| Title |
Research Directions For Aggregate Computing With Machine Learning |
| ID_Doc |
45209 |
| Authors |
Aguzzi G. |
| Year |
2021 |
| Published |
Proceedings - 2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2021 |
| DOI |
http://dx.doi.org/10.1109/ACSOS-C52956.2021.00078 |
| Abstract |
Collective adaptive systems are challenging from the engineering perspective. Different approaches aim at taming these systems either by specifying the behaviour programmatically or by using Machine Learning techniques. Aggregate programming is part of the first group and is a novel technique by which developers can express collective system behaviours from a global perspective, using a compositional and functional programming approach. Over the years, Aggregate Computing has been applied in different scenarios, ranging from smart cities to crowd of augmented people. Despite its promising capabilities, it is sometimes challenging to describe aggregate behaviours, so we aim at merging Aggregate Computing with Machine Learning techniques to simplify the aggregate program synthesis. © 2021 IEEE. |
| Author Keywords |
Aggregate Computing; Field Coordination; Machine Learning; Multi-Agent Reinforcement Learning |
