Marco Favorito — accepted AI-conference papers

2024

Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic (Extended Abstract)

Luigi Bonassi, Giuseppe De Giacomo, Marco Favorito, Francesco Fuggitti, Alfonso Emilio Gerevini, Enrico Scala

IJCAI 2024poster

We study classical planning for temporally extended goals expressed in Pure-Past Linear Temporal Logic (PPLTL). PPLTL is as expressive as Linear-time Temporal Logic on finite traces (LTLf), but as shown in this paper, it is computationally much better behaved for planning. Specifically, we show…

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2023

Exploiting Multiple Abstractions in Episodic RL via Reward Shaping

Roberto Cipollone, Giuseppe De Giacomo, Marco Favorito, Luca Iocchi, Fabio Patrizi

AAAI 2023technical

One major limitation to the applicability of Reinforcement Learning (RL) to many practical domains is the large number of samples required to learn an optimal policy. To address this problem and improve learning efficiency, we consider a linear hierarchy of abstraction layers of the Markov Decision…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗

2022

LTLf Synthesis as AND-OR Graph Search: Knowledge Compilation at Work

Giuseppe De Giacomo, Marco Favorito, Jianwen Li, Moshe Y. Vardi, Shengping Xiao, Shufang Zhu

IJCAI 2022poster

Synthesis techniques for temporal logic specifications are typically based on exploiting symbolic techniques, as done in model checking. These symbolic techniques typically use backward fixpoint computation. Planning, which can be seen as a specific form of synthesis, is a witness of the success of…

Cited by 19 Source ↗PDF ↗Scholar ↗