@article{osterdiekhoff2026giving,
  title={Giving AI agents a sense of control facilitates reinforcement learning in multitasking scenarios},
  author={{\"O}sterdiekhoff, Annika and Heinrich, Nils Wendel and Ru{\ss}winkel, Nele and Kopp, Stefan},
  journal={Plos One},
  year={2026}
}

@article{https://doi.org/10.1111/tops.70049,
author = {Preuss, Kai and Hilton, Christopher and Gramann, Klaus and Russwinkel, Nele},
title = {How to Match Cognitive Model Predictions With EEG Data},
journal = {Topics in Cognitive Science},
volume = {n/a},
number = {n/a},
pages = {e70049},
keywords = {Electroencephalography, Cognitive modeling, Independent component analysis, Generalized least squares, Mental spatial transformation},
doi = {https://doi.org/10.1111/tops.70049},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/tops.70049},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/tops.70049},
abstract = {Abstract Reliably identifying relevant brain areas implicated by the simulated activity from cognitive models is still an unsolved problem for cognitive modeling, particularly when matching model output with human electroencephalography (EEG) data. We propose a new method involving postprocessing of ACT-R module activity and clustered EEG component activity, and performing generalized least squares analysis to find matching patterns between predicted and observed data, thereby inferring neural substrates of distinct cognitive processes. This approach holds several advantages over other methods by controlling for autocorrelation and unequal variances. To exemplify its application, we used a cognitive model and EEG data from a mental spatial transformation study to show how this method finds areas involved in representational and transformational spatial processing. Parietal areas involved with spatial activity were identified, in line with prior studies on spatial cognition. In addition, previously established associations between ACT-R and brain areas were confirmed. Finally, we discuss limitations and possibilities of the approach.}
}

@INPROCEEDINGS{10871032,
  author={Sievers, Thomas},
  booktitle={2025 IEEE/SICE International Symposium on System Integration (SII)}, 
  title={Silence is Golden - Making Pauses in Human Utterances Comprehensible for Social Robots in Human-Robot Interaction *}, 
  year={2025},
  volume={},
  number={},
  pages={526-531},
  keywords={Sentiment analysis;Large language models;Social robots;System integration;Speech recognition;Chatbots;Text to speech},
  doi={10.1109/SII59315.2025.10871032}}

@conference{icaart25,
author={Thomas Sievers and Nele Russwinkel and Ralf Möller},
title={Grounding a Social Robot’s Understanding of Words with Associations in a Cognitive Architecture},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={406-410},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013144200003890},
isbn={978-989-758-737-5},
issn={2184-433X},
}

@article{LuMoGe25,
	author    = {Malte Luttermann and Ralf Möller and Marcel Gehrke},
	title     = {{Lifting Factor Graphs with Some Unknown Factors for New Individuals}},
	journal   = {International Journal of Approximate Reasoning},
	volume    = {179},
	year      = {2025},
	pages     = {109371},
	publisher = {Elsevier},
}

@INPROCEEDINGS{11079485,
  author={Sievers, Thomas and Russwinkel, Nele},
  booktitle={2025 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)}, 
  title={Using Memory Contents of a Cognitive Model for Prompt Augmentation of a Large Language Model}, 
  year={2025},
  volume={},
  number={},
  pages={172-176},
  keywords={Large language models;Computational modeling;Social robots;Memory management;Decision making;Reliability;Large Language Model;ACT-R;Cognitive Model;Prompt Augmentation;Human-Robot Interaction},
  doi={10.1109/CogSIMA64436.2025.11079485}}

