Computational Intelligence is the new(ish) approach to Artificial Intelligence, roughly distinguished by its sub-symbolic, bottom-up character and the use of nature-inspired computational methods. The greater context in which we position our research is that of the increasing number of connected information processing devices in our lives and the related 'trends' regarding, for instance, ambient intelligence, robotics, the internet of things, quantified self, etc.
We expect that the next wave of artificial intelligence will be collective intelligence, based on heterogeneous groups of many connected units. Furthermore, we envision two features becoming essential: adaptivity and autonomy.
We are especially interested in the combination of collectivity, adaptivity, and autonomy. Systems in the intersection of these areas include (future versions of) swarm robotic systems, smart grids, distributed sensor networks, eHealth systems with interactive sensing devices, ambient assisted living, and smart vehicles.
We perceive adaptivity as the Grand Challenge in collective intelligent systems of the future because these systems must be equipped for scenarios where the operational circumstances are:
- not fully known in advance,
- so complex that behavioural rules cannot be designed & coded by traditional analytical means.
Our research is focused on algorithmic aspects. In particular, we work in evolutionary computing and machine learning, addressing fundamental issues as well as applications in optimization, data mining, artificial life, robotics, and art. The strategic research threads of the group are:
- parameter setting in evolutionary algorithms
- embodied evolution in (simulated) robot swarms,
- collective adaptation in hybrid populations (humans, smart devices, robots)