On this interview collection, we’re assembly among the AAAI/SIGAI DOCTORAL Consortium members to seek out out extra about their analysis. The Doctoral Consortium supplies 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 finding out, and what’s the matter of your analysis?
I’m at present a PhD pupil 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 approach that aligns with human preferences and expectations, notably in navigation duties.
May you give us an outline of the analysis you’ve carried out to date throughout your PhD?
Up to now, I’ve developed a framework for environmental explanations of robotic actions and choices, particularly when issues go mistaken. I’ve explored black-box and generative approaches for the technology of textual and visible explanations. Moreover, I’ve been engaged on planning of various rationalization attributes, resembling timing, illustration, period, and many others. These days, I’ve been engaged on strategies for dynamically choosing the right rationalization technique relying on the context and consumer preferences.
Is there a facet of your analysis that has been notably attention-grabbing?
Sure, I discover it fascinating how folks interpret robotic habits otherwise relying on the urgency or failure context. It’s been particularly rewarding to check 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 date 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 CONSORTIR at AAAI 2025.
What made you need to examine AI, and, specifically, explainable robotic navigation?
I’ve all the time been within the intersection of people and machines. Throughout my research, I spotted that making AI techniques comprehensible isn’t only a technical problem—it’s key to belief and usefulness. 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?
Decide a subject that genuinely excites you—you’ll be dwelling with it for a number of years! Additionally, construct a help 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 attention-grabbing (non-AI associated) truth 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 techniques 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 Laptop Science at Mälardalen College in Västerås, Sweden. Outdoors academia, Amar enjoys travelling, images, and exploring connections between know-how and society.
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AIhub
is a non-profit devoted to connecting the AI neighborhood to the general public by offering free, high-quality data in AI.
