Search

Professor Artur d'Avila Garcez

Professor of Computer Science

School of Science & Technology Department of Computer Science

Contact details

Personal links

About

Overview

Professor Artur Garcez is Director of the Research Centre for Machine Learning at City, University of London. He holds a PhD in Computing (2000) from Imperial College London. He is a Fellow of the British Computer Society (FBCS).

Professor Garcez has an established track record of research in Machine Learning, Neural Computation and Artificial Intelligence. He is president of the steering committee of the Neural-Symbolic Learning and Reasoning Association, and founding course director of City's MSc in Data Science. He has co-authored two books: Neural-Symbolic Cognitive Reasoning, 2009, and Neural-Symbolic Learning Systems, 2002. His research has led to publications in the journals Behavioral & Brain Sciences, Theoretical Computer Science, Neural Computation, Machine Learning, Journal of Logic and Computation, IEEE Transactions on Neural Networks, Journal of Applied Logic, Artificial Intelligence, and Studia Logica, and the flagship AI and Neural Computation conferences AAAI, NeurIPS, IJCAI, IJCNN, AAMAS and ECAI.

Professor Garcez holds editorial positions with several scientific journals in the fields of Computational Logic and Artificial Intelligence, and has been Programme Committee member for several conferences, including IJCAI, IJCNN, NeurIPS and AAAI.

Personal Webpage

Qualifications

  • PhD Computing, Imperial College London, United Kingdom, 2000
  • MSc Systems Engineering and Computing, Federal University of Rio de Janeiro, Brazil, 1996
  • MEng Computing Engineering, Pontifical Catholic University of Rio de Janeiro, Brazil, 1993

Employment

  • Professor of Computer Science, City, University of London, United Kingdom, 2015 - present

Memberships of professional organisations

  • Fellow, British Computer Society (BCS)
  • Member, IEEE Computer Society
  • Member, AAAI (Association for the Advancement of Artificial Intelligence)
  • Member, City & Guilds College Association, London, UK
  • Member, Computability in Europe (CiE) Association

Research

Research Interests

- Neural-Symbolic Computation, Neural Networks and Applied Logic, Complex Networks
- Machine Learning, Integrating Robust Learning and Reasoning under Uncertainty
- Cognitive Agents and Intelligent Systems, Knowledge Extraction, Visual Information Processing
- Business Process Evolution, Requirements Engineering, Automated Software Engineering

Publications

Publications by category

Books (4)

  • Garcez, A.S.D., Lamb, L.C. and Gabbay, D.M. (2009). Neural-Symbolic Cognitive Reasoning. Cognitive Technologies. Springer-Verlag New York Inc. ISBN 9783540732457.
  • Artemov, S., Barringer, H., Garcez, A., Lamb, L.C. and Woods, J. (2005). We Will Show Them! Essays in Honour of Dov Gabbay. College Pubns. ISBN 9781904987116.
  • Artemov, S., Barringer, H., Garcez, A., Lamb, L.C. and Woods, J. (2005). We Will Show Them! Essays in Honour of Dov Gabbay. College Pubns. ISBN 9781904987260.
  • Garcez, A.S.D., Broda, K. and Gabbay, D.M. (2002). Neural-symbolic learning systems: Foundations and Applications. Perspectives in Neural Computing. Springer Verlag. ISBN 9781852335120.

Chapters (15)

