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Learning to Act for...
Learning to Act for Perceiving in Partially Unknown Environments
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- Lamanna, Leonardo (författare)
- Fondazione Bruno Kessler, Trento, Italy
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- Faridghasemnia, Mohamadreza, 1991- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,Center for Applied Autonomous Sensor Systems (AASS)
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- Gerevini, Alfonso (författare)
- Department of Information Engineering, University of Brescia, Italy
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- Saetti, Alessandro (författare)
- Department of Information Engineering, University of Brescia, Italy
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- Saffiotti, Alessandro, Professor, 1960- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,Center for Applied Autonomous Sensor Systems (AASS)
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- Serafini, Luciano (författare)
- Fondazione Bruno Kessler, Trento, Italy
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- Traverso, Paolo (författare)
- Fondazione Bruno Kessler, Trento, Italy
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(creator_code:org_t)
- International Joint Conferences on Artificial Intelligence, 2023
- 2023
- Engelska.
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Ingår i: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023). - : International Joint Conferences on Artificial Intelligence. - 9781956792034 ; , s. 5485-5493
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.2...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Artificial intelligence
- General method
- Learn
- Neural-networks
- Partially observable environments
- Physical environments
- Property
- Real-world scenario
- Sensory data
- Synthetic datasets
- Unknown environments
- Autonomous agents
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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