Current research is based on Multi-Agent Systems and Nature Inspired Artificial Intelligence. My masters research project was based on the application of Symbiotic Game Agents to the problem of Adaptive Game AI and Dynamic Difficulty Balancing. My PhD research is based on exploring the use of Immunologically Inspired Artificial Intelligence in conjunction with the Generative Adversarial approach to Machine Learning in order to realize an effective and competitive model for the protection of Industrial IoT systems. I believe that as we are in the age of digitization and extensive automation, it is also important to not forget the importance of securing the very systems we build to improve our livelihoods.
- Philezwini Sithungu, S. and Marie Ehlers, E., 2020, November. A Reinforcement Learning-Based Classification Symbiont Agent for Dynamic Difficulty Balancing. In 2020 The 3rd International Conference on Computational Intelligence and Intelligent Systems (pp. 15-23).
- Sithungu, S.P. and Ehlers, E.M., 2020, July. Adaptive Game AI-Based Dynamic Difficulty Scaling via the Symbiotic Game Agent. In International Conference on Intelligent Information Processing (pp. 107-117). Springer, Cham.
- Sithungu, S.P., Coulter, D.A. and Ehlers, E.M., 2019, November. Using Genetic Programming and Decision Trees for Team Evolution. In Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems (pp. 28-39).
- Sithungu, S. and Van der Haar, D., 2019, June. Real-Time Age Detection Using a Convolutional Neural Network. In International Conference on Business Information Systems (pp. 245-256). Springer, Cham.