MARVIN Research Group
AI Planning and Scheduling

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===== Damien Pellier =====

[[http://www.liglab.fr/|Laboratoire d'Informatique de Grenoble (UMR 5217)]] \\
[[http://marvin.imag.fr|Équipe Marvin]]
Université Grenoble Alpes\\
Bâtiment IMAG -- 700 avenue Centrale\\
Domaine Universitaire -- 38401 St Martin d'Hères\\
[[damien.pellier@imag.fr|damien.pellier@imag.fr]] \\
** Office:** 365\\
** Phone: ** (+33) 4 57 42 15 39 \\
===== Background =====

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I am currently a Full Professor at [[https://www.univ-grenoble-alpes.fr|Université Grenoble Alpes]], where I conduct research in Artificial Intelligence, with a particular focus on automated planning and scheduling, machine learning, and robotics. I lead the [[http://marvin.imag.fr|Marvin team]] team at the [[http://www.liglab.fr/|laboratory of computer sciences of Grenoble]] (LIG). My academic journey includes serving as an Associate Professor at Paris Descartes University from 2006 to 2013, during which I also held the position of Vice-President of the Board of Directors from 2011 to 2013.

I received a Master's degree in Operating Systems and Network Computing from the [[http://www.polytech-grenoble.fr/|Polytech'Grenoble School]] and a Master's degree in Artificial Intelligence from Université Grenoble Alpes. Additionally, I hold an PhD. and a HDR (Habilitation to Direct Research) from Grenoble Alpes University.

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 ===== Research Interest =====

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My research is in ** Artificial Intelligence** but specially **automated planning and scheduling**, **Robotics** and ** machine learning**.

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Automated planning is a branch of artificial intelligence focused on developing strategies or action sequences for execution by intelligent agents, autonomous robots, and unmanned vehicles.

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A typical planner operates with three main inputs: a description of the initial state of the world, a description of the desired goal, and a set of possible actions, all encoded in a formal language like STRIPS. The planner's task is to generate a sequence of actions that transition the system from the initial state to one that satisfies the goal. The complexity of planning varies based on the assumptions made, such as atomic time, deterministic outcomes, and complete observability. Classical planners, which operate under all these assumptions, have been the most extensively studied in the field.

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