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Continuous-time Automated Decision Making with Mathematical Optimisation

Primary supervisor

Buser 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.

Visualisation of a plan generated by SCIPPlan for an example navigation domain.
Figure 1: Visualisation of a plan generated by SCIPPlan for an example navigation domain where the red square represents the agent, the blue rectangles represent the blocks, the gold star represents the goal location and the delta represents time. The agent can control its acceleration and the duration of its control input to modify its speed and location in order to navigate in a two-dimensional maze. The goal of the domain is to find a path for the agent with minimum makespan such that the agent reaches its the goal without colliding with the obstacle. Note that SCIPPlan does not linearise or discretise the domain to find a valid plan.


Student cohort

Double Semester


Please note that this project can be extended to a fully-funded PhD project in the future.

Required knowledge

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.