  • Tran, S., Mota, E. and Garcez, A.D. (2026). Reasoning in Neurosymbolic AI. (pp. 15-68). Wiley. ISBN 9781394302376.
  • Townsend, J., Mansouri-Benssassi, E., Ngan, K.H. and d'Avila Garcez, A. (2023). Discovering visual concepts and rules in convolutional neural networks. Compendium of Neurosymbolic Artificial Intelligence (pp. 337-372).
  • Townsend, J., Mansouri-Benssassi, E., Ngan, K.H. and Garcez, A.D. (2023). Chapter 16. Discovering Visual Concepts and Rules in Convolutional Neural Networks. Frontiers in Artificial Intelligence and Applications IOS Press. ISBN 9781643684062.
  • Besold, T.R., d’Avila Garcez, A., Bader, S., Bowman, H., Domingos, P., Hitzler, P.... Zaverucha, G. (2021). Chapter 1. Neural-Symbolic Learning and Reasoning: A Survey and Interpretation1. Frontiers in Artificial Intelligence and Applications IOS Press. ISBN 9781643682440.
  • Serafini, L., d’Avila Garcez, A., Badreddine, S., Donadello, I., Spranger, M. and Bianchi, F. (2021). Chapter 17. Logic Tensor Networks: Theory and Applications. Frontiers in Artificial Intelligence and Applications IOS Press. ISBN 9781643682440.
  • Perotti, A., Garcez, A.D. and Boella, G. (2015). Neural-symbolic monitoring and adaptation. (pp. 1-8). IEEE.
  • Cherla, S., Weyde, T., Garcez, A. and Pearce, M. (2013). A Distributed Model For Multiple-Viewpoint Melodic Prediction. In de Souza Britto Jr, A., Gouyon, F. and Dixon, S. (Eds.), Proceedings of the 14th International Society for Music Information Retrieval Conference (pp. 15-20). International Society for Music Information Retrieval. ISBN 9780615900650.
  • Garcez, A.D. and Lamb, L.C. (2012). Learning and Argumentation in Neural-Symbolic Computation. In Seel, N.M. (Ed.), Encyclopedia of the Sciences of Learning Not Avail. ISBN 9781441914279.
  • Garcez, A.D. and Lamb, L.C. (2011). Cognitive Reasoning and Knowledge Representation. In Cutsuridis, V., Hussain, A. and Taylor, J.G. (Eds.), Perception-Action Cycle: Models, Algorithms and Hardware (pp. 573). Springer Verlag. ISBN 9781441914514.
  • de Penning, L., Garcez, A.D., Lamb, L.C. and Meyer, J.J. (2011). A Neural-Symbolic Cognitive Agent for Online Learning and Reasoning. Proceedings of the Twenty-Second international joint conference on Artificial Intelligence (pp. 1653-1658). ISBN 9781577355144.
  • Garcez, A.D. (2007). Advances in Neural-Symbolic Learning Systems: Modal and Temporal Reasoning. In Hammer, B. and Hitzler, P. (Eds.), Perspectives of neural-symbolic integration Springer Verlag. ISBN 9783540739531.
  • Garcez, A.D. and Lamb, L.C. (2005). Neural-Symbolic Systems and The Case for Non-Classical Reasoning. In Artemov, S., Barringer, H., Garcez, A.D., Lamb, L.C. and Woods, J. (Eds.), We Will Show Them! Essays in Honour of Dov Gabbay College Pubns. ISBN 9781904987116.
  • Garcez, A.D. (2004). On Gabbay's Fibring Methodology for Bayesian and Neural Networks. In Gillies, D. (Ed.), Laws and models in science College Publications. ISBN 9780954300661.
  • Garcez, A.D., Lamb, L.C. and Gabbay, D.M. (2003). Neural-Symbolic Intuitionistic Reasoning. In Abraham, A., Köppen, M. and Franke, K. (Eds.), Frontiers in Artificial Intelligence and Applications IOS Press. ISBN 9781586033941.
  • Garcez, A.D., Zaverucha, G. and de Carvalho, L.A.V. (1997). Logic Programming and Inductive Learning in Artificial Neural Networks. In H, C., Reine, F. and Strohmaier, A. (Eds.), Knowledge Representation in Neural Networks (pp. 33-46). ISBN 9783931216771.

Conference papers and proceedings (114)

