The MFG group researches multi-robot systems, swarm robotics, and AI to develop autonomous, cooperative, and intelligent robotic solutions.

The micro-focus group Multiagent Learning (MAL) studies how multiple autonomous agents learn, coordinate, and interact in shared environments. Research topics include multi-agent reinforcement learning, communication, and applications in intelligent systems

The micro-focus group ML: Transfer, Domain Adaptation, Multi-Task Learning explores machine learning techniques that enable models to transfer knowledge across tasks and domains. Research topics include domain adaptation, multi-task learning, representation transfer, and improving model generalisation under data distribution shifts.

The micro-focus group DMKM: Mining of Spatial, Temporal or Spatio-Temporal Data focuses on machine learning and data mining methods for analysing patterns across space and time. Research topics include spatio-temporal clustering, trajectory mining, hotspot detection, and predictive modelling of dynamic geographic and temporal data.

The micro-focus group SNLP: Applications focuses on transforming natural language processing research into real-world systems, including conversational agents, summarisation, machine translation, and information extraction for domain-specific applications.

The micro-focus group DMKM: Web Search & Information Retrieval explores research on search engines, ranking models, and information retrieval methods for large-scale and intelligent data access.

This micro focus group focuses on learning and optimization methods for robotics, including adaptive control, intelligent planning, and data-driven robot learning.

This micro focus group explores research in motion analysis and object tracking, covering dynamic scene understanding and tracking algorithms.

This micro focus group focuses on advances in 3D computer vision, including spatial understanding, depth estimation, and 3D scene reconstruction.

This micro focus group explores AI methods for agriculture, focusing on intelligent farming, data-driven crop management, and sustainable agricultural technologies within the Association for the Advancement of Artificial Intelligence (AAAI) community.

This group focuses on real-world applications of machine learning, exploring how models can be designed, deployed, and evaluated to solve practical challenges in health, finance, mobility, sustainability, and beyond.

This group explores real-world applications of computer vision, focusing on how AI can extract and use visual information to solve practical problems in healthcare, mobility, industry, and environmental monitoring.

Diese Gruppe beschäftigt sich mit dem praktischen Einsatz von Robotik in realen Umgebungen, wobei KI mit robotischen Systemen kombiniert wird, um anspruchsvolle Aufgaben in Industrie, Dienstleistung und Alltag zu lösen. Im Mittelpunkt stehen sowohl die Umsetzung robotischer Technologien als auch die Bewertung ihrer gesellschaftlichen und operativen Auswirkungen.

This category explores models and systems that emulate human-like cognition by integrating perception, reasoning, learning, memory, and decision-making. Research includes symbolic, connectionist, hybrid, and embodied approaches to building autonomous agents that adapt over time and act in goal-directed, context-aware ways. The aim is to design cognitive architectures that reflect core aspects of human or animal intelligence.

This category focuses on machine learning methods with built-in privacy protections to safeguard sensitive data during training, inference, and deployment. It includes approaches such as differential privacy, secure multi-party computation, homomorphic encryption, and privacy-preserving federated learning. Research addresses the trade-off between model performance and data confidentiality.

ese Kategorie befasst sich mit maschinellem Lernen in dezentralen oder verteilten Umgebungen, in denen Daten und Rechenressourcen über mehrere Geräte oder Organisationen verteilt sind. Im Fokus stehen Verfahren für gemeinsames Modelltraining ohne zentrale Datenteilung sowie Herausforderungen wie Kommunikationseffizienz, Datenschutz, Heterogenität und Systemrobustheit.

This category focuses on computational models of brain function and structure, using simulation, mathematical modeling, and machine learning to study neural mechanisms. Topics include neural coding, cognitive architectures, brain-inspired computing, and large-scale brain network simulations. The research bridges neuroscience, AI, and cognitive science, spanning both biologically detailed and abstract models.

Human-in-the-loop Machine Learning

Diese Kategorie befasst sich mit der Interpretierbarkeit und Transparenz von NLP-Modellen. Sie umfasst Methoden zur Analyse der Sprachverarbeitung, Erklärung von Modellentscheidungen, Aufdeckung von Verzerrungen und Verhaltensanalysen über verschiedene Aufgaben und Sprachen hinweg. Ziel ist es, NLP-Systeme verständlicher, vertrauenswürdiger und besser überprüfbar zu machen.

This category explores how AI systems can integrate moral reasoning, ethical principles, and human values. Research includes modeling and learning moral norms, addressing ethical dilemmas, and enabling value-sensitive decision-making. Approaches range from computational frameworks to interdisciplinary methods involving philosophy, law, and the social sciences. Example topics include fairness-aware reinforcement learning, logic-based modeling of moral dilemmas, and culturally sensitive value alignment for AI assistants.

This category focuses on algorithms that enable AI systems to anticipate, interpret, and adapt to human behavior in dynamic settings. It includes methods for predicting intentions, goals, and actions, and for planning with human preferences, beliefs, and uncertainty in mind. Applications span human-robot interaction, collaborative systems, autonomous vehicles, and assistive technologies. Examples include pedestrian trajectory prediction, adaptive robot motion planning, and driver intent modeling in mixed-autonomy traffic.

This category covers computational models and algorithms for interpreting brain activity and cognitive states using brain-sensing technologies such as EEG, fMRI, MEG, and NIRS. Applications include brain-computer interfaces, neuroadaptive systems, and cognitive state modeling. Contributions span signal processing, machine learning, human-AI interaction, and hybrid neuro-symbolic methods. Examples include mental workload classification from EEG, decision prediction from fMRI, and real-time BCI systems using graph neural networks.

This category covers supervised learning methods for predicting classes or continuous values from labeled data. Techniques include neural networks, decision trees, support vector machines, and ensemble models, with a focus on building models that generalize well. Applications range from cancer subtype prediction and drug response estimation to cell type classification in single-cell RNA-seq data.

This category focuses on unsupervised learning methods for uncovering structure in unlabeled datasets by grouping similar data points. Techniques such as k-means, hierarchical clustering, density-based models, and deep clustering frameworks are used to explore data, generate hypotheses, and enable tasks like patient stratification. Example applications include identifying novel disease subtypes from multi-omics data with spectral clustering, discovering gene modules with shared expression across tissues, and grouping patients based on metabolomics profiles using deep embedded clustering.