Advanced forms of AI Planning

Automated planning is the problem of automatically producing a plan (i.e., a course of actions) in order to achieve a certain goal (e.g., have all the packages delivered in a logistic domain). Those plans are generally meant to be executed by intelligent agents, autonomous robots, unmanned vehicles, or complex embedded systems.

I am particularly interested in advanced forms of planning (e.g., such as planning under non-determinism or adaptive planning), the link with controller synthesis for adversarial planning, and path-planning (or pathfinding) in an AI context.

Together with students, and sometimes as part of the COSC1024/2048 Agent-Oriented Programming course, we developed path-planning libraries and simulation-visualisation toolkit (that are compatible with Moving-AI benchmark framework).

Some representative papers in the area are:

• Ivan D. Rodriguez, Blai Bonet, Sebastian Sardiña, Hector Geffner: Flexible FOND Planning with Explicit Fairness Assumptions. ICAPS 2021: 290-29. Best Paper Award.

• Daniel Alfredo Ciolek, Nicolás D’Ippolito, Alberto Pozanco, Sebastian Sardiña: [ Multi-Tier Automated Planning for Adaptive Behavior]. ICAPS 2020: 66-74

• Nicolás D’Ippolito, Natalia Rodríguez, Sebastian Sardiña: Fully Observable Non-deterministic Planning as Assumption-Based Reactive Synthesis. Journal of Artificial Intelligence Research, 61: 593-621 (2018)

• Giuseppe De Giacomo, Alfonso Gerevini, Fabio Patrizi, Alessandro Saetti, and Sebastian Sardina. Agent planning programs. Artificial Intelligence, 231:64–106, 2016.

• Sarah Hickmott and Sebastian Sardina. Optimality properties of planning via Petri net unfolding: A formal analysis. In Alfonso Gerevini and Adele Howe, editors, Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), pages 170-177, Thessaloniki, Greece, September 2009. AAAI Press.

• Davide Aversa, Sebastian Sardina, and Stavros Vassos. Path planning with inventory-driven jump-point-search. In Proceedings of the AAAI-AIIDE, 2015.