  • Lushnei, S., Shumskyi, D., Shykula, S., Jiménez-Ruiz, E. and Garcez, A.D. (2026). Large Language Models as Oracles for Ontology Alignment. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) March.doi:10.18653/v1/2026.eacl-long.110
  • Sikar, D., Garcez, A.D. and Weyde, T. Explorations of the Softmax Space: Knowing When the Neural Network Doesn’t Know. .doi:10.1007/978-3-032-21477-5_20
  • Carvalho, B.W., d'Avila Garcez, A., Lamb, L.C. and Brazil, E.V. (2025). Inducing Grokking with Distribution Shifts. 2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI) 3-5 November, Athens, Greece.doi:10.1109/ictai66417.2025.00036
  • Jiménez-Ruiz, E., Lushnei, S., Shumskyi, D., Shykula, S. and Garcez, A.D.A. (2025). LogMap Family welcomes LogMapLLM in the OAEI 2025. Proceedings of the 20th International Workshop on Ontology Matching 2 November-, Nara, Japan.
  • Luca, T., Paes, A., Zaverucha, G. and Garcez, A.D. (2025). Towards Robust Neurosymbolic Relational Learning. 2025 International Joint Conference on Neural Networks (IJCNN) 30 June-5 July.doi:10.1109/ijcnn64981.2025.11229419
  • Silkar, D., d'Avila Garcez, A., Bloomfield, R., Weyde, T., Peeroo, K., Singh, N.... Reljan-Delaney, M. (2024). The Misclassification Likelihood Matrix: Some Classes Are More Likely To Be Misclassified Than Others. CGVC 12-13 September, London, United Kingdom.doi:10.2312/cgvc.20241239
  • Su, Q., Kloukinas, C. and d’Avila Garcez, A. (2024). FocusLearn: Fully-Interpretable, High-Performance Modular Neural Networks for Time Series. 2024 International Joint Conference on Neural Networks (IJCNN) 30 June-5 July.doi:10.1109/ijcnn60899.2024.10651481
  • Gandarela de Souza, J.P., Zaverucha, G. and d’Avila Garcez, A.S. (2024). Hypergraph Neural Networks with Logic Clauses. 2024 International Joint Conference on Neural Networks (IJCNN) 30 June-5 July.doi:10.1109/ijcnn60899.2024.10650412
  • Ngan, K.H., Phelan, J., Townsend, J. and Garcez, A.D. (2024). Symbolic Knowledge Extraction and Distillation into Convolutional Neural Networks to Improve Medical Image Classification. 2024 International Joint Conference on Neural Networks (IJCNN) 30 June-5 July.doi:10.1109/ijcnn60899.2024.10650683
  • Ngan, K.H., Phelan, J., Mansouri-Benssassi, E., Townsend, J. and d'Avila Garcez, A.S. (2023). Closing the Neural-Symbolic Cycle: Knowledge Extraction, User Intervention and Distillation from Convolutional Neural Networks. 17th International Workshop on Neural-Symbolic Learning and Reasoning 3-5 July, Siena, Italy.
  • Stromfelt, H., Dickens, L., d'Avila Garcez, A., Russo, A., Koyejo, S., Mohamed, S.... Oh, A. (2022). Formalizing Consistency and Coherence of Representation Learning. Neural Information Processing Systems 28 November-9 December, New Orleans, USA.
  • Carvalho, B.W., d’Avila Garcez, A.S. and Lamb, L.C. (2022). Graph-based Neural Modules to Inspect Attention-based Architectures: A Position Paper. Thinking Fast and Slow and Other Cognitive Theories in AI a AAAI 2022 Fall Symposium 17-19 November, Arlington, Virginia, US.
  • Apperly, I., Bundy, A., Cohn, A., Colton, S., Cussens, J., d'Avila Garcez, A.S.... Tamaddoni-Nezhad, A. (2022). Preface. 3rd Human-Like Computing Workshop (HLC 2022) 28-30 September, Windsor, United Kingdom.
  • d’Avila Garcez, A. and Jimenez-Ruiz, E. (2022). Preface. 16th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy) 28-30 September, Windsor, UK.
  • Ngan, K.H., Garcez, A.D. and Townsend, J. (2022). Extracting Meaningful High-Fidelity Knowledge from Convolutional Neural Networks. 2022 International Joint Conference on Neural Networks (IJCNN) 18-23 July, Padua, Italy.doi:10.1109/ijcnn55064.2022.9892194
