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Christiane Attig, Johannes Varga, Tim Schrills, Tobias Rodemann, Guenther Raidl , "Annoyance Modeling in Cooperative Personnel Scheduling", 17th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences, vol. 220, pp. 49-59, 2026.
AbstractAlthough many studied settings in algorithmic optimization affect humans, human factors and user behavior are often neglected. For instance, optimization algorithms that require human input seldomly model or consider humans cognitive states, even though these might affect data input quality and consequently computational results. The objective of this work is to demonstrate how annoyance—as one prototypical user state that can be elicited when sy...
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Christiane Attig, Patricia Wollstadt, Martina Hasenjäger, Alan Sarkisian, Jouh Yeong Chew, Christiane Wiebel , "Shaping Expectations of AI: A Basic Psychological Needs Perspective Across Cultures", SDT Conference 2026, 2026.
AbstractInteractions with artificial intelligence (AI) systems, particularly LLM-based chatbots, have become an integral part of daily work and private life for millions of people worldwide in recent years. However, given the diverse purposes and embodiments that AI systems can assume, hands-on experience remains only one source shaping expectations for future AI encounters. Other influential factors include the perceived role of AI (e.g., an AI seen as ...
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Ahmed Sadik and Mariusz Bujny , "Human-in-the-Loop: Quantitative Evaluation of 3D Models Generation by Large Language Models", 18th International Conference on Agents and Artificial Intelligence , 2026.
AbstractRecent advancements in Large Language Models (LLMs) have demonstrated their potential in generating 3D shapes, yet the evaluation of these outputs remains a critical challenge. In this study, we propose a quantitative evaluation framework that integrates human-in-the-loop feedback to iteratively refine 3D shape generation. Our approach employs a set of complexity and similarity metrics to assess the generated models against ground truth (GT) coun...
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Christiane Attig, Patricia Wollstadt, Jouh Yeong Chew, Alan Sarkisian, Christiane Wiebel , "Human–AI Cooperation Reconsidered: Integrating Reciprocity and Psychological Needs", Artificial Intelligence in HCI. HCII 2026, Springer, vol. 16743, pp. 235-261, 2026.
AbstractArtificial Intelligence (AI) systems are becoming increasingly pervasive in our daily lives. As these systems are applied across a wide range of domains, the need to thoughtfully design successful cooperative human–machine interaction becomes more relevant than ever. In this rapidly evolving sociotechnological landscape, what counts as “successful” human–AI cooperation from the human perspective remains an open question. We here argue that design...
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Ahmed Sadik, Muhammad Ashfaq, Niko Mäkitalo, Tommi Mikkonen , "Human-LLM Synergy in Context-Aware Adaptive Architecture for Scalable Drone Swarm Operation", 18th International Conference on Agents and Artificial Intelligence , 2026.
AbstractThe deployment of autonomous drone swarms in disaster response missions necessitates the development of flexible, scalable, and robust coordination systems. Traditional fixed architectures struggle to cope with dynamic and unpredictable environments, leading to inefficiencies in energy consumption and connectivity. This paper addresses this gap by proposing an adaptive architecture for drone swarms, leveraging a Large Language Model (LLM) to dyna...
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Jingyan Li, Sebastian Brulin, Nithin Santhanam, Sean Qian , "Understanding day-to-day traffic patterns during disruption: an incremental nonnegative matrix factorization approach", Transportation Research Board Annual Meeting, 2026.
AbstractThis research investigates daily traffic pattern changes resulting from significant network disruptions using data-driven incremental Non-negative Matrix Factorization (NMF). Employing high-resolution traffic speed data collected from the I-95 corridor in Pittsburg, the study analyzes how spatial-temporal traffic features evolve during normal and disrupted states. By integrating multi-stage incremental learning with route-based and topological re...
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Benedict Flade , "Relational Local Dynamic Maps for Advanced Driver Assistance Systems", 2026.
AbstractIntelligent transportation systems support humans in the driving task while increasing safety and comfort. Over recent decades, these systems have evolved from an ego-centered perspective to approaches that consider the ego vehicle as embedded in its surrounding environment. Holistic support requires awareness of both the ego state and the state of nearby entities, ranging from static infrastructure to dynamic traffic participants. To meet the...
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Manuel Dietrich, Alan Sarkisian, Thomas H Weisswange , "Bystander Privacy Implications of Robots in Everyday Spaces: A Scoping Review", 2026 21st ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2026.
AbstractThe advancement of AI is driving the integration of robots into everyday environments. The acceptance of these robots not only depends on direct users, but also on others who share these spaces, often referred to as bystanders or non-users. A frequently discussed prerequisite for acceptance is the proper handling of personal information as robots are equipped with means for environmental awareness, data inference, and human interaction. Although ...
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Jose Almeida, Steffen Limmer, Joao Soares, Maria Bresich, Guenther Raidl, Zita Vale , "Dataset of Electric Autonomous Dial-a-Ride Instances with Local Energy Communities and Electricity Tariffs", Data in Brief, 2026.
AbstractShared autonomous electric vehicle (AEV) fleets offer significant potential for decarbonizing urban mobility, but their efficient operation requires jointly optimizing passenger routing and battery charging under real-world energy pricing constraints. The electric autonomous dial-a-ride problem (e-ADARP) formalizes this challenge, and its integration with local energy communities (LECs) introduces further complexity by coupling fleet scheduling ...
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Ariq Koh, Melvin Wong, Jiao Liu, Caishun Chen, Thiago de Jesus de Araujo Rios, Stefan Menzel, Yew Soon Ong , "QD-LLMs: Quality-Diversity Optimization with LLMs for Generative Design Exploration", The Genetic and Evolutionary Computation Conference, 2026.
AbstractText-to-3D generative models synthesize 3D geometries from free-form natural language prompts, enabling the exploration of vast generative design spaces. These models present an opportunity in engineering design to systematically search for diverse, high-performing candidates within a target feature space. While the Quality-Diversity (QD) optimization paradigm supports structured diversity maintenance through discretized grid-based archives, exis...
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