Currently browsing: Items authored or edited by Agnese Chiatti

Bardaro, Gianluca; Daga, Enrico; Carvalho, Jason; Chiatti, Agnese and Motta, Enrico (2022). Introducing a Smart City Component in a Robotic Competition: A Field Report. Frontiers in Robotics and AI, 9, article no. 728628.

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Bardaro, Gianluca; Semprebon, Andrea; Chiatti, Agnese and Matteucci, Matteo (2019). From Models to Software Through Automatic Transformations: An AADL to ROS End-to-End Toolchain. In: Third IEEE International Conference on Robotic Computing (IRC), 25-27 Feb 2019, Naples, Italy.

Chiatti, Agnese; Bardaro, Gianluca; Matteucci, Matteo and Motta, Enrico (2023). Visual Model Building for Robot Sensemaking: Perspectives, Challenges, and Opportunities. In: Bridge Session on AI and Robotics of the thirty-seventh AAAI conference on Artificial Intelligence (AAAI-23), 7-8 Feb 2023, Washington DC, USA, AAAI.

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Chiatti, Agnese (2022). Visually Intelligent Agents: Improving Sensemaking in Service Robotics. PhD thesis The Open University.

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Chiatti, Agnese; Bardaro, Gianluca; Motta, Enrico and Daga, Enrico (2022). A Spatial Reasoning Framework for Commonsense Reasoning in Visually Intelligent Agents. In: AIC 2022, 8th International Workshop on Artificial Intelligence and Cognition, 15-17 Jun 2022, Örebro, Sweden, CEUR.

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Chiatti, Agnese; Motta, Enrico and Daga, Enrico (2022). Robots with Commonsense: Improving Object Recognition through Size and Spatial Awareness. In: the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022) (Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Doug; Stolle, Reinhard and van Harmelen, Frank eds.), 21-23 Mar 2022, Palo Alto, California, USA, CEUR.

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Chiatti, Agnese and Daga, Enrico (2022). Neuro-symbolic learning for dealing with sparsity in cultural heritage image archives: an empirical journey. In: Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG), co-located with the 21st International Semantic Web Conference (ISWC), 23-24 Oct 2022, Virtual, CEUR Workshop Proceedings 3342.

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Chiatti, Agnese (2021). Towards Visually Intelligent Agents (VIA): A Hybrid Approach. In: The Semantic Web: ESWC 2021 Satellite Events (Verborgh, Ruben; Hogan, Aidan; Tiddi, Ilaria; Mayer, Simon; Tommasini, Riccardo; Dimou, Anastasia; d' Amato, Claudia; Bröring, Arne; Ongenae, Femke and Alam, Mehwish eds.), Lecture Notes in Computer Science, Springer, Cham, pp. 195–206.

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Chiatti, Agnese; Motta, Enrico; Daga, Enrico and Bardaro, Gianluca (2021). Fit to Measure: Reasoning about Sizes for Robust Object Recognition. In: Proceedings of the AAAI2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021) (Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Doug; Stolle, Reinhard and van Harmelen, Frank eds.), 22-24 Mar 2021, International Virtual Event.

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Chiatti, Agnese; Bardaro, Gianluca; Bastianelli, Emanuele; Tiddi, Ilaria; Mitra, Prasenjit and Motta, Enrico (2020). Task-Agnostic Object Recognition for Mobile Robots through Few-Shot Image Matching. Electronics, 9(3), article no. 380.

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Chiatti, Agnese (2020). Robots with common sense: Improving sensemaking in service robotics. Postgraduate Research Poster Competition, The Open University.

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Chiatti, Agnese; Motta, Enrico and Daga, Enrico (2020). Towards a Framework for Visual Intelligence in Service Robotics:Epistemic Requirements and Gap Analysis. In: Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR 2020), 12-18 Sep 2020.

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Chiatti, Agnese; Bardaro, Gianluca; Bastianelli, Emanuele; Tiddi, Ilaria; Mitra, Prasenjit and Motta, Enrico (2019). Exploring Task-agnostic, ShapeNet-based Object Recognition for Mobile Robots. In: Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference.

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Hoque, Md Reshad Ul; Bradley, Dash; Kwan, Chiman; Chiatti, Agnese; Li, Jiang and Wu, Jian (2019). Searching for Evidence of Scientific News in Scholarly Big Data. Proceedings of the 10th International Conference on Knowledge Capture - K-CAP '19 pp. 251–254.