beitrag Konferenzschrift
- PublikationA Novel Backup Path Planning Approach with ACO(08/2017) Meier, Danni; Tullumi, Ilir; Stauffer, Yannick; Dornberger, Rolf; Hanne, Thomas [in: 5th International Symposium on Computational and Business Intelligence (ISCBI)]04B - Beitrag Konferenzschrift
- PublikationFacility Layout Planning Using Fuzzy Closeness Computation and a Genetic Algorithm(09.12.2015) Menon, Dilip; Zwimpfer, Cédric; Hanne, Thomas; Dornberger, Rolf [in: 3rd International Symposium on Computational and Business Intelligence (ISCBI 2015)]04B - Beitrag Konferenzschrift
- PublikationAnalysis of Chaotic Maps Applied to Kohonen Self-organizing Maps for the Traveling Salesman Problem(05/2015) Stauffer, Michael; Ryter, Remo; Hanne, Thomas; Dornberger, Rolf [in: Proceedings of the annual IEEE Congress on Evolutionary Computation (IEEE CEC 2015)]04B - Beitrag Konferenzschrift