no code implementations • 25 Apr 2022 • Andrés Occhipinti Liberman, Blai Bonet, Hector Geffner
In this work, we develop a new formulation for learning crisp first-order planning models that are grounded on parsed images, a step to combine the benefits of the two approaches.
no code implementations • 13 Sep 2021 • Thomas Bolander, Nina Gierasimczuk, Andrés Occhipinti Liberman
We consider a learning agent in a partially observable environment, with which the agent has never interacted before, and about which it learns both what it can observe and how its actions affect the environment.
no code implementations • 14 Jun 2019 • Andrés Occhipinti Liberman, Andreas Achen, Rasmus Kræmmer Rendsvig
The formalism combines the strengths of DEL (higher-order reasoning) with those of first-order logic (lifted representation) to model multi-agent epistemic planning.
