Welcome to the website of the Robots, Agents, and Interaction (RAI) group at the Department of Data Science & Knowledge Engineering of the Faculty of Science and Engineering (FSE) at Maastricht University.
RAI has strong experience in the design and implementation of machine learning, data mining, computer vision, and robotics. This includes broad know-how in research involving learning and coordination among autonomous agents (software or robots), human-robot interaction, human activity & emotion recognition in HCI/HRI (Human-Computer/Robot Interaction), swarm and modular robotics, automated negotiation, and AI knowledge transfer.
Paper accepted at the 17th IEEE International Conference on Machine Learning and Applications (ICMLA’18)
Within the frame of H2020 MaTHiSiS EU project, our paper titled “Towards Affect Recognition through Interactions with Learning Materials” is accepted as a regular paper for oral presenation at ICMLA’18. The conference will take place in Orlando, Florida, next December.
New paper to be presented at the 2018 IEEE Computer Vision and Pattern Recognition Workshops (CVPRW)
Within the frame of the H2020 ICT4Life EU project, D. Dotti, M. Popa and S. Asteriadis will be presenting their work on Behavior and Personality Analysis in a nonsocial context Dataset, next June, at the IEEE Computer Vision and Pattern Recognition Workshop on Understanding Subjective Attributes of Data
Our paper titled “Social Emotion Mining Techniques for Facebook Posts Reaction Prediction” is accepted for publication at the 10th International Conference on Agents and Artificial Intelligence (ICAART 2018) as a full paper.
This paper was authored by four of our Master in Artificial Intelligence students and their supervisor (Florian Krebs, Bruno Lubascher, Tobias Moers, Pieter Schaap, Gerasimos Spanakis) as part of a semester research project and explored how deep learning techniques (CNN and RNN) can be used to set up a prediction module for reaction predictions on Facebook posts.
Our joint work with the Faculty of Health, Medicine and Life Sciences (FHML) ‘A risk score of BMI, HbA1c and triglycerides predicts future glycemic control in type 2 diabetes’ has been accepted for publication at the Diabetes, Obesity and Metabolism Journal.
A description of the platform employed in ICT4Life has been published as a book chapter; it is a joint work of the ICT4Life consortium, in which, RAI, plays a leading role in AAL activities and machine intelligence applications.
For more details:
- A. Sánchez-Rico, P. Garel, I. Notarangelo, M. Quintana, G. Hernández, S. Asteriadis, M. Popa, N. Vretos, V. Solachidis, M. Burgos, A. Girault. ICT Services for Life Improvement for the Elderly. Stud Health Technol Inform. 242:600-605, 2017
ICT4Life project link: http://www.ict4life.eu/
Oour joint work with UPM and CERTH ‘Behaviour analysis through multimodal sensing for improving Parkinson and Alzheimer patients quality of life’ has been accepted for publication at IEEE multimedia magazine
It has been a good week for RAI!
Our paper titled “A retrieval-based dialogue system utilizing utterance and context embeddings” (A. Bartl, G. Spanakis) is accepted as a poster paper at the 16th IEEE International on Machine Learning and Applications (ICMLA2017).
This paper was first-authored by one of our Master in Artificial Intelligence graduates and describes his work towards building a chatbot for one of Maastricht based companies that DKE works with.
Our paper titled “Accumulated Gradient Optimization” (J. Hermans, G. Spanakis, R. Moeckel) is accepted as a full paper at the 9th Asian Conference on Machine Learning (ACML2017).
This paper was first-authored by one of our Master in Artificial Intelligence graduates, based on his thesis work (started at CERN and awarded one of the best thesis awards from DKE for 2017).
Our paper titled “Massive Open Online Courses Temporal Profiling for Dropout Prediction” (T. Rolandus Hagedoorn, G. Spanakis) is accepted as a full paper at the 29th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2017).
This paper was first-authored by one of our Bachelor in Data Science and Knowledge Engineering graduates.