2024W

• Bachelor Thesis for Computer Science and Business Informatics / 192.143 / PR
• Deep Learning for Natural Language Processing / 192.039 / VU
• Project in Computer Science 1 / 192.021 / PR
• Project in Computer Science 2 / 192.022 / PR
• Scientific Research and Writing / 193.052 / SE
• Seminar for Master Students in Logic and Computation / 180.773 / SE
• Seminar for PhD Students / 192.031 / SE
• Seminar in Artificial Intelligence : Neuroscience-based Artificial Intelligence / 192.047 / SE
• Seminar in Knowledge Representation and Reasoning : Neurosymbolic Artificial Intelligence / 192.048 / SE

2025S

• Bachelor Thesis for Computer Science and Business Informatics / 192.143 / PR
• Deep Learning for Natural Language Processing / 192.039 / VU
• Introduction to Deep Learning / 192.151 / VU
• Project in Computer Science 1 / 192.021 / PR
• Project in Computer Science 2 / 192.022 / PR
• Scientific Research and Writing / 193.052 / SE
• Seminar for Master Students in Logic and Computation / 180.773 / SE
• Seminar for PhD Students / 192.031 / SE
• Seminar in Artificial Intelligence : Neuroscience-based Artificial Intelligence / 192.047 / SE
• Seminar in Knowledge Representation and Reasoning : Neurosymbolic Artificial Intelligence / 192.048 / SE

• Explainable Artificial Intelligence in Healthcare
  2023 – 2027 / AXA
• Game-Theoretic Agent Programming
  2005 – 2008 / Austrian Science Fund (FWF)

2025

• Aggregated Mutual Learning between CNN and Transformer for semi-supervised medical image segmentation / Xu, Z., Wang, H., Yang, R., Yang, Y., Liu, W., & Lukasiewicz, T. (2025). Aggregated Mutual Learning between CNN and Transformer for semi-supervised medical image segmentation. Knowledge-Based Systems, 311, Article 113005. https://doi.org/10.1016/j.knosys.2025.113005
• Hybrid Reinforced Medical Report Generation With M-Linear Attention and Repetition Penalty / Xu, Z., Xu, W., Wang, R., Chen, J., Qi, C., & Lukasiewicz, T. (2025). Hybrid Reinforced Medical Report Generation With M-Linear Attention and Repetition Penalty. IEEE Transactions on Neural Networks and Learning Systems, 36(2), 2206–2220. https://doi.org/10.1109/TNNLS.2023.3343391
• Self-Supervised Medical Image Segmentation Using Deep Reinforced Adaptive Masking / Xu, Z., Liu, Y., Xu, G., & Lukasiewicz, T. (2025). Self-Supervised Medical Image Segmentation Using Deep Reinforced Adaptive Masking. IEEE Transactions on Medical Imaging, 44(1), 180–193. https://doi.org/10.1109/TMI.2024.3436608

2024

• Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting / Kayser, M., Menzat, B. I., Emde, C., Bercean, B., Novak, A., Espinosa, A., Papiez, B. W., Gaube, S., Lukasiewicz, T., & Camburu, O.-M. (2024). Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 18891–18919). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.1051
• CCN⁺: A Neuro-symbolic Framework for Deep Learning with Requirements   / Giunchiglia, E., Tatomir, A., Stoian, M. C., & Lukasiewicz, T. (2024). CCN+: A Neuro-symbolic Framework for Deep Learning with Requirements  . International Journal of Approximate Reasoning, 171, Article 109124. https://doi.org/10.1016/j.ijar.2024.109124
• Correcting Flaws in Common Disentanglement Metrics / Mahon, L., Sha, L., & Lukasiewicz, T. (2024). Correcting Flaws in Common Disentanglement Metrics. Transactions on Machine Learning Research, 2024.
• Evaluating Language Models for Mathematics through Interactions / Collins, K. M., Jiang, A. Q., Frieder, S., Wong, L., Zilka, M., Bhatt, U., Lukasiewicz, T., Wu, Y., Tenenbaum, J. B., Hart, W., Gowers, T., Li, W., Weller, A., & Jamnik, M. (2024). Evaluating Language Models for Mathematics through Interactions. Proceedings of the National Academy of Sciences of the United States of America, 121(24), Article e2318124121. https://doi.org/10.1073/pnas.2318124121
• Pre-training and diagnosing knowledge base completion models / Kocijan, V., Jang, M., & Lukasiewicz, T. (2024). Pre-training and diagnosing knowledge base completion models. Artificial Intelligence, 329, Article 104081. https://doi.org/10.1016/j.artint.2024.104081
• Hard Regularization to Prevent Deep Online Clustering Collapse without Data Augmentation / Mahon, L., & Lukasiewicz, T. (2024). Hard Regularization to Prevent Deep Online Clustering Collapse without Data Augmentation. In Proceedings of the 38th AAAI Conference on Artificial Intelligence : AAAI-24 Technical Tracks 13 (pp. 14281–14288). AAAI Press. https://doi.org/10.1609/aaai.v38i13.29340
• Automatic data augmentation for medical image segmentation using Adaptive Sequence-length based Deep Reinforcement Learning / Xu, Z., Wang, S., Xu, G., Liu, Y., Yu, M., Zhang, H., Lukasiewicz, T., & Gu, J. (2024). Automatic data augmentation for medical image segmentation using Adaptive Sequence-length based Deep Reinforcement Learning. Computers in Biology and Medicine, 169, Article 107877. https://doi.org/10.1016/J.COMPBIOMED.2023.107877
• Minimum description length clustering to measure meaningful image complexity / Mahon, L., & Lukasiewicz, T. (2024). Minimum description length clustering to measure meaningful image complexity. Pattern Recognition, 145, Article 109889. https://doi.org/10.1016/j.patcog.2023.109889
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• PhaCIA-TCNs: Short-Term Load Forecasting Using Temporal Convolutional Networks With Parallel Hybrid Activated Convolution and Input Attention / Xu, Z., Yu, Z., Zhang, H., Chen, J., Gu, J., Lukasiewicz, T., & Leung, V. C. M. (2024). PhaCIA-TCNs: Short-Term Load Forecasting Using Temporal Convolutional Networks With Parallel Hybrid Activated Convolution and Input Attention. IEEE Transactions on Network Science and Engineering, 11(1), 427–438. https://doi.org/10.1109/TNSE.2023.3300744
• A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks / Tommaso Salvatori, Song, Y., Yordanov, Y., Millidge, B., Sha, L., Emde, C., Xu, Z., Bogacz, R., & Thomas Lukasiewicz. (2024). A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks. In The Twelfth International Conference on Learning Representations (p. 25). http://hdl.handle.net/20.500.12708/211106
• Multi-ConDoS: Multimodal Contrastive Domain Sharing Generative Adversarial Networks for Self-Supervised Medical Image Segmentation / Zhang, J., Zhang, S., Shen, X., Lukasiewicz, T., & Xu, Z. (2024). Multi-ConDoS: Multimodal Contrastive Domain Sharing Generative Adversarial Networks for Self-Supervised Medical Image Segmentation. IEEE Transactions on Medical Imaging, 43(1), 76–95. https://doi.org/10.1109/TMI.2023.3290356
• Text attribute control via closed-loop disentanglement / Sha, L., & Thomas Lukasiewicz. (2024). Text attribute control via closed-loop disentanglement. Transactions of the Association for Computational Linguistics, 12. https://doi.org/10.1162/tacl_a_00640
• The IMO Small Challenge: Not-Too-Hard Olympiad Math Datasets for LLMs / Frieder, S., Olšák, M., Berner, J., & Lukasiewicz, T. (2024). The IMO Small Challenge: Not-Too-Hard Olympiad Math Datasets for LLMs. In The Second Tiny Papers Track at ICLR 2024. The Twelfth International Conference on Learning Representations (ICLR 2024), Wien, Austria. http://hdl.handle.net/20.500.12708/210292
• How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data / Stoian, M. C., Dyrmishi, S., Cordy, M., Lukasiewicz, T., & Giunchiglia, E. (2024). How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data. In The Twelfth International Conference on Learning Representations. 12th International Conference on Learning Representations (ICLR 2024), Wien, Austria. http://hdl.handle.net/20.500.12708/210296
• Predictive Coding beyond Correlations / Salvatori, T., Pinchetti, L., M’Charrak, A., Millidge, B., & Lukasiewicz, T. (2024). Predictive Coding beyond Correlations. In Forty-first International Conference on Machine Learning. Forty-first International Conference on Machine Learning, ICML 2024, Wien, Austria. http://hdl.handle.net/20.500.12708/210295
• PiShield: A NeSy Framework for Learning with Requirements / Stoian, M. C., Tatomir, A., Lukasiewicz, T., & Giunchiglia, E. (2024). PiShield: A NeSy Framework for Learning with Requirements. In K. Larson (Ed.), IJCAI ’24: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (pp. 8805–8809). Association for Computing Machinery. https://doi.org/10.24963/ijcai.2024/1037
• Inferring neural activity before plasticity as a foundation for learning beyond backpropagation / Song, Y., Millidge, B., Salvatori, T., Lukasiewicz, T., Xu, Z., & Bogacz, R. (2024). Inferring neural activity before plasticity as a foundation for learning beyond backpropagation. Nature Neuroscience. https://doi.org/10.1038/s41593-023-01514-1
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• Bad Predictive Coding Activation Functions / Frieder, S., Pinchetti, L., & Lukasiewicz, T. (2024). Bad Predictive Coding Activation Functions. In The Second Tiny Papers Track at ICLR 2024. The Twelfth International Conference on Learning Representations (ICLR 2024), Wien, Austria.
• Cross-domain attention-guided generative data augmentation for medical image analysis with limited data / Xu, Z., Tang, J., Qi, C., Yao, D., Liu, C., Zhan, Y., & Lukasiewicz, T. (2024). Cross-domain attention-guided generative data augmentation for medical image analysis with limited data. Computers in Biology and Medicine, 168, Article 107744. https://doi.org/10.1016/J.COMPBIOMED.2023.107744