@Article{app15105778,
AUTHOR = {Sievers, Thomas and Russwinkel, Nele},
TITLE = {Retrieving Memory Content from a Cognitive Architecture by Impressions from Language Models for Use in a Social Robot},
JOURNAL = {Applied Sciences},
VOLUME = {15},
YEAR = {2025},
NUMBER = {10},
ARTICLE-NUMBER = {5778},
URL = {https://www.mdpi.com/2076-3417/15/10/5778},
ISSN = {2076-3417},
ABSTRACT = {Large Language Models (LLMs) and Vision-Language Models (VLMs) have the potential to significantly advance the development and application of cognitive architectures for human–robot interaction (HRI) to enable social robots with enhanced cognitive capabilities. An essential cognitive ability of humans is the use of memory. We investigate a way to create a social robot with a human-like memory and recollection based on cognitive processes for a better comprehensible and situational behavior of the robot. Using a combined system consisting of an Adaptive Control of Thought-Rational (ACT-R) model and a humanoid social robot, we show how recollections from the declarative memory of the ACT-R model can be retrieved using data obtained by the robot via an LLM or VLM, processed according to the procedural memory of the cognitive model and returned to the robot as instructions for action. Real-world data captured by the robot can be stored as memory chunks in the cognitive model and recalled, for example by means of associations. This opens up possibilities for using human-like judgment and decision-making capabilities inherent in cognitive architectures with social robots and practically offers opportunities of augmenting the prompt for LLM-driven utterances with content from declarative memory, thus keeping them more contextually relevant. We illustrate the use of such an approach in HRI scenarios with the social robot Pepper.},
DOI = {10.3390/app15105778}
}

@InProceedings{10.1007/978-981-95-2379-5_57,
author="Sievers, Thomas
and Russwinkel, Nele",
editor="Staffa, Mariacarla
and Cabibihan, John-John
and Siciliano, Bruno
and Ge, Shuzhi Sam
and Bodenhagen, Leon
and Tapus, Adriana
and Rossi, Silvia
and Cavallo, Filippo
and Fiorini, Laura
and Matarese, Marco
and He, Hongsheng",
title="A Path to Gradual Individual Experience and Recollection for Social Robots Based on a Cognitive Architecture",
booktitle="Social Robotics + AI",
year="2026",
publisher="Springer Nature Singapore",
address="Singapore",
pages="641--647",
abstract="Many attempts are aiming to provide social robots with human-like cognitive abilities such as memory, language comprehension and visual and spatial processing. Cognitive architectures in combination with Large Language Models (LLMs) have the potential to act as basic components for such a system. We demonstrate the use of an Adaptive Control of Thought-Rational (ACT-R) model in combination with an LLM to store experiences from human-robot interaction (HRI) in the declarative memory of the cognitive architecture for a humanoid social robot. These experiences can be retrieved from memory as associated recollections and used for the robot's actions and for prompt augmentation of the LLM. This type of memory also allows the creation, storage and updating of person models from interactions with different people, which enables the robot to get to know these people better through temporally unrelated interactions and to respond to them individually.",
isbn="978-981-95-2379-5"
}

@inproceedings{LuMoGe25,
    author    = {Malte Luttermann and Ralf Möller and Marcel Gehrke},
    title     = {{Lifted Model Construction without Normalisation: A Vectorised Approach to Exploit Symmetries in Factor Graphs}},
    booktitle = {Proceedings of the Third Learning on Graphs Conference (LoG-2024)},
    year      = {2025},
    pages     = {46:1-46:17},
    publisher = {{PMLR}},
}

@inproceedings{LuSpGeBrMoHa25,
	author    = {Malte Luttermann and Jan Speller and Marcel Gehrke and Tanya Braun and Ralf Möller and Mattis Hartwig},
	title     = {{Approximate Lifted Model Construction}},
	booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (IJCAI-2025)},
	year      = {2025},
	pages     = {9077--9085},
	publisher = {{IJCAI} Organization},
}

@inproceedings{SpLuGeBr25,
	author    = {Jan Speller and Malte Luttermann and Marcel Gehrke and Tanya Braun},
	title     = {{Towards Explainability of Approximate Lifted Model Construction: A Geometric Perspective}},
	booktitle = {Proceedings of the Eleventh Workshop on Formal and Cognitive Reasoning (FCR-2025)},
	year      = {2025},
	pages     = {41--56},
	publisher = {{CEUR}},
}

@inproceedings{SpLuGeBr25,
	author    = {Jan Speller and Malte Luttermann and Marcel Gehrke and Tanya Braun},
	title     = {{Compression versus Accuracy: A Hierarchy of Lifted Models}},
	booktitle = {Proceedings of the Twenty-Eighth European Conference on Artificial Intelligence (ECAI-2025)},
	year      = {2025},
	pages     = {5051--5058},
	publisher = {{IOS} Press},
}