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Sökning: id:"swepub:oai:DiVA.org:oru-112139" > Planning for Learni...

  • Lamanna, LeonardoFondazione Bruno Kessler, Trento, Italy; Department of Information Engineering, University of Brescia, Brescia, Italy (författare)

Planning for Learning Object Properties

  • Artikel/kapitelEngelska2023

Förlag, utgivningsår, omfång ...

  • AAAI Press,2023
  • printrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:oru-112139
  • https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-112139URI
  • https://doi.org/10.1609/aaai.v37i10.26416DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:kon swepub-publicationtype

Anmärkningar

  • This work has been partially supported by AI-Plan4EU and TAILOR, two projects funded by the EU Horizon 2020 research and innovation program under GA n. 101016442 and n. 952215, respectively, and by MUR PRIN-2020 project RIPER (n. 20203FFYLK). 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.
  • 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 of labelled data. In real-world, open-ended deployments, however, it is unrealistic to assume to have a pre-trained model for all possible environments. Therefore, agents need to dynamically learn/adapt/extend their perceptual abilities online, in an autonomous way, by exploring and interacting with the environment where they operate. This paper describes a way to do so, by exploiting symbolic planning. Specifically, we formalize the problem of automatically training a neural network to recognize object properties as a symbolic planning problem (using PDDL). We use planning techniques to produce a strategy for automating the training dataset creation and the learning process. Finally, we provide an experimental evaluation in both a simulated and a real environment, which shows that the proposed approach is able to successfully learn how to recognize new object properties.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Serafini, LucianoFondazione Bruno Kessler, Trento, Italy (författare)
  • Faridghasemnia, Mohamadreza,1991-Örebro universitet,Institutionen för naturvetenskap och teknik,Center for Applied Autonomous Sensor Systems (AASS)(Swepub:oru)mfa (författare)
  • Saffiotti, Alessandro,Professor,1960-Örebro universitet,Institutionen för naturvetenskap och teknik,Center for Applied Autonomous Sensor Systems (AASS)(Swepub:oru)asaffio (författare)
  • Saetti, AlessandroDepartment of Information Engineering, University of Brescia, Brescia, Italy (författare)
  • Gerevini, AlfonsoDepartment of Information Engineering, University of Brescia, Brescia, Italy (författare)
  • Traverso, PaoloFondazione Bruno Kessler, Trento, Italy (författare)
  • Fondazione Bruno Kessler, Trento, Italy; Department of Information Engineering, University of Brescia, Brescia, ItalyFondazione Bruno Kessler, Trento, Italy (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:Proceedings of the AAAI Conference on Artificial Intelligence: AAAI Press, s. 12005-120139781577358800

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