2023

• Large Language Models for Mathematicians / Frieder, S., Berner, J., Petersen, P., & Lukasiewicz, T. (2023). Large Language Models for Mathematicians. Internationale Mathematische Nachrichten, 254, 1–20. http://hdl.handle.net/20.500.12708/192474
• RIRGAN: An end-to-end lightweight multi-task learning method for brain MRI super-resolution and denoising / Yu, M., Guo, M., Zhang, S., Zhan, Y., Zhao, M., Lukasiewicz, T., & Xu, Z. (2023). RIRGAN: An end-to-end lightweight multi-task learning method for brain MRI super-resolution and denoising. Computers in Biology and Medicine, 167, Article 107632. https://doi.org/10.1016/j.compbiomed.2023.107632
• The Defeat of the Winograd Schema Challenge / Kocijan, V., Davis, E., Lukasiewicz, T., Marcus, G., & Morgenstern, L. (2023). The Defeat of the Winograd Schema Challenge. Artificial Intelligence, 325, Article 103971. https://doi.org/10.1016/j.artint.2023.103971
• Collaborative Attention Guided Multi-Scale Feature Fusion Network for Medical Image Segmentation / Xu, Z., Tian, B., Liu, S., Wang, X., Yuan, D., Gu, J., Chen, J., Lukasiewicz, T., & Leung, V. C. M. (2023). Collaborative Attention Guided Multi-Scale Feature Fusion Network for Medical Image Segmentation. IEEE Transactions on Network Science and Engineering, 1–15. https://doi.org/10.1109/TNSE.2023.3332810
• EFPN: Effective medical image detection using feature pyramid fusion enhancement / Xu, Z., Zhang, X., Zhang, H., Liu, Y., Zhan, Y., & Lukasiewicz, T. (2023). EFPN: Effective medical image detection using feature pyramid fusion enhancement. Computers in Biology and Medicine, 163, Article 107149. https://doi.org/https://doi.org/10.1016/j.compbiomed.2023.107149
• ROAD-R: the autonomous driving dataset with logical requirements / Giunchiglia, E., Stoian, M. C., Khan, S., Cuzzolin, F., & Lukasiewicz, T. (2023). ROAD-R: the autonomous driving dataset with logical requirements. Machine Learning, 112, 3261–3291. https://doi.org/10.1007/s10994-023-06322-z
• μ-Net: Medical image segmentation using efficient and effective deep supervision / Yuan, D., Xu, Z., Tian, B., Wang, H., Zhan, Y., & Lukasiewicz, T. (2023). μ-Net: Medical image segmentation using efficient and effective deep supervision. Computers in Biology and Medicine, 160, Article 106963. https://doi.org/10.1016/j.compbiomed.2023.106963
• Recurrent predictive coding models for associative memory employing covariance learning / Tang, M., Salvatori, T., Millidge, B., Song, Y., Lukasiewicz, T., & Bogacz, R. (2023). Recurrent predictive coding models for associative memory employing covariance learning. PLoS Computational Biology, 19(4), e1010719. https://doi.org/10.1371/journal.pcbi.1010719
• PAC-Net: Multi-pathway FPN with position attention guided connections and vertex distance IoU for 3D medical image detection / Xu, Z., Li, T., Liu, Y., Zhan, Y., Chen, J., & Lukasiewicz, T. (2023). PAC-Net: Multi-pathway FPN with position attention guided connections and vertex distance IoU for 3D medical image detection. Frontiers in Bioengineering and Biotechnology, 11, Article 1049555. https://doi.org/10.3389/fbioe.2023.1049555
• Painless and accurate medical image analysis using deep reinforcement learning with task-oriented homogenized automatic pre-processing / Yuan, D., Liu, Y., Xu, Z., Zhan, Y., Chen, J., & Lukasiewicz, T. (2023). Painless and accurate medical image analysis using deep reinforcement learning with task-oriented homogenized automatic pre-processing. Computers in Biology and Medicine, 153, Article 106487. https://doi.org/10.1016/j.compbiomed.2022.106487
• Rationalizing predictions by adversarial information calibration / Sha, L., Camburu, O.-M., & Lukasiewicz, T. (2023). Rationalizing predictions by adversarial information calibration. Artificial Intelligence, 315, 103828. https://doi.org/https://doi.org/10.1016/j.artint.2022.103828
• Hi-BEHRT: Hierarchical Transformer-Based Model for Accurate Prediction of Clinical Events Using Multimodal Longitudinal Electronic Health Records / Li, Y., Mamouei, M., Salimi-Khorshidi, G., Rao, S., Hassaine, A., Canoy, D., Lukasiewicz, T., & Rahimi, K. (2023). Hi-BEHRT: Hierarchical Transformer-Based Model for Accurate Prediction of Clinical Events Using Multimodal Longitudinal Electronic Health Records. IEEE Journal of Biomedical and Health Informatics, 27(2), 1106–1117. https://doi.org/10.1109/JBHI.2022.3224727
• Consistency Analysis of ChatGPT / Jang, M., & Lukasiewicz, T. (2023). Consistency Analysis of ChatGPT. In H. Bouamor, J. Pino, & K. Bali (Eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 15970–15985). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.991
• Mathematical Capabilities of ChatGPT / Frieder, S., Pinchetti, L., Chevalier, A., Griffiths, R.-R., Salvatori, T., Lukasiewicz, T., Petersen, P., & Berner, J. (2023). Mathematical Capabilities of ChatGPT. In Advances in Neural Information Processing Systems 36 pre-proceedings (NeurIPS 2023). 37th Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, United States of America (the). http://hdl.handle.net/20.500.12708/194133
• MvCo-DoT: Multi-View Contrastive Domain Transfer Network for Medical Report Generation / Wang, R., Wang, X., Xu, Z., Xu, W., Chen, J., & Lukasiewicz, T. (2023). MvCo-DoT: Multi-View Contrastive Domain Transfer Network for Medical Report Generation. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2023 International Conference on Acoustics, Speech, and Signal Processing, Rhodes, Greece. IEEE. https://doi.org/10.1109/ICASSP49357.2023.10095254
• MPS-AMS: Masked Patches Selection and Adaptive Masking Strategy Based Self-Supervised Medical Image Segmentation / Wang, X., Wang, R., Tian, B., Zhang, J., Zhang, S., Chen, J., Lukasiewicz, T., & Xu, Z. (2023). MPS-AMS: Masked Patches Selection and Adaptive Masking Strategy Based Self-Supervised Medical Image Segmentation. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Rhodes, Greece. IEEE. https://doi.org/10.1109/ICASSP49357.2023.10094657
• Associative Memories in the Feature Space / Salvatori, T., Millidge, B., Song, Y., Bogacz, R., & Lukasiewicz, T. (2023). Associative Memories in the Feature Space. In K. Gal, A. Nowé, & G. J. Nalepa (Eds.), 26th European Conference on Artificial Intelligence, September 30–October 4, 2023, Kraków, Poland – Including 12th Conference on Prestigious Applications of Intelligent Systems (PAIS 2023) (pp. 2065–2072). IOS Press. https://doi.org/10.3233/FAIA230500
• Multi-Head Feature Pyramid Networks for Breast Mass Detection / Zhang, H., Xu, Z., Yao, D., Zhang, S., Chen, J., & Thomas Lukasiewicz. (2023). Multi-Head Feature Pyramid Networks for Breast Mass Detection. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Rhodes, Greece. IEEE. https://doi.org/10.1109/ICASSP49357.2023.10095967
• Adaptive-Masking Policy with Deep Reinforcement Learning for Self-Supervised Medical Image Segmentation / Xu, G., Wang, S., Lukasiewicz, T., & Xu, Z. (2023). Adaptive-Masking Policy with Deep Reinforcement Learning for Self-Supervised Medical Image Segmentation. In 2023 IEEE International Conference on Multimedia and Expo (ICME) (pp. 2285–2290). IEEE. https://doi.org/10.1109/ICME55011.2023.00390
• NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation / Wang, J., Massiceti, D., Hu, X., Pavlovic, V., & Lukasiewicz, T. (2023). NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), PMLR Proceedings of Machine Learning Research. http://hdl.handle.net/20.500.12708/192515
• KNOW How to Make Up Your Mind! Adversarially Detecting and Remedying Inconsistencies in Natural Language Explanations / Jang, M., Majumder, B. P., McAuley, J., Lukasiewicz, T., & Camburu, O.-M. (2023). KNOW How to Make Up Your Mind! Adversarially Detecting and Remedying Inconsistencies in Natural Language Explanations. In A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 540–553). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-short.47
• Exploiting T-norms for Deep Learning in Autonomous Driving / Stoian, M. C., Giunchiglia, E., & Lukasiewicz, T. (2023). Exploiting T-norms for Deep Learning in Autonomous Driving. In A. S. d’Avila Garcez, T. R. Besold, M. Gori, & E. Jimenez-Ruiz (Eds.), Proceedings of the 17th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy 2023) (pp. 369–380). http://hdl.handle.net/20.500.12708/193631
• Faithfulness Tests for Natural Language Explanations / Atanasova, P., Camburu, O.-M., Lioma, C., Lukasiewicz, T., Simonsen, J. G., & Augenstein, I. (2023). Faithfulness Tests for Natural Language Explanations. In Association for Computational Linguistics (Ed.), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 283–294). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-short.25
• Complexity of Inconsistency-Tolerant Query Answering in Datalog+/- under Preferred Repairs / Lukasiewicz, T., Malizia, E., & Molinaro, C. (2023). Complexity of Inconsistency-Tolerant Query Answering in Datalog+/- under Preferred Repairs. In P. Marquis, T. C. Son, & G. Kern-Isberner (Eds.), Proceedings of 20th International Conference on Principles of Knowledge Representation and Reasoning (pp. 472–481). IJCAI Organization. https://doi.org/10.24963/kr.2023/46
• Counter−GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns / Zhongbin, X., Kocijan, V., Lukasiewicz, T., & Camburu, O.-M. (2023). Counter−GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns. In A. Vlachos & Isabelle Augenstein (Eds.), Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (pp. 3761–3773). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.eacl-main.272
• An Empirical Analysis of Parameter-Efficient Methods for Debiasing Pre-Trained Language Models / Xie, Z., & Lukasiewicz, T. (2023). An Empirical Analysis of Parameter-Efficient Methods for Debiasing Pre-Trained Language Models. In In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 15730–15745). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.876
• Efficient Deep Clustering of Human Activities and How to Improve Evaluation / Mahon, L., & Lukasiewicz, T. (2023). Efficient Deep Clustering of Human Activities and How to Improve Evaluation. In E. Khan & M. Gönen (Eds.), Proceedings of Machine Learning Research 189, 2022 (pp. 722–737). http://hdl.handle.net/20.500.12708/193628
• Improving Language Models’ Meaning Understanding and Consistency by Learning Conceptual Roles from Dictionary / Jang, M., & Lukasiewicz, T. (2023). Improving Language Models’ Meaning Understanding and Consistency by Learning Conceptual Roles from Dictionary. In H. Bouamor, J. Pino, & K. Bali (Eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 8496–8510). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.527
• Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning / Millidge, B., Song, Y., Salvatori, T., Lukasiewicz, T., & Bogacz, R. (2023). Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning. In The Eleventh International Conference on Learning Representations, ICLR 2023 (pp. 1–14). http://hdl.handle.net/20.500.12708/192480
• A Theoretical Framework for Inference and Learning in Predictive Coding Networks / Millidge, B., Song, Y., Salvatori, T., Lukasiewicz, T., & Bogacz, R. (2023). A Theoretical Framework for Inference and Learning in Predictive Coding Networks. In The Eleventh International Conference on Learning Representations, ICLR 2023 (pp. 1–24). http://hdl.handle.net/20.500.12708/192478

