Thomas Lukasiewicz

Univ.Prof. Dipl.-Inf. Dr.rer.nat.

Research Focus

Information Systems Engineering: 100%

Research Areas

Artificial Intelligence, Machine Learning, Computer Vision, Natural Language Processing

About

Our research aims at enabling machines to mimic human-like intelligence, via models and algorithms to process vast amounts of data, recognize patterns, and make decisions, using techniques from machine and deep learning as well as symbolic reasoning.

In our research activities, we cover all modalities, including natural language processing and computer vision. We focus especially on (i) explainable AI, (ii) deep learning with logical constraints for safe AI , (iii) hybrid model-based approaches to explainable, fair, and robust AI, (iv) general AI via predictive coding and active inference, and (v) intelligent applications, such as in healthcare and law.

Roles

Head of Research Unit
Artificial Intelligence Techniques, E192-07
Full Professor
Artificial Intelligence Techniques, E192-07
Affiliated
Knowledge-Based Systems, E192-03

Contact

thomas.lukasiewicz@tuwien.ac.at
vCard from TISS

Courses

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

Projects

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

Publications

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
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
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
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
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
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
Download: Article (6.58 MB)
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
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
Download: Article (3.29 MB)
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. http://hdl.handle.net/20.500.12708/210293
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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

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
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
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
(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
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
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
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
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
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
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
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 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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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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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
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

Supervisions

Identifying Data Contamination in LLMs for Mathematical Benchmarks / Mathis, T. G. (2025). Identifying Data Contamination in LLMs for Mathematical Benchmarks [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2025.129501
Download: PDF (1.42 MB)
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
Download: PDF (1.76 MB)
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
Download: PDF (1.32 MB)
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
Download: PDF (1.11 MB)

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