Johan Hagelbäck
Senior lecturer
Department of Computer Science and Media Technology
Faculty of Technology
+46480497707
14075, Hus Magna, Kalmar
Senior Lecturer in Computer Science with a specialty in artificial intelligence and machine learning. My research is about applying AI, mainly machine learning, on different application areas in academia and industry.
Teaching
Program manager for Högskoleingenjör i Mjukvaruteknik. Examiner for degree projects at bachelor level in computer science.
Publications
Article in journal (Refereed)
- Jercic, P., Wen, W., Hagelbäck, J., Sundstedt, V. (2018). The Effect of Emotions and Social Behavior on Performance in a Collaborative Serious Game Between Humans and Autonomous Robots. International Journal of Social Robotics. 10. 115-129.
- Hagelbäck, J. (2016). Hybrid pathfinding in StarCraft. IEEE Transactions on Computational Intelligence and AI in Games. 8. 319-324.
- Togelius, J., Preuss, M., Beume, N., Wessing, S., Hagelbäck, J., et al. (2013). Controllable procedural map generation via multiobjective evolution. Genetic Programming and Evolvable Machines. 14. 245-277.
- Hagelbäck, J., Johansson, S.J. (2009). A Multiagent Potential Field-Based Bot for Real-Time Strategy Games. International Journal of Computer Games Technology. 2009.
Conference paper (Refereed)
- Qureshi, S., Hagelbäck, J., Iqbal, S.M.Z., Javaid, H., Lindley, C. (2018). Evaluation of Classifiers for Emotion Detection while Performing Physical and Visual Tasks : Tower of Hanoi and IAPS. Intelligent Systems and Applications. IntelliSys 2018 : Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1. 347-363.
- Golub, K., Hagelbäck, J., Ardö, A. (2018). Automatic classification using DDC on the Swedish Union Catalogue. Proceedings of the 18th European Networked Knowledge Organization Systems (NKOS 2018) Workshop, Porto, Portugal, September 13, 2018. 4-16.
- Jerčić, P., Hagelbäck, J., Lindley, C. (2018). Physiological Affect and Performance in a Collaborative Serious Game Between Humans and an Autonomous Robot. Entertainment Computing – ICEC 2018 : 17th IFIP TC 14 International Conference, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 17–20, 2018. 127-138.
- Sohaib, A.T., Qureshi, S., Hagelbäck, J., Hilborn, O., Jercic, P. (2013). Evaluating classifiers for Emotion Recognition using EEG. Foundations of Augmented Cognition : 7th International Conference, AC 2013, Held as Part of HCI International 2013, Las Vegas, NV, USA, July 21-26, 2013. Proceedings. 492-501.
- Preuss, M., Kozakowski, D., Hagelbäck, J., Trautmann, H. (2013). Reactive Strategy Choice in StarCraft by Means of Fuzzy Control. 2013 IEEE Conference on Computational Intelligence in Games (CIG). 1-8.
- Hagelbäck, J. (2012). Potential-Field Based navigation in StarCraft. 2012 IEEE Conference on Computational Intelligence and Games (CIG). 388-393.
- Togelius, J., Preuss, M., Beume, N., Wessing, S., Hagelbäck, J., et al. (2010). Multiobjective Exploration of the StarCraft Map Space. 2010 IEEE Symposium on Computational Intelligence and Games (CIG). 265-272.
- Hagelbäck, J., Johansson, S.J. (2010). A Study on Human like Characteristics in Real Time Strategy Games. 2010 IEEE Symposium on Computational Intelligence and Games (CIG). 139-145.
- Hagelbäck, J., Johansson, S.J. (2009). A Multi-agent Potential Field based bot for a Full RTS Game Scenario. .
- Hagelbäck, J., Johansson, S.J. (2009). Measuring player experience on runtime dynamic difficulty scaling in an RTS game. IEEE Symposium on Computational Intelligence and Games, 2009 : CIG 2009. 46-52.
- Hagelbäck, J., Johansson, S.J. (2008). Dealing with Fog of War in a Real Time Strategy Game Environment. IEEE Symposium On Computational Intelligence and Games, 2008. CIG '08. 55-62.
- Hagelbäck, J., Johansson, S.J. (2008). Using Multi-Agent Potential Fields in Real-Time Strategy Games. Seventh International Conference on Autonomous Agents and Multi-agent Systems (AAMAS), 12-16, 2008, Estoril. 631-638.
- Hagelbäck, J., Johansson, S.J. (2008). The Rise of Potential Fields in Real Time Strategy bots. Artificial Intelligence and Interactive Digital Entertainment (AIIDE), Stanford.
- Davidsson, P., Hagelbäck, J., Svensson, K. (2005). Comparing approaches to predict transmembrane domains in protein sequences. ProceedingSAC '05 Proceedings of the 2005 ACM symposium on Applied computing. 185-189.
Chapter in book (Refereed)
- Hagelbäck, J., Hilborn, O., Jercic, P., Johansson, S.J., Lindley, C.A., et al. (2014). Psychophysiological Interaction and Empathic Cognition for Human-robot Cooperative Work (PsyIntEC). Gearing Up and Accelerating Cross‐fertilization between Academic and Industrial Robotics Research in Europe : Technology Transfer Experiments from the ECHORD Project. Springer. 283-299.
Conference paper (Other academic)
- Golub, K., Hagelbäck, J., Ardö, A. (2019). Automatic classification Using DDC on the Swedish Union Catalogue. European DDC Users Group, EDUG, Annual Meeting 9-10 May 2019 : National Library of Sweden, Stockholm, Sweden.
- Hagelbäck, J., Johansson, S.J. (2008). Demonstration of Multi-agent Potential Fields in Real-time Strategy Game. Seventh International Conference on Autonomous Agents and Multi-agent Systems (AAMAS), May 12-16, 2008, Estoril. 1687-1688.
Doctoral thesis, monograph (Other academic)
- Hagelbäck, J. (2012). Multi-Agent Potential Field based Architectures for Real-Time Strategy Game Bots. Doctoral Thesis. Karlskrona, Blekinge Tekniska Högskola.
Doctoral thesis, monograph (Other academic)
- Hagelbäck, J. (2009). A Multi-Agent Potential Field Based Approach for Real-Time Strategy Game Bots. Licentiate Thesis. Karlskrona, Blekinge Tekniska Högskola. 81.