Primary supervisorBuser Say
SCIPPlan is a mathematical optimisation based automated planner for domains with i) mixed (i.e., real and/or discrete valued) state and action spaces, ii) nonlinear state transitions that are functions of time, and iii) general reward functions. SCIPPlan iteratively i) finds violated constraints (i.e., zero-crossings) by simulating the state transitions, and ii) adds the violated constraints back to its underlying optimization model, until a valid plan is found. The purpose of this project is to improve the performance of SCIPPlan. Potential applications of this project include pandemic planning, navigation (e.g., see Figure 1 below), Heating, Ventilation and Air Conditioning control etc.
Please note that this project can be extended to a fully-funded PhD project in the future.
A successful candidate should have proficient programming skills (e.g., in Python) as well as background in at least one of the
- automated planning, and/or
- mathematical modeling.