Multi-agent systems are a set of techniques and models that are used to simulate the interaction of different typically intelligent entities, and to solve complex computational problems. The group has extensive expertise in using multi-agent systems and agent-based models in applications related to healthcare and smart cities.
Areas of Activity
- Mobile software agents
- Multi-agent computer network routing protocols
- Agent-based machine learning systems
- Agent-based modelling
Staff working in this group
- Fatima Abdallah Agent-based Modelling to Simulate Energy Consumption in Residential Buildings
- Diana Haidar Opportunistic Machine Learning Methods for Insider Threat Detection
- Mona Demaidi Ontology-based Personalised Feedback in Virtual Learning Environments
- Hossein Ghomeshi An Evolutionary Approach to Seamless Adaptation to Concept Drifts in High Velocity Big Data
- Asim Majeed Modelling Mobile Edge Computing Systems for Mobile Crowdsensing Applications
- Amna Dridi A Machine Learning Approach to Analysis & Prediction of Scientific Trends
- Julian Hatwell White-boxing Ensemble Machine Learning Methods
- Richard Davies Edge Deep Learning Systems
- Oliver Batty Mapping Coordination Patterns and Quality of Movement to Mental and Emotional States
- Khadija Hanga A Personalised Recommender System to Support People with Diabetes
- Aliyu Sambo Leveraging Parallel Computing to automatically produce efficient Computer Code/Computational Models for Applications in Health Science and Biomedical Engineering
- Besher Alhalaby Designing lightweight machine learning algorithms for IoT solutions for energy efficient predictive building control