  • Wagner, B. and d'Avila Garcez, A. (2022). Neural-Symbolic Reasoning Under Open-World and Closed-World Assumptions. AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022) 21-23 March, California, USA.
  • Stromfelt, H., Dickens, L., d'Avila Garcez, A.S. and Russo, A. (2021). Coherent and Consistent Relational Transfer Learning with Auto-encoders. 15th International Workshop on Neural-Symbolic Learning and Reasoning 25-27 October, Virtual event.
  • d’Avila Garcez, A. and Jimenez-Ruiz, E. (2021). Preface. 15th International Workshop on Neural-Symbolic Learning and Reasoning 25-27 October, Online.
  • Wagner, B. and d'Avila Garcez, A.S. (2021). Neural-symbolic integration for fairness in AI. AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021) 22-24 March, California, USA.
  • Charitou, C., Garcez, A.D. and Dragicevic, S. (2020). Semi-supervised GANs for Fraud Detection. 2020 International Joint Conference on Neural Networks (IJCNN) 19-24 July.doi:10.1109/ijcnn48605.2020.9206844
  • Riveret, R., Tran, S. and Garcez, A.D.A. Neural-symbolic probabilistic argumentation machines. .
  • Percy, C., d’Avila Garcez, A., Dragicevic, S. and Sarkar, S. Lessons learned from problem gambling classification: indirect discrimination and algorithmic fairness. .
  • White Adam, and d’Avila Garcez Artur, Measurable Counterfactual Local Explanations for Any Classifier. .doi:10.3233/faia200387
  • Ngan, K.H., Garcez, A.D., Knapp, K.M., Appelboam, A. and Reyes-Aldasoro, C.C. A Machine Learning Approach for Colles’ Fracture Treatment Diagnosis. .doi:10.1007/978-3-030-52791-4_25
  • Lamb, L.C., Garcez, A.D., Gori, M., Prates, M.O.R., Avelar, P.H.C. and Vardi, M.Y. Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective. .
  • Riveret, R., Tran, S. and Garcez, A.D. Neural-Symbolic Probabilistic Argumentation Machines. .
  • Philps, D., Garcez, A. and Weyde, T. (2019). Making Good on LSTMs' Unfulfilled Promise. NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy 13 December-, Vancouver, Canada.
  • Mota, E., Howe, J.M., Schramm, A. and d'Avila Garcez, A.S. (2019). Efficient Predicate Invention using Shared NeMuS. 14th International Workshop on Neural-Symbolic Learning and Reasoning 10-16 August, Macau, China.
  • Weyde, T., Philps, D. and d'Avila Garcez, A. (2018). Continual Learning Augmented Investment Decisions. 2018 NeurIPS Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy (FEAP-AI4Fin) 2-8 December, Montreal.
  • Donadello, I., Serafini, L. and d'Avila Garcez, A. (2017). Logic Tensor Networks for Semantic Image Interpretation. Twenty-Sixth International Joint Conference on Artificial Intelligence 19-26 August.doi:10.24963/ijcai.2017/221
  • Schramm, A.C.M., Mota, E.D.S., Howe, J.M. and Garcez, A.S.D. (2017). Category-based Inductive Learning in Shared NeMuS. 17-18 July.
  • Tenório, M.R., Mota, E.D.S., Howe, J.M. and Garcez, A.S.D.A. (2017). Learning about Actions and Events in Shared NeMuS. Twelfth International Workshop on Neural-Symbolic Learning and Reasoning (NeSy 2017) 17-18 July, London, UK.
  • Howe, J.M., Mota, E.D. and Garcez, A. (2017). Inductive learning in Shared Neural Multi-Spaces. 17-18 July.
  • Russell, A.J., Benetos, E. and D'Avila Garcez, A. On the memory properties of recurrent neural models. .doi:10.1109/IJCNN.2017.7966173
  • Serafini, L., Donadello, I. and Garcez, A.D. Learning and reasoning in logic tensor networks. SAC 2017: Symposium on Applied Computing.doi:10.1145/3019612.3019642
  • Dutra, A.R.R. and D’Avila Garcez, A.S. A Comparison between deep Q-networks and deep symbolic reinforcement learning. .
  • Odense, S. and D’Avila Garcez, A. Confidence values and compact rule extraction from probabilistic neural networks. .
  • Odense, S. and d’Avila Garcez, A. Extracting M of N Rules from Restricted Boltzmann Machines. .doi:10.1007/978-3-319-68612-7_14
