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Learning Planning Domains for Intelligent Transport Systems
Örebro University, School of Science and Technology. Intelligent Transport Systems, Scania CV AB, Södertälje, Sweden. (Center for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-6897-0244
2023 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

This short paper presents an ongoing industrial PhD project focused on learning domain models in the transportation industry. We seek to develop a two-phase learning process that leverages Behavior Trees to involve human planning experts in the learning loop. In this paper, we outline our research questions, discuss the initial steps we have taken in our research, and highlight the challenges we expect to face moving forward.

Place, publisher, year, edition, pages
2023.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-110795OAI: oai:DiVA.org:oru-110795DiVA, id: diva2:1829013
Conference
35th Swedish Artificial Intelligence Society (SAIS'23) annual workshop, Karlskrona, Sweden, June 12-13, 2023
Funder
Swedish Foundation for Strategic ResearchAvailable from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-01-18Bibliographically approved

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Learning Planning Domains for Intelligent Transport Systems(264 kB)157 downloads
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Type fulltextMimetype application/pdf

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Gugliermo, Simona

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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Output format
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