Interview with Amar Halilovic: Explainable AI for robotics

On this interview collection, we’re assembly a few of the AAAI/SIGAI Doctoral Consortium members to search out out extra about their analysis. The Doctoral Consortium gives a chance for a bunch 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 scholar at Ulm College.

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

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

May you give us an summary of the analysis you’ve carried out up to now throughout your PhD?

To this point, I’ve developed a framework for environmental explanations of robotic actions and choices, particularly when issues go flawed. 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 clarification attributes, akin to timing, illustration, period, and many others. These days, I’ve been engaged on strategies for dynamically choosing the right clarification 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 individuals interpret robotic conduct in another way relying on the urgency or failure context. It’s been particularly rewarding to check how clarification expectations shift in several conditions and the way we will tailor clarification timing and content material accordingly.

What are your plans for constructing in your analysis up to now in the course of the PhD – what elements will you be investigating subsequent?

Subsequent, I’ll be extending the framework to include real-time adaptation, enabling robots to study 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 wish 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 programs 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 pondering of doing a PhD within the area?

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

May you inform us an fascinating (non-AI associated) truth about you?

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

About Amar

Amar is a PhD scholar 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 programs 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. Exterior academia, Amar enjoys travelling, pictures, and exploring connections between expertise and society.