  • Sarkar, S., Weyde, T., Garcez, A., Slabaugh, G.G., Dragicevic, S. and Percy, C. (2016). Accuracy and interpretability trade-offs in machine learning applied to safer gambling. 9 December-.
  • Tran, S.N. and Garcez, A.D. (2016). Adaptive Transferred-profile Likelihood Learning. 2016 International Joint Conference on Neural Networks (IJCNN) 24-29 July.doi:10.1109/ijcnn.2016.7727536
  • Yilmaz, O., D'Avila Garcez, A. and Silver, D. A proposal for common dataset in neural-symbolic reasoning studies. .
  • Serafini, L. and Garcez, A.D.A. Logic tensor networks: Deep learning and logical reasoning from data and knowledge. .
  • Percy Chris, , d'Avila Garcez Artur S., , Dragičević Simo, , França Manoel V.M., , Slabaugh Greg, and Weyde Tillman, The Need for Knowledge Extraction: Understanding Harmful Gambling Behavior with Neural Networks. .doi:10.3233/978-1-61499-672-9-974
  • Cherla, S., Tran, S.N., Garcez, A.D. and Weyde, T. (2015). Discriminative learning and inference in the Recurrent Temporal RBM for melody modelling. 2015 International Joint Conference on Neural Networks (IJCNN) 12-17 July.doi:10.1109/ijcnn.2015.7280691
  • Tran, S.N. and Garcez, A.D. (2015). Efficient representation ranking for transfer learning. 2015 International Joint Conference on Neural Networks (IJCNN) 12-17 July.doi:10.1109/ijcnn.2015.7280454
  • Sigtia, S., Benetos, E., Boulanger-Lewandowski, N., Weyde, T., d'Avila Garcez, A.S. and Dixon, S. (2015). A hybrid recurrent neural network for music transcription. ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 19-24 April.doi:10.1109/icassp.2015.7178333
  • Garcez, A., Besold, T.R., Raedt, L., Foldiak, P., Hitzler, P., Icard, T.... Silver, D.L. (2015). Neural-Symbolic Learning and Reasoning: Contributions and Challenges. 2015 AAAI Spring Symposium Series 23-3 January, Stanford University, USA.
  • França, M.V.M., Zaverucha, G. and Garcez, A. (2015). Neural Relational Learning Through Semi-Propositionalization of Bottom Clauses. 2015 AAAI Spring Symposium Series 23-3 January, Stanford University, USA.
  • França, M.V.M., D'Avila Garcez, A.S. and Zaverucha, G. Relational knowledge extraction from neural networks. .
  • Cherla, S., Tran, S.N., Weyde, T. and d’Avila Garcez, A. Hybrid long- and short-term models of folk melodies. .
  • Perotti, A., Boella, G. and Garcez, A.D. Runtime Verification Through Forward Chaining. .doi:10.1007/978-3-319-23820-3_12
  • Tran, S.N., Benetos, E. and d'Avila Garcez, A. (2014). Learning motion-difference features using Gaussian restricted Boltzmann machines for efficient human action recognition. 2014 International Joint Conference on Neural Networks (IJCNN) 6-11 July.doi:10.1109/ijcnn.2014.6889945
  • de Penning, L., d'Avila Garcez, A.S., Lamb, L.C., Stuiver, A. and Meyer, J.-.J.C. (2014). Applying Neural-Symbolic Cognitive Agents in Intelligent Transport Systems to reduce CO<inf>2</inf> emissions. 2014 International Joint Conference on Neural Networks (IJCNN) 6-11 July.doi:10.1109/ijcnn.2014.6889788
  • Perotti, A., d'Avila Garcez, A. and Boella, G. (2014). Neural Networks for Runtime Verification. 2014 International Joint Conference on Neural Networks (IJCNN) 6-11 July.doi:10.1109/ijcnn.2014.6889961
  • Sigtia, S., Benetos, E., Cherla, S., Weyde, T., Garcez, A., Dixon, S.... Lee, J.H. (2014). An RNN-based Music Language Model for Improving Automatic Music Transcription. 15th International Society for Music Information Retrieval Conference (ISMIR) 27-10 January, Taipei, Taiwan.
  • Tran, S.N., Wolff, D., Weyde, T. and Garcez, A.D.A. Feature preprocessing with Restricted Boltzmann Machines for music similarity learning. .
  • De Penning, L., D'Avila Garcez, A.S., Lamb, L.C. and Meyer, J.J.C. Neural-symbolic cognitive agents: Architecture, theory and application. .
