Interview with Amar Halilovic: Explainable AI for robotics

On this interview sequence, we’re assembly a number of the AAAI/SIGAI Doctoral Consortium individuals to search out out extra about their analysis. The Doctoral Consortium gives a possibility for a gaggle of PhD college students to debate and discover their analysis pursuits and profession targets in an interdisciplinary workshop along with a panel of established researchers. On this newest interview, we hear from Amar Halilovic, a PhD pupil at Ulm College.

Inform us a bit about your PhD – the place are you learning, and what’s the subject of your analysis?

I’m at the moment a PhD pupil at Ulm College in Germany, the place I give attention to explainable AI for robotics. My analysis investigates how robots can generate explanations of their actions in a means that aligns with human preferences and expectations, notably in navigation duties.

Might you give us an outline of the analysis you’ve carried out to this point throughout your PhD?

To date, I’ve developed a framework for environmental explanations of robotic actions and choices, particularly when issues go improper. I’ve explored black-box and generative approaches for the era of textual and visible explanations. Moreover, I’ve been engaged on planning of various rationalization attributes, equivalent to timing, illustration, period, and so forth. Currently, I’ve been engaged on strategies for dynamically selecting the right rationalization technique relying on the context and consumer preferences.

Is there a facet of your analysis that has been notably fascinating?

Sure, I discover it fascinating how folks interpret robotic conduct in another way relying on the urgency or failure context. It’s been particularly rewarding to review how rationalization expectations shift in several conditions and the way we will tailor rationalization timing and content material accordingly.

What are your plans for constructing in your analysis to this point through the PhD – what elements will you be investigating subsequent?

Subsequent, I’ll be extending the framework to include real-time adaptation, enabling robots to be taught from consumer suggestions and modify their explanations on the fly. I’m additionally planning extra consumer research to validate the effectiveness of those explanations in real-world human-robot interplay settings.

Amar along with his poster on the AAAI/SIGAI Doctoral Consortium at AAAI 2025.

What made you need to examine AI, and, particularly, explainable robotic navigation?

I’ve all the time been within the intersection of people and machines. Throughout my research, I spotted that making AI methods comprehensible isn’t only a technical problem—it’s key to belief and value. Robotic navigation struck me as a very compelling space as a result of choices are spatial and visible, making explanations each difficult and impactful.

What recommendation would you give to somebody considering of doing a PhD within the area?

Choose a subject that genuinely excites you—you’ll be dwelling with it for a number of years! Additionally, construct a assist community of mentors and friends. It’s straightforward to get misplaced within the technical work, however collaboration and suggestions are important.

Might you inform us an fascinating (non-AI associated) reality about you?

I’ve lived and studied in 4 completely different international locations.

About Amar

Amar is a PhD pupil on the Institute of Synthetic Intelligence of Ulm College in Germany. His analysis focuses on Explainable Synthetic Intelligence (XAI) in Human-Robotic Interplay (HRI), notably how robots can generate context-sensitive explanations for navigation choices. He combines symbolic planning and machine studying to construct explainable robotic methods that adapt to human preferences and completely different contexts. Earlier than beginning his PhD, he studied Electrical Engineering on the College of Sarajevo in Sarajevo, Bosnia and Herzegovina, and Pc Science at Mälardalen College in Västerås, Sweden. Outdoors academia, Amar enjoys travelling, pictures, and exploring connections between know-how and society.