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. Potential applications of this project include pandemic planning, navigation (e.g., see Figure 1 below), Heating, Ventilation and Air Conditioning control etc. The purpose of this Ph.D. project is to incorporate safety measures (e.g., with respect to uncertainty, against adversarial agents etc.) into the automated decision making of SCIPPlan.
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.