2022

• BECEL: Benchmark for Consistency Evaluation of Language Models / Jang, M., Kwon, D. S., & Lukasiewicz, T. (2022). BECEL: Benchmark for Consistency Evaluation of Language Models. In N. Calzolari, C.-R. Huang, & H. Kim (Eds.), Proceedings of the 29th International Conference on Computational Linguistics (pp. 3680–3696). International Committee on Computational Linguistics. http://hdl.handle.net/20.500.12708/192675
• Clustering Generative Adversarial Networks for Story Visualization / Li, B., Torr, P. H. S., & Lukasiewicz, T. (2022). Clustering Generative Adversarial Networks for Story Visualization. In MM ’22: Proceedings of the 30th ACM International Conference on Multimedia (pp. 769–778). Association for Computing Machinery. https://doi.org/10.1145/3503161.3548034
• Explaining Chest X-Ray Pathologies in Natural Language / Kayser, M., Emde, C., Camburu, O.-M., Parsons, G., Papiez, B., & Lukasiewicz, T. (2022). Explaining Chest X-Ray Pathologies in Natural Language. In L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, & S. Li (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 (pp. 701–713). https://doi.org/10.1007/978-3-031-16443-9_67
• NoiER: An Approach for Training more Reliable Fine-Tuned Downstream Task Models / Jang, M., & Thomas Lukasiewicz. (2022). NoiER: An Approach for Training more Reliable Fine-Tuned Downstream Task Models. IEEE/ACM Transactions on Audio, Speech and Language Processing, 30, 2514–2525. https://doi.org/10.1109/TASLP.2022.3193292
• NP-Match: When Neural Processes meet Semi-Supervised Learning / Wang, J., Lukasiewicz, T., Massiceti, D., Hu, X., Pavlovic, V., & Neophytou, A. (2022). NP-Match: When Neural Processes meet Semi-Supervised Learning. In Proceedings of the 39th International Conference on Machine Learning (pp. 22919–22934). PMLR. http://hdl.handle.net/20.500.12708/192517
• Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations / Majumder, B. P., Camburu, O.-M., Lukasiewicz, T., & McAuley, J. (2022). Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations. In K. Chaudhuri, S. Jegelka, & L. Song (Eds.), Proceedings of the 39th International Conference on Machine Learning (pp. 14786–14801). MLResearch Press. http://hdl.handle.net/20.500.12708/192473
• Explanations for Negative Query Answers under Inconsistency-Tolerant Semantics / Lukasiewicz, T., Malizia, E., & Molinaro, C. (2022). Explanations for Negative Query Answers under Inconsistency-Tolerant Semantics. In L. De Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (pp. 2705–2711). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2022/375
• Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup / Yordanov, Y., Kocijan, V., Lukasiewicz, T., & Camburu, O.-M. (2022). Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup. In Y. Goldberg, K. Zornitsa, & Y. Zhang (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2022 (pp. 3486–3501). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.findings-emnlp.255
• Image-to-Image Translation with Text Guidance / Li, B., Torr, P. H. S., & Lukasiewicz, T. (2022). Image-to-Image Translation with Text Guidance. In The 33rd British Machine Vision Conference Proceedings (pp. 1–14). http://hdl.handle.net/20.500.12708/193629
• Deep Learning with Logical Constraints / Giunchiglia, E., Stoian, M. C., & Lukasiewicz, T. (2022). Deep Learning with Logical Constraints. In L. De Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (pp. 5478–5485). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2022/767
• Syntactically Rich Discriminative Training: An Effective Method for Open Information Extraction / Mtumbuka, F., & Lukasiewicz, T. (2022). Syntactically Rich Discriminative Training: An Effective Method for Open Information Extraction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 5972–5987). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.401
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• Memory-Driven Text-to-Image Generation / Li, B., Torr, P. H. S., & Lukasiewicz, T. (2022). Memory-Driven Text-to-Image Generation. In The 33rd British Machine Vision Conference Proceedings. 33rd British Machine Vision Conference, London, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/193654
• Learning to Model Multimodal Semantic Alignment for Story Visualization / Li, B., & Lukasiewicz, T. (2022). Learning to Model Multimodal Semantic Alignment for Story Visualization. In Findings of the Association for Computational Linguistics: EMNLP 2022 (pp. 4741–4747). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.findings-emnlp.346
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• Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation? / Millidge, B., Salvatori, T., Song, Y., Bogacz, R., & Lukasiewicz, T. (2022). Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation? In L. De Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22) (pp. 5538–5545). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2022/774
• Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models / Millidge, B., Salvatori, T., Song, Y., Lukasiewicz, T., & Bogacz, R. (2022). Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models. In Proceedings of the 39th International Conference on Machine Learning (pp. 15561–15583). http://hdl.handle.net/20.500.12708/192477
• (Non-)Convergence Results for Predictive Coding Networks / Frieder, S., & Lukasiewicz, T. (2022). (Non-)Convergence Results for Predictive Coding Networks. In Proceedings of the 39th International Conference on Machine Learning (pp. 6793–6810). http://hdl.handle.net/20.500.12708/187543
• Predictive Coding beyond Gaussian Distributions / Pinchetti, L., Salvatori, T., Yordanov, Y., Millidge, B., Song, Y., & Lukasiewicz, T. (2022). Predictive Coding beyond Gaussian Distributions. In S. Koyejo, S. Mohamed, & A. Agarwal (Eds.), Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (pp. 1280–1293). http://hdl.handle.net/20.500.12708/192691
• Democratizing Financial Knowledge Graph Construction by Mining Massive Brokerage Research Reports / Cheng, Z., Wu, L., Thomas Lukasiewicz, Emanuel Sallinger, & Georg Gottlob. (2022). Democratizing Financial Knowledge Graph Construction by Mining Massive Brokerage Research Reports. In M. Ramanath & T. Palpanas (Eds.), Proceedings of the Workshops of the EDBT/ICDT 2022 Joint Conference. http://hdl.handle.net/20.500.12708/192765
• Learning on Arbitrary Graph Topologies via Predictive Coding / Salvatori, T., Pinchetti, L., Millidge, B., Song, Y., Bao, T., Bogacz, R., & Lukasiewicz, T. (2022). Learning on Arbitrary Graph Topologies via Predictive Coding. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (pp. 38232–38244). Neural information processing systems foundation. http://hdl.handle.net/20.500.12708/192475
• Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence / Jang, M., Mtumbuka, F., & Lukasiewicz, T. (2022). Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence. In Findings of the Association for Computational Linguistics: NAACL 2022 (pp. 2030–2042). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.findings-naacl.156

