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Learning to Act for Perceiving in Partially Unknown Environments
Fondazione Bruno Kessler, Trento, Italy.
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-7072-7104
Department of Information Engineering, University of Brescia, Italy.
Department of Information Engineering, University of Brescia, Italy.
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2023 (English)In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023) / [ed] Edith Elkind, International Joint Conferences on Artificial Intelligence , 2023, p. 5485-5493Conference paper, Published paper (Refereed)
Abstract [en]

Autonomous agents embedded in a physical environment need the ability to correctly perceive the state of the environment from sensory data. In partially observable environments, certain properties can be perceived only in specific situations and from certain viewpoints that can be reached by the agent by planning and executing actions. For instance, to understand whether a cup is full of coffee, an agent, equipped with a camera, needs to turn on the light and look at the cup from the top. When the proper situations to perceive the desired properties are unknown, an agent needs to learn them and plan to get in such situations. In this paper, we devise a general method to solve this problem by evaluating the confidence of a neural network online and by using symbolic planning. We experimentally evaluate the proposed approach on several synthetic datasets, and show the feasibility of our approach in a real-world scenario that involves noisy perceptions and noisy actions on a real robot.

Place, publisher, year, edition, pages
International Joint Conferences on Artificial Intelligence , 2023. p. 5485-5493
Series
IJCAI International Joint Conference on Artificial Intelligence, ISSN 1045-0823
Keywords [en]
Artificial intelligence, General method, Learn+, Neural-networks, Partially observable environments, Physical environments, Property, Real-world scenario, Sensory data, Synthetic datasets, Unknown environments, Autonomous agents
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-112138DOI: 10.24963/ijcai.2023/609ISI: 001202344205065Scopus ID: 2-s2.0-85170365795ISBN: 9781956792034 (electronic)OAI: oai:DiVA.org:oru-112138DiVA, id: diva2:1842789
Conference
32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, S.A.R., August 19-25, 2023
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Knut and Alice Wallenberg FoundationEU, Horizon 2020, 101016442
Note

We acknowledge the support of the PNRR project FAIR - Future AI Research (PE00000013), under the NRRP MUR program funded by the NextGenerationEU. This work has also been partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, and AIPlan4EU funded by the EU Horizon 2020 research and innovation program under GA n. 101016442.

Available from: 2024-03-06 Created: 2024-03-06 Last updated: 2024-08-13Bibliographically approved

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Faridghasemnia, MohamadrezaSaffiotti, Alessandro

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