  • Perotti, A., Boella, G. and D'Avila Garcez, A. Scalable process monitoring through rules and neural networks. .
  • Cherla, S., Weyde, T. and d’Avila Garcez, A. Multiple viewpoint melodic prediction with fixed-context neural networks. .
  • Sigtia, S., Benetos, E., Boulanger-Lewandowski, N., Weyde, T., Garcez, A. and Dixon, S. A Hybrid Recurrent Neural Network For Music Transcription. .
  • França, M.V.M., Garcez, A.S.D. and Zaverucha, G. Relational knowledge extraction from attribute-value learners. .doi:10.4230/OASIcs.ICCSW.2013.35
  • Boella, G., Colombo Tosatto, S., Garcez, A.D., Genovese, V., Ienco, L. and van der Torre, L. (2011). Embedding Normative Reasoning into Neural-Symbolic Systems. IJCAI Workshop on Neural-Symbolic Learning and Reasoning NeSy11 July-, Barcelona.
  • Borges, R.V., Garcez, A.D., Lamb, L.C. and Nuseibeh, B. Learning to adapt requirements specifications of evolving systems (NIER Track). .
  • Boella, G., Colombo Tosatto, S., Garcez, A.D., Genovese, V., Ienco, D. and van der Torre, L. (2011). A Neural-Symbolic System for Normative Agents. 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS'11) May-, Taipei.
  • Garcez, A.D., Lamb, L.C. and Hitzler, P. Proceedings of IJCAI International Workshop on Neural-Symbolic Learning and Reasoning (NeSy11). Barcelona, Spain.
  • Borges, R.V., Garcez, A.D., Lamb, L.C. and Nuseibeh, B. Learning to Adapt Requirements Specifications of Evolving Systems (NIER Track). .
  • Garcez, A.D., de Penning, L., Lamb, L.C. and Meyer, J.J. (2010). An Integrated Neural Symbolic Cognitive Agent Architecture for Training and Assessment in Simulators. AAAI Workshop on Neural-Symbolic Learning and Reasoning NeSy'10 July-, Georgia.
  • Komendantskaya, E., Broda, K. and Garcez, A.D. (2010). Using Inductive Types for Ensuring Correctness of Neuro-Symbolic Computations. Computability in Europe CiE 2010 June-, Ponta Delgada, Portugal.
  • Boella, G., Colombo Tosatto, S., Garcez, A.D. and Genovese, V. (2010). On the Relationship between I-O Logic and Connectionism. Nonmonotonic Reasoning NMR 2010 Preferences and Norms workshop May-, Toronto.
  • Komendantskaya, E., Broda, K. and Garcez, A.D.A. Neuro-symbolic representation of logic programs defining infinite sets. .doi:10.1007/978-3-642-15819-3_39
  • Borges, R.V., Garcez, A.D. and Lamb, L.C. Representing, Learning and Extracting Temporal Knowledge from Neural Networks: A Case Study. .
  • Ryman-Tubb, N.F. and Garcez, A.D. SOAR - Sparse Oracle-based Adaptive Rule Extraction: Knowledge extraction from large-scale datasets to detect credit card fraud. .
  • Guillame-Bert, M., Broda, K. and Garcez, A.D. First-order Logic Learning in Artificial Neural Networks. .
  • Garcez, A.D., Hammer, B., Hitzler, P., Maass, W. and Toussaint, M. Neurons and Symbols: A Manifesto. Dagstuhl Seminar Proceedings 10302. Learning paradigms in dynamic environments Schloss Dagstuhl Leibniz-Zentrum fuer Informatik.
  • Ren, L. and Garcez, A.D.A. Symbolic knowledge extraction from support vector machines: A geometric approach. .doi:10.1007/978-3-642-03040-6_41
  • Garcez, A.D. and Hitzler, P. Proceedings of IJCAI International Workshop on Neural-Symbolic Learning and Reasoning (NeSy09). Pasadena, USA.
  • Garcez, A.D. and Hitzler, P. Proceedings of ECAI International Workshop on Neural-Symbolic Learning and Reasoning (NeSy08). Patras, Greece.
  • Borges, R.V., Lamb, L.C. and Garcez, A.D. (2007). Reasoning and Learning about Past Temporal Knowledge in Connectionist Models. 20th International Joint Conference on Neural Networks (IJCNN 2007) August-, Orlando.
  • Child, C., Stathis, K. and Garcez, A.D. (2007). Learning to Act with RVRL Agents. 14th RCRA Workshop, Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion July-, Rome.