2016

• Generalized Consistent Query Answering under Existential Rules / Eiter, T., Lukasiewicz, T., & Predoiu, L. (2016). Generalized Consistent Query Answering under Existential Rules. In J. P. Delgrande & F. Wolter (Eds.), Proceedings, Fifteenth International Conference on Principles of Knowledge Representation and Reasoning (KR 2016) (pp. 359–368). http://hdl.handle.net/20.500.12708/56833

2015

• From Classical to Consistent Query Answering under Existential Rules / Lukasiewicz, T., Martinez, M. V., Pieris, A., & Simari, G. I. (2015). From Classical to Consistent Query Answering under Existential Rules. In Proceedings of the 9th Alberto Mendelzon International Workshop on Foundations of Data Management, Lima, Peru, May 6 - 8, 2015 (p. 6). CEUR Workshop Proceedings. http://hdl.handle.net/20.500.12708/56409
  Project: START (2014–2022)
• From Classical to Consistent Query Answering under Existential Rules / Lukasiewicz, T., Martinez, M. V., Pieris, A., & Simari, G. I. (2015). From Classical to Consistent Query Answering under Existential Rules. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (pp. 1546–1552). AAAI Press. http://hdl.handle.net/20.500.12708/56408
  Project: START (2014–2022)

2012

• Uniform Evaluation of Nonmonotonic DL-Programs / Eiter, T., Krennwallner, T., Schneider, P., & Xiao, G. (2012). Uniform Evaluation of Nonmonotonic DL-Programs. In T. Lukasiewicz & A. Sali (Eds.), Foundations of Information and Knowledge Systems (pp. 1–22). Springer. https://doi.org/10.1007/978-3-642-28472-4_1
  Projects: HEX-Programme (2008–2012) / Hybride Wissensbasen (2008–2012)

2011

• Well-founded semantics for description logic programs in the Semantic Web / Eiter, T., Ianni, G., Lukasiewicz, T., & Schindlauer, R. (2011). Well-founded semantics for description logic programs in the Semantic Web. ACM Transactions on Computational Logic, 12(2), 1–41. https://doi.org/10.1145/1877714.1877717

2010

• Tightly coupled fuzzy description logic programs under the answer set semantics for the Semantic Web / Lukasiewicz, T., & Straccia, U. (2010). Tightly coupled fuzzy description logic programs under the answer set semantics for the Semantic Web. In M. Lytras & A. Sheth (Eds.), Progressive Concepts for Semantic Web Evolution: Applications and Developments (pp. 237–256). Information Science Reference. https://doi.org/10.4018/978-1-60566-992-2.ch011
• A novel combination of answer set programming with description logics for the Semantic Web / Lukasiewicz, T. (2010). A novel combination of answer set programming with description logics for the Semantic Web. IEEE Transactions on Knowledge and Data Engineering, 22(11), 1577–1592. https://doi.org/10.1109/tkde.2010.111
• Datalog+/-: A Family of Logical Knowledge Representation and Query Languages for New Applications / Calì, A., Gottlob, G., Lukasiewicz, T., Marnette, B., & Pieris, A. (2010). Datalog+/-: A Family of Logical Knowledge Representation and Query Languages for New Applications. In J.-P. Jouannaud (Ed.), 2010 25th Annual IEEE Symposium on Logic in Computer Science. IEEE Computer Society. https://doi.org/10.1109/lics.2010.27
• Ontological reasoning with F-Logic Lite and its extensions / Cali, A., Gottlob, G., Kifer, M., Lukasiewicz, T., & Pieris, A. (2010). Ontological reasoning with F-Logic Lite and its extensions. In M. Fox & D. Poole (Eds.), Proceedings of the 24th National Conference on Artificial Intelligence (AAAI 2010) (pp. 1660–1665). AAAI Press. http://hdl.handle.net/20.500.12708/53559
• Datalog extensions for tractable query answering over ontologies / Cali, A., Gottlob, G., & Lukasiewicz, T. (2010). Datalog extensions for tractable query answering over ontologies. In R. De Virgilio, F. Giunchiglia, & L. Tanca (Eds.), Semantic Web Information Management: A Model-Based Perspective (pp. 249–279). Springer. http://hdl.handle.net/20.500.12708/27045
• Redundancy Elimination on RDF Graphs in the Presence of Rules, Constraints, and Queries / Pichler, R., Polleres, A., Skritek, S., & Woltran, S. (2010). Redundancy Elimination on RDF Graphs in the Presence of Rules, Constraints, and Queries. In P. Hitzler & T. Lukasiewicz (Eds.), Web Reasoning and Rule Systems (pp. 133–148). Lecture Notes/ Springer. https://doi.org/10.1007/978-3-642-15918-3_11
• Semantic search on the Web / Fazzinga, B., & Lukasiewicz, T. (2010). Semantic search on the Web. Semantic Web: Interoperability, Usability, Applicability, 1(1/2), 89–96. http://hdl.handle.net/20.500.12708/167954
• Semantic Web search based on ontological conjunctive queries / Fazzinga, B., Gianforme, G., Gottlob, G., & Lukasiewicz, T. (2010). Semantic Web search based on ontological conjunctive queries. In S. Link & H. Prade (Eds.), Foundations of Information and Knowledge Systems (pp. 153–172). Springer LNCS. https://doi.org/10.1007/978-3-642-11829-6_12
• Combining Semantic Web search with the power of inductive reasoning / d´Amato, C., Fanizzi, N., Fazzinga, B., Gottlob, G., & Lukasiewicz, T. (2010). Combining Semantic Web search with the power of inductive reasoning. In A. Deshpande & A. Hunter (Eds.), Scalable Uncertainty Management (pp. 137–150). Springer LNCS. https://doi.org/10.1007/978-3-642-15951-0_17
• Inductive reasoning and semantic web search / d’Amato, C., Esposito, F., Fanizzi, N., Fazzinga, B., Gottlob, G., & Lukasiewicz, T. (2010). Inductive reasoning and semantic web search. In S. Shin, S. Ossowski, M. Schumacher, M. J. Palakal, & C.-C. Hung (Eds.), Proceedings of the 2010 ACM Symposium on Applied Computing - SAC ’10. ACM. https://doi.org/10.1145/1774088.1774397
• Proceedings of the 6th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2010) / Proceedings of the 6th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2010). (2010). In F. Bobillo, R. Carvalho, P. C. G. da Costa, C. d´Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, T. Lukasiewicz, T. Martin, M. Nickles, & M. Pool (Eds.), CEUR Workshop Proceedings. CEUR-Proceedings. http://hdl.handle.net/20.500.12708/23242
• Proceedings of the 1st International Workshop on Uncertainty in Description Logics (UniDL 2010) / Proceedings of the 1st International Workshop on Uncertainty in Description Logics (UniDL 2010). (2010). In T. Lukasiewicz, R. Penaloza, & A.-Y. Turhan (Eds.), CEUR Workshop Proceedings. CEUR-Proceedings. http://hdl.handle.net/20.500.12708/23206

