How Autonomous Agents can better process the World’s Knowledge

March 11, 2021 by Thomas Thurner

We had the chance to ask Ilaria Tiddi about her research on AI&Robotics. One of the fields which are reflected in this year's program at SEMANTiCS 2021. 

Thomas: You have a background in artificial intelligence and you have been working in the area of social psychology and robotics for quite some time. How do these two areas of expertise come together?

Ilaria: My background is AI and knowledge representation, and work on finding the AI/KR techniques that can benefit the work of behavioural social scientists and roboticists during our projects. How these two come together? AI and autonomous systems are becoming more and more part of our everyday lives, and we want them interacting with us in the right way. To do this, we first need to understand how humans behave and collaborate in social situations. 

Thomas: What are critical aspects in the design of social robots? Can you give us a brief 1-0-1 on this topic and how your research contributes to this area?

Ilaria: Social robots that operate in public/commercial spaces (cfr. waiters, policemen, receptionists) need to show a proper understanding of the surrounding environment through autonomy, reasoning and sensing capabilities. My research looks at understanding how autonomous agents can better process the world’s knowledge, and better interact with us, humans.  

Thomas: Together with Maria Maleshkova from the University of Bonn, you are this year’s Poster and Demo Chair. Any recommendations to submitters what makes a great poster?

Ilaria: I like the KISS principle, Keep it simple and stupid. Show the keypoints (problem, motivation, approach, evaluation) with a simple design — everything else can be explained face-to-face. And engage with your audience!

About Ilaria Tiddi

Ilaria Tiddi is a Research Associate at the Vrije Universiteit Amsterdam, working for the Knowledge Representation and Reasoning group as well as the Amsterdam Cooperation Lab. Her research focuses on creating transparent, intelligent systems that generate explanations through a combination of machine learning, semantic technologies, open data and cognitive theories, mostly applied in e-Science and robotics scenarios.