The greater context in which we position our research is set by a number of prominent trends related to information processing devices in our lives (homes, offices, etc).
- The number and the computing power of such devices is growing.
- The communication capabilities and the degree of connectedness of such devices is growing.
- Users / owners want these devices work with minimal effort on setup and maintenance, that is, intelligently.
- Users / owners want these devices work together, that is, collectively.
As a consequence, 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.
Within this context, we perceive adaptivity as the Grand Challenge in collective intelligent systems of the future. We foresee a pivotal role for adaptive capabilities 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:
- self-calibrating evolutionary algorithms,
- embodied evolution in (simulated) robot swarms,
- collective adaptation in hybrid populations (humans, smart devices, robots).