2009

• Datalog±: A Unified Approach to Ontologies and Integrity Constraints / Cali, A., Gottlob, G., & Lukasiewicz, T. (2009). Datalog±: A Unified Approach to Ontologies and Integrity Constraints. In V. De Antonellis, S. Castano, B. Catania, & G. Guerrini (Eds.), Proceedings of the 17th Italian Symposium on Advanced Database Systems (SEBD 2009) (pp. 5–6). Seneca Edizioni. http://hdl.handle.net/20.500.12708/53031
• Uncertainty in the Semantic Web / Lukasiewicz, T. (2009). Uncertainty in the Semantic Web. In L. Godo & A. Pugliese (Eds.), Scalable Uncertainty Management (pp. 2–11). Springer Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-04388-8_2
• Reasoning about Actions with Sensing under Qualitative and Probabilistic Uncertainty / Iocchi, L., Lukasiewicz, T., Nardi, D., & Rosati, R. (2009). Reasoning about Actions with Sensing under Qualitative and Probabilistic Uncertainty. ACM Transactions on Computational Logic, 10(1), 1–41. https://doi.org/10.1145/1459010.1459015
• Description Logic Programs under Probabilistic Uncertainty and Fuzzy Vagueness / Lukasiewicz, T., & Straccia, U. (2009). Description Logic Programs under Probabilistic Uncertainty and Fuzzy Vagueness. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 50(6), 837–853. https://doi.org/10.1016/j.ijar.2009.03.004
• Hybrid Reasoning with Rules and Ontologies / Drabent, W., Eiter, T., Ianni, G., Krennwallner, T., Lukasiewicz, T., & Maluszynski, J. (2009). Hybrid Reasoning with Rules and Ontologies. In F. Bry & J. Maluszynski (Eds.), Semantic Techniques for the Web (pp. 1–49). Springer. https://doi.org/10.1007/978-3-642-04581-3_1
  Projects: HEX-Programme (2008–2012) / Hybride Wissensbasen (2008–2012) / IncMan (2009–2012)
• Uncertainty Reasoning for the Semantic Web / Lukasiewicz, T. (2009). Uncertainty Reasoning for the Semantic Web. In A. F. Polleres & T. Swift (Eds.), Web Reasoning and Rule Systems (pp. 26–39). Springer Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-05082-4_3
• Tightly Coupled Probabilistic Description Logic Programs for the Semantic Web / Cali, A., Lukasiewicz, T., Predoiu, L., & Stuckenschmidt, H. (2009). Tightly Coupled Probabilistic Description Logic Programs for the Semantic Web. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-00685-2
• Combining Semantic Web Search with the Power of Inductive Reasoning / d´Amato, C., Fanizzi, N., Fazzinga, B., Gottlob, G., & Lukasiewicz, T. (2009). Combining Semantic Web Search with the Power of Inductive Reasoning. In F. Bobillo, P. C. G. da Costa, C. d´Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, T. Lukasiewicz, T. Martin, M. Nickles, M. Pool, & P. Smrz (Eds.), Proceedings of the Fifth International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2009) (pp. 15–26). CEUR-Proceedings. http://hdl.handle.net/20.500.12708/53024
• Inductive Query Answering and Concept Retrieval Exploiting Local Models / d’Amato, C., Fanizzi, N., Esposito, F., & Lukasiewicz, T. (2009). Inductive Query Answering and Concept Retrieval Exploiting Local Models. In B. Lazzerini, L. Jain, A. Abraham, F. Marcelloni, F. Herrera, & V. Loia (Eds.), 2009 Ninth International Conference on Intelligent Systems Design and Applications. IEEE Computer Society. https://doi.org/10.1109/isda.2009.34
• Approximate Classification of Semantically Annotated Web Resources Exploiting Pseudo-metrics Induced by Local Models / d´Amato, C., Fanizzi, N., Esposito, F., & Lukasiewicz, T. (2009). Approximate Classification of Semantically Annotated Web Resources Exploiting Pseudo-metrics Induced by Local Models. In R. Baeza-Yates, B. Berendt, E. Bertino, E.-P. Lim, & G. Pasi (Eds.), Proceedings of the 2009 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2009) (pp. 689–692). IEEE Computer Society. http://hdl.handle.net/20.500.12708/53042
• Combining Boolean Games with the Power of Ontologies for Automated Multi-Attribute Negotiation in the Semantic Web / Lukasiewicz, T., & Ragone, A. (2009). Combining Boolean Games with the Power of Ontologies for Automated Multi-Attribute Negotiation in the Semantic Web. In R. Baeza-Yates, J. Lang, S. Mitra, S. Parsons, & G. Pasi (Eds.), Proceedings of the 2009 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2009) (pp. 395–402). IEEE. http://hdl.handle.net/20.500.12708/53041
• A Combination of Boolean Games with Description Logics for Automated Multi-Attribute Negotiation / Lukasiewicz, T., & Ragone, A. (2009). A Combination of Boolean Games with Description Logics for Automated Multi-Attribute Negotiation. In B. Cuenca Grau, I. Horrocks, B. Motik, & U. Sattler (Eds.), Proceedings of the 22nd International Workshop on Description Logics (DL 2009) (pp. 47:1-47:12). CEUR workshop proceedings. http://hdl.handle.net/20.500.12708/53040
• Tractable Query Answering over Ontologies with Datalog+- / Cali, A., Gottlob, G., & Lukasiewicz, T. (2009). Tractable Query Answering over Ontologies with Datalog+-. In B. Cuenca Grau, I. Horrocks, B. Motik, & U. Sattler (Eds.), Proceedings of the 22nd International Workshop on Description Logics (DL 2009) (pp. 46:1-46:12). CEUR workshop proceedings. http://hdl.handle.net/20.500.12708/53039
• A general datalog-based framework for tractable query answering over ontologies / Calì, A., Gottlob, G., & Lukasiewicz, T. (2009). A general datalog-based framework for tractable query answering over ontologies. In J. Paredaens & S. Jianwen (Eds.), Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems - PODS ’09. ACM Press. https://doi.org/10.1145/1559795.1559809
• A General Datalog-Based Framework for Tractable Query Answering over Ontologies / Cali, A., Gottlob, G., & Lukasiewicz, T. (2009). A General Datalog-Based Framework for Tractable Query Answering over Ontologies. In V. De Antonellis, S. Castano, B. Catania, & G. Guerrini (Eds.), Proceedings of the 17th Italian Symposium on Advanced Database Systems (SEBD 2009) (pp. 29–36). Seneca Edizioni. http://hdl.handle.net/20.500.12708/53032
• Datalog±: A Unified Approach to Ontologies and Integrity Constraints / Cali, A., Gottlob, G., & Lukasiewicz, T. (2009). Datalog±: A Unified Approach to Ontologies and Integrity Constraints. In R. Fagin (Ed.), Proceedings of the 12th International Conference on Database Theory (ICDT 2009) (pp. 14–30). ACM International Conference Proceeding Series. http://hdl.handle.net/20.500.12708/53030
• Well-Founded Semantics for Description Logic Programs in the Semantic Web / Eiter, T., Ianni, G., Lukasiewicz, T., & Schindlauer, R. (2009). Well-Founded Semantics for Description Logic Programs in the Semantic Web (INFSYS RR 1843-09-01). http://hdl.handle.net/20.500.12708/36178
  Projects: HEX-Programme (2008–2012) / Hybride Wissensbasen (2008–2012)
• Proceedings of the Fifth International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2009) / Proceedings of the Fifth International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2009). (2009). In F. Bobillo, P. C. G. da Costa, C. d´Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, T. Lukasiewicz, T. Martin, M. Nickles, M. Pool, & P. Smrz (Eds.), CEUR Workshop Proceedings. CEUR-Proceedings. http://hdl.handle.net/20.500.12708/23033