  • Lamb, L.C., Borges, R.V. and Garcez, A.D. (2007). A Connectionist Cognitive Model for Temporal Synchronisation and Learning. 22nd National Conference on Artificial Intelligence (AAAI 2007) July-, Vancouver.
  • Borges, R.V., Lamb, L.C. and Garcez, A.D. (2007). Towards Reasoning about the Past in Neural-Symbolic Systems. IJCAI Workshop on Neural-Symbolic Learning and Reasoning NeSy07 January-, Hyderabad, India.
  • Garcez, A.D., Hitzler, P. and Tamburrini, G. Proceedings of IJCAI International Workshop on Neural-Symbolic Learning and Reasoning (NeSy07). Hyderabad, India.
  • Borges, R.V., Lamb, L.C. and Garcez, A.D. (2006). Combining Architectures for Temporal Learning in Neural-Symbolic Systems. 6th International Conference on Hybrid Intelligent Systems ((HIS'06)) December-.
  • Carneiro, R., Dias, S.S., Fardin Jr, D., Oliveira, H., Garcez, A.D. and de Souza, A.F. Improving VG-RAM Neural Network Performance using Knowledge Correlation. 13th International Conference on Neural Information Processing (ICONIP'06) Hong Kong.
  • Ray, O. and Garcez, A.D. (2006). Towards the Integration of Abduction and Induction in Artificial Neural Networks. ECAI Workshop on Neural-Symbolic Learning and Reasoning NeSy06 August-, Riva del Garda, Italy.
  • Garcez, A.D., Lamb, L.C., Gabbay, D.M., Weiss, Y., Scholkopf, B. and Platt, J. A Connectionist Model for Constructive Modal Reasoning. In Advances in Neural Information Processing Systems 18 (NIPS 2005) Vancouver.
  • Garcez, A.D., Hitzler, P. and Tamburrini, G. Proceedings of ECAI International Workshop on Neural-Symbolic Learning and Reasoning (NeSy06). Trento, Italy.
  • Hitzler, P., Bader, S. and Garcez, A.D. (2005). Ontology Learning as a Use-Case for Neural-Symbolic Integration. IJCAI Workshop on Neural-Symbolic Learning and Reasoning NeSy05 August-, Edinburgh.
  • Garcez, A.D. Fewer Epistemological Challenges for Connectionism. Computability in Europe 2005: New Computational Paradigms Amsterdam.
  • Bader, S. and Garcez, A.D. Computing First Order Logic Programs by Fibring Artificial Neural Networks. 18th International FLAIRS Conference Florida.
  • Garcez, A.D., Elman, J. and Hitzler, P. Proceedings of IJCAI International Workshop on Neural-Symbolic Learning and Reasoning (NeSy05). Edinburgh, Scotland.
  • Garcez, A.D., Gabbay, D.M. and Lamb, L.C. Towards a Connectionist Argumentation Framework. 16th European Conference on Artificial Intelligence (ECAI'04) Valencia.
  • Garcez, A.D. and Gabbay, D.M. Fibring Neural Networks. 19th National Conference on Artificial Intelligence (AAAI'04) San Jose, California.
  • Rodrigues, O., Garcez, A.D.A. and Russo, A. Reasoning about requirements evolution using clustered belief revision. .doi:10.1007/978-3-540-28645-5_5
  • Garcez, A.D., Gabbay, D. and Lamb, L.C. Argumentation neural networks. .
  • Garcez, A.D., Lamb, L.C., Thrun, S., Saul, L. and Schoelkopf, B. Reasoning about Time and Knowledge in Neural-Symbolic Learning Systems. Advances in Neural Information Processing Systems 16 (NIPS 2003) Vancouver.
  • Garcez, A.D., Dustdar, S., Gall, H., Lucia, A., Mana, A., Menzies, T.... Russo, A. Proceedings of Automated Software Engineering: Workshops at the 19th International Conference on Automated Software Engineering ASE'04. .
  • Rodrigues, O., Garcez, A.D. and Russo, A. (2003). Reasoning about Requirements Evolution using Clustered Belief Revision. ESEC/FSE International Workshop on Intelligent Technologies for Software Engineering (WITSE'03) September-, Helsinki.
  • Spanoudakis, G., Zisman, A. and Garcez, A. Proceedings of Workshop on Intelligent Technologies for Software Engineering (WITSE03). In conjunction with the 9th European Software Engineering Conference and the 11th Symposium on Foundations of Software Engineering.
