Automated planning is the reasoning side of acting in Artificial Intelligence. An autonomous agent must plan, i.e., select and order its actions, to achieve its goals as best as possible. A partial-order plan compactly represents a set of plans which provides the agent some execution flexibility. This project will focus on building a decomposition-based least commitment flexible partial-order planner.
Mathematical modeling based approaches to:
- automated planning heuristics [1,2,3],
- partial-order relaxation [4,5,6],
- decomposition based automated planning .
constitute a good starting point for literature review.
 Tom Bylander. A Linear Programming Heuristic for Optimal Planning. 1997.
 Menkes van den Briel, J. Benton, Subbarao Kambhampati, Thomas Vossen. An LP-Based Heuristic for Optimal Planning. 2007.
 Florian Pommerening, Gabriele Roger, Malte Helmert, Blai Bonet. LP-Based Heuristics for Cost-Optimal Planning. 2014.
 Minh B. Do, Subbarao Kambhampati. Improving the temporal flexibility of position constrained metric temporal plans. 2003.
 Christian Muise, Sheila A. McIlraith, J. Christopher Beck. Optimally Relaxing Partial-Order Plans with MaxSAT. 2012.
 Buser Say, Andre A. Cire, J. Christopher Beck. Mathematical Programming Models For Optimizing Partial-Order Plan Flexibility. 2016.
 Toby Davies, Adrian R. Pearce, Peter Stuckey, Nir Lipovetzky. Sequencing Operator Counts. 2015.
A successful candidate should have proficient programming skills (e.g., in Python) as well as background in at least one of the following:
- automated planning, and/or
- mathematical modeling.