2008

• Logical approaches to imprecise probabilities / Lukasiewicz, T. (2008). Logical approaches to imprecise probabilities. International Journal of Approximate Reasoning, 49(1), 1–2. https://doi.org/10.1016/j.ijar.2007.08.004
• An Approach to Probabilistic Data Integration for the Semantic Web / Calì, A., & Lukasiewicz, T. (2008). An Approach to Probabilistic Data Integration for the Semantic Web. In P. C. G. da Costa, C. d´Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, T. Lukasiewicz, M. Nickles, & M. Pool (Eds.), Uncertainty Reasoning for the Semantic Web I (pp. 52–65). Springer LNCS. https://doi.org/10.1007/978-3-540-89765-1_4
• Rule-Based Approaches for Representing Probabilistic Ontology Mappings / Calì, A., Lukasiewicz, T., Predoiu, L., & Stuckenschmidt, H. (2008). Rule-Based Approaches for Representing Probabilistic Ontology Mappings. In P. C. G. da Costa, C. d´Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, T. Lukasiewicz, M. Nickles, & M. Pool (Eds.), Uncertainty Reasoning for the Semantic Web I (pp. 66–87). Springer LNCS. https://doi.org/10.1007/978-3-540-89765-1_5
• Tractable Reasoning with Bayesian Description Logics / d’Amato, C., Fanizzi, N., & Lukasiewicz, T. (2008). Tractable Reasoning with Bayesian Description Logics. In S. Greco & T. Lukasiewicz (Eds.), Scalable Uncertainty Management (pp. 146–159). Lecture Notes in Computer Science, Springer. https://doi.org/10.1007/978-3-540-87993-0_13
• Tightly Coupled Fuzzy Description Logic Programs under the Answer Set Semantics for the Semantic Web / Lukasiewicz, T., & Straccia, U. (2008). Tightly Coupled Fuzzy Description Logic Programs under the Answer Set Semantics for the Semantic Web. International Journal on Semantic Web and Information Systems, 4(3), 68–89. http://hdl.handle.net/20.500.12708/170863
• Fuzzy Description Logic Programs under the Answer Set Semantics for the Semantic Web / Lukasiewicz, T. (2008). Fuzzy Description Logic Programs under the Answer Set Semantics for the Semantic Web. Fundamenta Informaticae, 82(3), 289–310. http://hdl.handle.net/20.500.12708/170864
• Managing Uncertainty and Vagueness in Description Logics for the Semantic Web / Lukasiewicz, T., & Straccia, U. (2008). Managing Uncertainty and Vagueness in Description Logics for the Semantic Web. Journal of Web Semantics, 6(4), 291–308. https://doi.org/10.1016/j.websem.2008.04.001
• Probabilistic Description Logic Programs under Inheritance with Overriding for the Semantic Web / Lukasiewicz, T. (2008). Probabilistic Description Logic Programs under Inheritance with Overriding for the Semantic Web. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 49(1), 18–34. https://doi.org/10.1016/j.ijar.2007.08.005
• Expressive Probabilistic Description Logics / Lukasiewicz, T. (2008). Expressive Probabilistic Description Logics. Artificial Intelligence, 172(6–7), 852–883. https://doi.org/10.1016/j.artint.2007.10.017
• Combining Answer Set Programming with Description Logics for the Semantic Web / Eiter, T., Ianni, G., Lukasiewicz, T., Schindlauer, R., & Tompits, H. (2008). Combining Answer Set Programming with Description Logics for the Semantic Web. Artificial Intelligence, 172(12–13), 1495–1539. https://doi.org/10.1016/j.artint.2008.04.002
• Uncertainty Representation and Reasoning in the Semantic Web / da Costa, P. C. G., Laskey, K. B., & Lukasiewicz, T. (2008). Uncertainty Representation and Reasoning in the Semantic Web. In J. Cardoso & M. Lytras (Eds.), Semantic Web Engineering in the Knowledge Society (pp. 315–340). Information Science Reference. http://hdl.handle.net/20.500.12708/26220
• Using Search Strategies and a Description Logic Paradigm with Conditional Preferences for Literature Search / Schellhase, J., & Lukasiewicz, T. (2008). Using Search Strategies and a Description Logic Paradigm with Conditional Preferences for Literature Search. International Journal of Metadata, Semantics and Ontologies, 3(1), 68. https://doi.org/10.1504/ijmso.2008.021206
• Uncertainty Reasoning for the Semantic Web I / da Costa, P. C. G., d´Amato, C., Fanizzi, N., Laskey, K. B., Laskey, K. J., Lukasiewicz, T., Nickles, M., & Pool, M. (Eds.). (2008). Uncertainty Reasoning for the Semantic Web I. Springer LNCS. https://doi.org/10.1007/978-3-540-89765-1
• Scalable Uncertainty Management / Greco, S., & Lukasiewicz, T. (Eds.). (2008). Scalable Uncertainty Management. Springer, LNCS. https://doi.org/10.1007/978-3-540-87993-0
• Tightly Integrated Probabilistic Description Logic Programs for Representing Ontology Mappings / Cali, A., Lukasiewicz, T., Predoiu, L., & Stuckenschmidt, H. (2008). Tightly Integrated Probabilistic Description Logic Programs for Representing Ontology Mappings. In S. Hartmann & G. Kern-Isberner (Eds.), Proceedings of the 5th International Symposium on Foundations of Information and Knowledge Systems (FoIKS 2008) (pp. 178–198). Springer LNCS. http://hdl.handle.net/20.500.12708/52570
• Representing Ontology Mappings with Probabilistic Description Logic Programs / Cali, A., Lukasiewicz, T., Predoiu, L., & Stuckenschmidt, H. (2008). Representing Ontology Mappings with Probabilistic Description Logic Programs. In Proceedings of the 16th Italian Symposium on Advanced Database Systems (SEBD 2008) (pp. 438–445). http://hdl.handle.net/20.500.12708/52568
• Representing Uncertain Concepts in Rough Description Logics via Contextual Indiscernibility Relations / Fanizzi, N., d´Amato, C., Esposito, F., & Lukasiewicz, T. (2008). Representing Uncertain Concepts in Rough Description Logics via Contextual Indiscernibility Relations. In F. Bobillo, P. C. G. da Costa, C. d´Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, & T. Lukasiewicz (Eds.), Proceedings of the 4th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2008) (p. 10). CEUR Workshop Proceedings. http://hdl.handle.net/20.500.12708/52565
• Combining Boolean Games with the Power of Ontologies for Automated Multi-Attribute Negotiation in the Semantic Web (SMRR) / Lukasiewicz, T., & Ragone, A. (2008). Combining Boolean Games with the Power of Ontologies for Automated Multi-Attribute Negotiation in the Semantic Web (SMRR). In R. Lara Hernandez, T. Di Noia, & I. Toma (Eds.), Proceedings of the 2nd International Workshop on Service Matchmaking and Resource Retrieval in the Semantic Web (SMRR 2008) (p. 15). CEUR Workshop Proceedings. http://hdl.handle.net/20.500.12708/52564
• From Web Search to Semantic Web Search / Fazzinga, B., Gianforme, G., Gottlob, G., & Lukasiewicz, T. (2008). From Web Search to Semantic Web Search (INFSYS RR-1843-08-11). http://hdl.handle.net/20.500.12708/35362
• "Combining Boolean Games with the Power of Ontologies for Automated Multi-Attribute Negotiation in the Semantic Web / Lukasiewicz, T., & Ragone, A. (2008). "Combining Boolean Games with the Power of Ontologies for Automated Multi-Attribute Negotiation in the Semantic Web (INFSYS RR-1843-08-08). http://hdl.handle.net/20.500.12708/35360
• Adaptive Game-Theoretic Agent Programming in Golog / Finzi, A., & Lukasiewicz, T. (2008). Adaptive Game-Theoretic Agent Programming in Golog (INFSYS RR-1843-08-07). http://hdl.handle.net/20.500.12708/35359
• Team Programming in Golog under Partial Observability / Farinelli, A., Finzi, A., & Lukasiewicz, T. (2008). Team Programming in Golog under Partial Observability (INFSYS RR-1843-08-04). http://hdl.handle.net/20.500.12708/35356
• Proceedings of the 4th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2008). Volume 423 of CEUR Workshop Proceedings / Proceedings of the 4th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2008). Volume 423 of CEUR Workshop Proceedings. (2008). In F. Bobillo, P. C. G. da Costa, C. d´Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, T. Lukasiewicz, T. Martin, M. Nickles, M. Pool, & P. Smrz (Eds.), CEUR Workshop Proceedings. CEUR-WS.org. http://hdl.handle.net/20.500.12708/22812