  • Spanoudakis, G., Garcez, A.D. and Zisman, A. (2003). Revising Rules to Capture Requirements Traceability Relations: A Machine Learning Approach. Fifteenth International Conference on Software Engineering and Knowledge Engineering (SEKE'03) July-, San Francisco.
  • Garcez, A.D., Lamb, L.C., Broda, K. and Gabbay, M. Distributed Knowledge Representation in Neural--Symbolic Learning Systems: A Case Study. AAAI International FLAIRS Conference Florida.
  • Garcez, A.D., Lamb, L.C. and Gabbay, D.M. (2002). A Connectionist Inductive Learning System for Modal Logic Programming. 9th IEEE International Conference on Neural Information Processing ICONIP'02 November-, Singapore.
  • Garcez, A.D. (2002). Extended Theory Refinement in Knowledge-based Neural Networks. IEEE International Joint Conference on Neural Networks (IJCNN'2002). World Congress on Computational Intelligence May-, Hawaii.
  • Garcez, A.D., Russo, A., Nuseibeh, B. and Kramer, K. (2001). An Analysis-Revision Cycle to Evolve Requirements Specifications. 16th IEEE International Conference on Automated Software Engineering (ASE-2001) November-, San Diego.
  • Broda, K., Garcez, A.D. and Gabbay, D.M. (2000). Metalevel Priorities and Neural Networks. Workshop on the Foundations of Connectionist-Symbolic Integration ECAI2000 August-, Berlin.
  • Garcez, A.D., Broda, K., Gabbay, D.M. and de Souza, A.F. (1999). Knowledge Extraction from Trained Neural Networks: A Position Paper. 6th IEEE International Conference on Neural Information Processing ICONIP'99 November-, Australia.
  • Basilio, R., Zaverucha, G. and Garcez, A.D. (1998). Inducing Relational Concepts with Neural Networks via the LINUS System. 5th International Conference on Neural Information Processing ICONIP'98 October-, Kitakyushu, Japan.
  • Garcez, A.D., Zaverucha, G. and da Silva, V.N.L. (1997). Applying the Connectionist Inductive Learning and Logic Programming System to Power Systems Diagnosis. IEEE International Joint Conference on Neural Networks (IJCNN'97) June-, Houston.
  • Garcez, A.D., Zaverucha, G. and de Carvalho, L.A.V. (1996). Logic Programming and Inductive Learning in Artificial Neural Networks. Workshop on Knowledge Representation and Neural Networks. XX German Conference on Artificial Intelligence KI96 September-, Dresden, Germany.
  • Garcez, A.D., Zaverucha, G. and de Carvalho, L.A.V. (1996). Logical Inference and Inductive Learning in Artificial Neural Networks. Workshop on Neural Networks and Structured Knowledge ECAI'96 August-, Budapest.
  • Garcez, A.D., Zaverucha, G. and da Silva, V.N.L. Programa çao em Lógica Estendida e Aprendizado Indutivo em Redes Neurais:Uma Aplicaçao em Sistemas de Potencia. 3rd Simposio Brasileiro de Redes Neurais (SBRN'96) Recife, Brasil.
  • Garcez, A.D., Zaverucha, G. and Carvalho, L.A.V. (1995). Inferencia Lógica e Aprendizado Automáatico em Redes Neurais: Uma Representaçao Integrada do Conhecimento. 2nd Congresso Brasileiro de Redes Neurais CBRN'95 October-, Curitiba, Brazil.
  • Philps, D., Weyde, T., Garcez, A.D. and Batchelor, R. Continual Learning Augmented Investment Decisions. .
  • Tran, S.N. and Garcez, A.D. Neurosymbolic Reasoning and Learning with Restricted Boltzmann Machines. .doi:10.1609/aaai.v37i5.25806

Journal articles (69)

Reports (15)

Theses/dissertations (3)

  • Garcez, A.D. Nonmonotonic Theory Refinement in Artificial Neural Networks. (PhD)
  • Garcez, A.D. Um Sistema Neural para Programaçao em Lógica com Aprendizado Indutivo. (Master's thesis)
  • Garcez, A.D. Redes Neurais: Fundamentos e Aplicaçoes. Final Year Project, Computing Engineering. (Undergraduate dissertation)

Working paper