Planning for Learning Object Properties Article Swipe
Leonardo Lamanna
,
Luciano Serafini
,
Mohamadreza Faridghasemnia
,
Alessandro Saffiotti
,
Alessandro Saetti
,
Alfonso Gerevini
,
Paolo Traverso
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v37i10.26416
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v37i10.26416
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v37i10.26416
- https://ojs.aaai.org/index.php/AAAI/article/download/26416/26188
- OA Status
- diamond
- Cited By
- 3
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382202796
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4382202796Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v37i10.26416Digital Object Identifier
- Title
-
Planning for Learning Object PropertiesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-26Full publication date if available
- Authors
-
Leonardo Lamanna, Luciano Serafini, Mohamadreza Faridghasemnia, Alessandro Saffiotti, Alessandro Saetti, Alfonso Gerevini, Paolo TraversoList of authors in order
- Landing page
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https://doi.org/10.1609/aaai.v37i10.26416Publisher landing page
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https://ojs.aaai.org/index.php/AAAI/article/download/26416/26188Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://ojs.aaai.org/index.php/AAAI/article/download/26416/26188Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Process (computing), Perception, Object (grammar), Set (abstract data type), Machine learning, Human–computer interaction, Artificial neural network, Programming language, Neuroscience, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2023: 3Per-year citation counts (last 5 years)
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62Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_lineage_names | Association for the Advancement of Artificial Intelligence |
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| publication_date | 2023-06-26 |
| publication_year | 2023 |
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