2007

• Reasoning with imprecise probabilities / Cano, A., Cozman, F. G., & Lukasiewicz, T. (2007). Reasoning with imprecise probabilities. International Journal of Approximate Reasoning, 44(3), 197–199. https://doi.org/10.1016/j.ijar.2006.09.001
• Description Logic Programs Under Probabilistic Uncertainty and Fuzzy Vagueness / Lukasiewicz, T., & Straccia, U. (2007). Description Logic Programs Under Probabilistic Uncertainty and Fuzzy Vagueness. In K. Mellouli (Ed.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty (pp. 187–198). Springer Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-75256-1_19
• Tractable Probabilistic Description Logic Programs / Lukasiewicz, T. (2007). Tractable Probabilistic Description Logic Programs. In H. Prade & V. S. Subrahmanian (Eds.), Scalable Uncertainty Management (pp. 143–156). Springer Lecture Notes in Artificial Intelligence. https://doi.org/10.1007/978-3-540-75410-7_11
• Top-k Retrieval in Description Logic Programs Under Vagueness for the Semantic Web / Lukasiewicz, T., & Straccia, U. (2007). Top-k Retrieval in Description Logic Programs Under Vagueness for the Semantic Web. In H. Prade & V. S. Subrahmanian (Eds.), Scalable Uncertainty Management (pp. 16–30). Springer Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-75410-7_2
• A Novel Combination of Answer Set Programming with Description Logics for the Semantic Web / Lukasiewicz, T. (2007). A Novel Combination of Answer Set Programming with Description Logics for the Semantic Web. In E. Franconi, M. Kifer, & W. May (Eds.), The Semantic Web: Research and Applications (pp. 384–398). Springer Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-72667-8_28
• Variable-Strength Conditional Preferences for Ranking Objects in Ontologies / Lukasiewicz, T., & Schellhase, J. (2007). Variable-Strength Conditional Preferences for Ranking Objects in Ontologies. Journal of Web Semantics, 5(3), 180–194. https://doi.org/10.1016/j.websem.2007.06.001
• Probabilistic Description Logic Programs / Lukasiewicz, T. (2007). Probabilistic Description Logic Programs. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 45(2), 288–307. https://doi.org/10.1016/j.ijar.2006.06.012
• Nonmonotonic Probabilistic Logics under Variable-Strength Inheritance with Overriding: Complexity, Algorithms, and Implementation. / Lukasiewicz, T. (2007). Nonmonotonic Probabilistic Logics under Variable-Strength Inheritance with Overriding: Complexity, Algorithms, and Implementation. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 44(3), 301–321. https://doi.org/10.1016/j.ijar.2006.07.015
• Tutorial on Managing Uncertainty and Vagueness in Semantic Web Languages / Lukasiewicz, T., & Straccia, U. (2007). Tutorial on Managing Uncertainty and Vagueness in Semantic Web Languages. 4th European Semantic Web Conference (ESWC 2007), Innsbruck, Österreich, Austria. http://hdl.handle.net/20.500.12708/84626
• Tutorial on Managing Uncertainty and Vagueness in Semantic Web Languages / Lukasiewicz, T., & Straccia, U. (2007). Tutorial on Managing Uncertainty and Vagueness in Semantic Web Languages. National Conference on Artificial Intelligence (AAAI), Pittsburgh, Pennsylvania, USA, Austria. http://hdl.handle.net/20.500.12708/84627
• Team Programming in Golog under Partial Observability / Farinelli, A., Finzi, A., & Lukasiewicz, T. (2007). Team Programming in Golog under Partial Observability. In M. Veloso (Ed.), Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007) (pp. 2097–2102). AAAI Press/IJCAI. http://hdl.handle.net/20.500.12708/51959
• Tightly Integrated Fuzzy Description Logic Programs Under the Answer Set Semantics for the Semantic Web / Lukasiewicz, T., & Straccia, U. (2007). Tightly Integrated Fuzzy Description Logic Programs Under the Answer Set Semantics for the Semantic Web. In M. Marchiori, J. Z. Pan, & C. de Sainte Marie (Eds.), Web Reasoning and Rule Systems (pp. 289–298). Springer Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-72982-2_23
• Tightly Integrated Probabilistic Description Logic Programs for the Semantic Web / Calì, A., & Lukasiewicz, T. (2007). Tightly Integrated Probabilistic Description Logic Programs for the Semantic Web. In V. Dahl & I. Niemelä (Eds.), Logic Programming (pp. 428–429). Springer Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-74610-2_30
• A Framework for Representing Ontology Mappings under Probabilities and Inconsistency / Cali, A., Lukasiewicz, T., Prodoiu, L., & Stuckenschmidt, H. (2007). A Framework for Representing Ontology Mappings under Probabilities and Inconsistency. In F. Bobillo, P. Costa, C. d´Amato, N. Fanizzi, F. Fung, T. Lukasiewicz, & T. Martin (Eds.), Proceedings of the ISWC-2007 Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2007). CEUR Workshop Proceedings. http://hdl.handle.net/20.500.12708/51952
• Tightly Integrated Probabilistic Description Logic Programs. / Cali, A., & Lukasiewicz, T. (2007). Tightly Integrated Probabilistic Description Logic Programs. (INFSYS RR-1843-07-05). http://hdl.handle.net/20.500.12708/33095
• Uncertainty and Vagueness in Description Logic Programs for the Semantic Web / Lukasiewicz, T., & Straccia, U. (2007). Uncertainty and Vagueness in Description Logic Programs for the Semantic Web (INFSYS RR 1843-07-02). http://hdl.handle.net/20.500.12708/33096
• Tightly Integrated Fuzzy Description Logic Programs under the Answer Set Semantics for the Semantic Web / Lukasiewicz, T., & Straccia, U. (2007). Tightly Integrated Fuzzy Description Logic Programs under the Answer Set Semantics for the Semantic Web (INFSYS RR 1843-07-03). http://hdl.handle.net/20.500.12708/33094
• Variable-Strength Conditional Preferences for Ranking Objects in Ontologies / Lukasiewicz, T., & Schellhase, J. (2007). Variable-Strength Conditional Preferences for Ranking Objects in Ontologies (INFSYS RR-1843-07-06). http://hdl.handle.net/20.500.12708/33093
• Combining Answer Set Programming with Description Logics for the Semantic Web / Eiter, T., Ianni, G., Lukasiewicz, T., Schindlauer, R., & Tompits, H. (2007). Combining Answer Set Programming with Description Logics for the Semantic Web (INFSYS RR-1843-07-04). http://hdl.handle.net/20.500.12708/33092
• Proceedings of the ISWC-2007 Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2007). / Proceedings of the ISWC-2007 Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2007). (2007). In F. Bobillo, P. Costa, N. Fanizzi, F. Fung, T. Lukasiewicz, T. Martin, M. Nickles, Y. Peng, M. Pool, P. Smrz, & P. Vojt (Eds.), CEUR Workshop Proceedings. CEUR-Proceedings. http://hdl.handle.net/20.500.12708/22349

