Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Saetti, Alessandro"'
Autor:
Lamanna, Leonardo, Serafini, Luciano, Faridghasemnia, Mohamadreza, Saffiotti, Alessandro, Saetti, Alessandro, Gerevini, Alfonso, Traverso, Paolo
Autonomous agents embedded in a physical environment need the ability to recognize objects and their properties from sensory data. Such a perceptual ability is often implemented by supervised machine learning models, which are pre-trained using a set
Externí odkaz:
http://arxiv.org/abs/2301.06054
Autor:
Lamanna, Leonardo, Serafini, Luciano, Saetti, Alessandro, Gerevini, Alfonso Emilio, Traverso, Paolo
Publikováno v:
In Artificial Intelligence February 2025 339
If a robotic agent wants to exploit symbolic planning techniques to achieve some goal, it must be able to properly ground an abstract planning domain in the environment in which it operates. However, if the environment is initially unknown by the age
Externí odkaz:
http://arxiv.org/abs/2112.10007
Autor:
Gerevini, Alfonso E., Lipovetzky, Nir, Peli, Nico, Percassi, Francesco, Saetti, Alessandro, Serina, Ivan
In multi-agent planning, agents jointly compute a plan that achieves mutual goals, keeping certain information private to the individual agents. Agents' coordination is achieved through the transmission of messages. These messages can be a source of
Externí odkaz:
http://arxiv.org/abs/1906.08061
In multi-agent planning, preserving the agents' privacy has become an increasingly popular research topic. For preserving the agents' privacy, agents jointly compute a plan that achieves mutual goals by keeping certain information private to the indi
Externí odkaz:
http://arxiv.org/abs/1906.03955
Publikováno v:
In Artificial Intelligence May 2023 318
Autor:
De Giacomo, Giuseppe, Gerevini, Alfonso Emilio, Patrizi, Fabio, Saetti, Alessandro, Sardina, Sebastian
Publikováno v:
In Artificial Intelligence February 2016 231:64-106
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Lamanna, Leonardo, Mohamadreza, Faridghasemnia, Gerevini, Alfonso Emilio, Saetti, Alessandro, Alessandro, Saffiotti, Serafini, Luciano, Paolo, Traverso
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3662::86fb88123539bae58df61951c7cd9fef
https://hdl.handle.net/11379/576006
https://hdl.handle.net/11379/576006
Autor:
Shizhe, Zhao, Chiari, Mattia, Botea, ADI IONEL, Gerevini, Alfonso Emilio, Daniel, Harabor, Saetti, Alessandro, Stuckey, Peter J.
Publikováno v:
Proceedings of the International Conference on Automated Planning and Scheduling. 30:333-341
Compressed Path Databases (CPDs) are a state-of-the-art method for path planning. They record, for each start position, an optimal first move to reach any target position. Computing an optimal path with CPDs is extremely fast and requires no state-sp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b8cbe0e08309c0f1742602314e8a3bfb
https://doi.org/10.1609/icaps.v30i1.6678
https://doi.org/10.1609/icaps.v30i1.6678