Abstract
In sports, athletes need detailed and individualised training plans for maintaining and improving their skills in order to deliver their best performance in competitions. This presents a considerable amount of effort and overhead for coaches, who not only have to set realistic objectives, but also formulate extremely detailed training plans. Automated Planning, which has already been successfully deployed in many real-world applications such as space exploration, robotics, or manufacturing processes, embodies a useful mechanism that can be exploited for generating training plans for athletes.In this thesis, we propose the use of automated-planning techniques for generating individual physical preparation training plans, which consist of exercises the athlete has to perform during training, given the athlete’s current performance, the period of time, and target performance that should be achieved. Our experimental analysis, which considers general training of kickboxers, shows that apart of considerable less planning time, training plans automatically generated by the proposed approach are more detailed and individualised than plans prepared manually by a coach.
| Date of Award | 23 Apr 2020 |
|---|---|
| Original language | English |
| Supervisor | Mauro Vallati (Main Supervisor) & Lukas Chrpa (Co-Supervisor) |