2006

• Causes and Explanations in the Structural-Model Approach: Tractable Cases / Eiter, T., & Lukasiewicz, T. (2006). Causes and Explanations in the Structural-Model Approach: Tractable Cases. Artificial Intelligence, 170(6–7), 542–580. http://hdl.handle.net/20.500.12708/173415
• An Approach to Probabilistic Data Integration for the Semantic Web / Cali, A., & Lukasiewicz, T. (2006). An Approach to Probabilistic Data Integration for the Semantic Web. In P. C. G. da Costa, K. B. Laskey, K. J. Laskey, F. Fung, & M. Pool (Eds.), Proceedings of the ISWC-2006 Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2006) (pp. 67–68). CEUR Workshop Proceedings. http://hdl.handle.net/20.500.12708/51718
• Variable-Strength Conditional Preferences for Ranking Objects in Ontologies / Lukasiewicz, T., & Schellhase, J. (2006). Variable-Strength Conditional Preferences for Ranking Objects in Ontologies. In Y. Sure & J. Domingue (Eds.), Proceedings of the 3rd European Semantic Web Conference (ESWC 2006), Budva, Montenegro, June 2006 (pp. 288–302). Lecture Notes in Computer Science. Springer. http://hdl.handle.net/20.500.12708/51712
• Preferences, Links, and Probabilities for Ranking Objects in Ontologies / Lukasiewicz, T., & Schellhase, J. (2006). Preferences, Links, and Probabilities for Ranking Objects in Ontologies. In P. C. G. da Costa, K. B. Laskey, K. J. Laskey, F. Fung, & M. Pool (Eds.), Proceedings of the ISWC-2006 Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2006) (pp. 65–66). CEUR Workshop Proceedings. http://hdl.handle.net/20.500.12708/51719
• Fuzzy Description Logic Programs under the Answer Set Semantics for the Semantic Web / Lukasiewicz, T. (2006). Fuzzy Description Logic Programs under the Answer Set Semantics for the Semantic Web. In T. Eiter, E. Franconi, R. Hodgson, & S. Stephens (Eds.), Proceedings of the 2nd International Conference on Rules and Rule Markup Languages for the Semantic Web (RuleML 2006) (pp. 89–96). IEEE Computer Society. http://hdl.handle.net/20.500.12708/51717
• Adaptive Multi-Agent Programming in GTGolog / Finzi, A., & Lukasiewicz, T. (2006). Adaptive Multi-Agent Programming in GTGolog. In G. Brewka, S. Coradeschi, A. Perini, & P. Traverso (Eds.), Proceedings of the 17th biennial European Conference on Artificial Intelligence (ECAI 2006) (pp. 753–754). IOS Press. http://hdl.handle.net/20.500.12708/51716
• Game-Theoretic Agent Programming in Golog under Partial Observability / Finzi, A., & Lukasiewicz, T. (2006). Game-Theoretic Agent Programming in Golog under Partial Observability. In C. Freksa, M. Kohlhase, & K. Schill (Eds.), Proceedings of the 29th Annual German Conference on Artificial Intelligence (KI 2006), Bremen, Germany, June 2006. (pp. 113–127). Lecture Notes in Computer Science, Springer. http://hdl.handle.net/20.500.12708/51715
• Adaptive Multi-Agent Programming in GTGolog / Finzi, A., & Lukasiewicz, T. (2006). Adaptive Multi-Agent Programming in GTGolog. In C. Freksa, M. Kohlhase, & K. Schill (Eds.), Proceedings of the 29th Annual German Conference on Artificial Intelligence (KI 2006), Bremen, Germany, June 2006. (pp. 389–403). Lecture Notes in Computer Science. Springer. http://hdl.handle.net/20.500.12708/51714
• Variable-Strength Conditional Preferences for Matchmaking in Description Logics / Lukasiewicz, T., & Schellhase, J. (2006). Variable-Strength Conditional Preferences for Matchmaking in Description Logics. In P. Doherty, J. Mylopoulos, & C. Welty (Eds.), Proceedings of the 10th International Conference on Principles of Knowledge Representation and Reasoning (KR 2006), Lake District, UK, June 2006. (pp. 164–174). AAAI Press. http://hdl.handle.net/20.500.12708/51713
• A novel combination of answer set programming with description logics for the Semantic Web / Lukasiewicz, T. (2006). A novel combination of answer set programming with description logics for the Semantic Web (INFSYS RR-1843-06-08). http://hdl.handle.net/20.500.12708/33073
• An Overview of Uncertainty and Vagueness in Description Logics for the Semantic Web / Lukasiewicz, T., & Straccia, U. (2006). An Overview of Uncertainty and Vagueness in Description Logics for the Semantic Web (INFSYS RR-1843-06-07). http://hdl.handle.net/20.500.12708/33072
• Probabilistic Description Logics for the Semantic Web / Lukasiewicz, T. (2006). Probabilistic Description Logics for the Semantic Web (INFSYS RR-1843-06-05). http://hdl.handle.net/20.500.12708/33071
• Probabilistic Description Logic Programs / Lukasiewicz, T. (2006). Probabilistic Description Logic Programs (INFSYS RR-1843-06-04). http://hdl.handle.net/20.500.12708/33070

2005

• Probabilistic Logic under Coherence: Complexity and Algorithms / Biazzo, V., Gilio, A., Lukasiewicz, T., & Sanfilippo, G. (2005). Probabilistic Logic under Coherence: Complexity and Algorithms. Annals of Mathematics and Artificial Intelligence, ONLINE FIRST(Online First). http://hdl.handle.net/20.500.12708/173380
• Weak Nonmonotonic Probabilistic Logics / Lukasiewicz, T. (2005). Weak Nonmonotonic Probabilistic Logics. Artificial Intelligence, 168(1–2), 119–161. http://hdl.handle.net/20.500.12708/173381
• Nonmonotonic Probabilistic Reasoning under Variable-Strength Inheritance with Overriding / Lukasiewicz, T. (2005). Nonmonotonic Probabilistic Reasoning under Variable-Strength Inheritance with Overriding. Synthese, 146(1–2), 153–169. http://hdl.handle.net/20.500.12708/173382
• Nonmonotonic Probabilistic Logics under Variable-Strength Inheritance with Overriding: Algorithms and Implementation in NMPROBLOG / Lukasiewicz, T. (2005). Nonmonotonic Probabilistic Logics under Variable-Strength Inheritance with Overriding: Algorithms and Implementation in NMPROBLOG. In Proceedings of the 4th International Symposium on Imprecise Probabilities and Their Applications (ISIPTA 2005) (pp. 230–239). CMU. http://hdl.handle.net/20.500.12708/51341
• Game-Theoretic Golog under Partial Observability / Finzi, A., & Lukasiewicz, T. (2005). Game-Theoretic Golog under Partial Observability. In F. Dignum, V. Dignum, S. Koenig, & S. Kraus (Eds.), Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2005) (pp. 1301–1302). ACM Press. http://hdl.handle.net/20.500.12708/51340
• Probabilistic Description Logic Programs / Lukasiewicz, T. (2005). Probabilistic Description Logic Programs. In L. Godo (Ed.), Proceedings of the 8th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2005) (pp. 737–749). Springer. http://hdl.handle.net/20.500.12708/51339
• Game-Theoretic Reasoning About Actions in Nonmonotonic Causal Theories / Finzi, A., & Lukasiewicz, T. (2005). Game-Theoretic Reasoning About Actions in Nonmonotonic Causal Theories. In C. Baral, G. Greco, & N. Leone (Eds.), Proceedings of the 8th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 2005) (pp. 185–197). Springer. http://hdl.handle.net/20.500.12708/51308
• Game-Theoretic Agent Programming in Golog under Partial Observability / Finzi, A., & Lukasiewicz, T. (2005). Game-Theoretic Agent Programming in Golog under Partial Observability. In P. Gmytrasiewicz & S. Parsons (Eds.), Working Notes of IJCAI-05 Workshop on Game Theoretic and Decision Theoretic Agents (GTDT 2005). http://hdl.handle.net/20.500.12708/51343
• Stratified Probabilistic Description Logic Programs / Lukasiewicz, T. (2005). Stratified Probabilistic Description Logic Programs. In P. C. G. da Costa, K. B. Laskey, & K. J. Laskey (Eds.), Proceedings of the ISWC-2005 Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2005) (pp. 87–97). http://hdl.handle.net/20.500.12708/51342
• Nonmonotonic Probabilistic Logics under Variable-Strength Inheritance with Overriding: Algorithms and Implementation in NMPROBLOG / Lukasiewicz, T. (2005). Nonmonotonic Probabilistic Logics under Variable-Strength Inheritance with Overriding: Algorithms and Implementation in NMPROBLOG. http://hdl.handle.net/20.500.12708/33047
• Game-Theoretic Reasoning about Actions in Nonmonotonic Causal Theories / Finzi, A., & Lukasiewicz, T. (2005). Game-Theoretic Reasoning about Actions in Nonmonotonic Causal Theories. http://hdl.handle.net/20.500.12708/33048
• Game-Theoretic Golog under Partial Observability / Finzi, A., & Lukasiewicz, T. (2005). Game-Theoretic Golog under Partial Observability. http://hdl.handle.net/20.500.12708/33046
• Variable-Strength Conditional Preferences for Matchmaking in Description Logics / Lukasiewicz, T., & Schellhase, J. (2005). Variable-Strength Conditional Preferences for Matchmaking in Description Logics. http://hdl.handle.net/20.500.12708/33053

2004

• Game-Theoretic Agent Programming in Golog / Finzi, A., & Lukasiewicz, T. (2004). Game-Theoretic Agent Programming in Golog. http://hdl.handle.net/20.500.12708/32946

2003

• Combining Answer Set Programming with Description Logics for the Semantic Web / Eiter, T., Lukasiewicz, T., Schindlauer, R., & Tompits, H. (2003). Combining Answer Set Programming with Description Logics for the Semantic Web. http://hdl.handle.net/20.500.12708/32933

2002

• Causes and Explanations in the Structural-Model Approach: Tractable Cases (INFSYS RR-1843-02-03) / Eiter, T., & Lukasiewicz, T. (2002). Causes and Explanations in the Structural-Model Approach: Tractable Cases (INFSYS RR-1843-02-03). http://hdl.handle.net/20.500.12708/32793

• Multimodal modeling of chest X-rays / Blasko, D. (2024). Multimodal modeling of chest X-rays [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2025.118782
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• Automatically testing the out-of-distribution reasoning capabilities of LLMs/AGI systems with generative formal games / Vonderlind, P. (2024). Automatically testing the out-of-distribution reasoning capabilities of LLMs/AGI systems with generative formal games [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2024.118784
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• Deep learning für das Semantic Web / Hohenecker, P. (2016). Deep learning für das Semantic Web [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2016.37489
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