Call for Papers: Journal of AI Research (JAIR), Special Track on Deep Learning and Symbolic Reasoning
Cognitive Computation at NIPS 2016, towards explainable AI, Barcelona, December 2016
Dagstuhl seminar on Human-like Neural-Symbolic Computing, Wadern, May 2017
The Research Centre for Machine Learning
Artur Garcez is Professor of Computer Science at City, University of London. He holds a Ph.D. in Computing (2000) from Imperial College London. He is a Fellow of the British Computer Society (FBCS), member of the ACM, AAAI, IEEE, CGCA, and partner at Performance Systems, Rio de Janeiro, and Cognitive Intelligence, London.
Garcez is Director of the Research Centre for Machine Learning
at City, president of the Steering Committee of the
Neural-Symbolic Learning and Reasoning Association
, London, and founding director of City's MSc in Data Science
. For more information about neural-symbolic computation, please visit www.neural-symbolic.org
Garcez has an established track record of research in Machine Learning, Neural Computation and Artificial Intelligence. He has co-authored two books: Neural-Symbolic Cognitive Reasoning
, with Lamb and Gabbay (Springer 2009), and Neural-Symbolic Learning Systems
, with Broda and Gabbay (Springer 2002). His research has led to publications in Behavioral & Brain Sciences, Theoretical Computer Science, Neural Computation, Machine Learning, Journal of Logic and Computation, Artificial Intelligence, Journal of Applied Logic, IEEE Transactions on Neural Networks and Learning Systems, and Studia Logica. He has consistently published at the flagship Artificial Intelligence and Neural Computation conferences AAAI, NIPS, IJCAI, AAMAS, IJCNN, ECAI.Garcez is editor-in-chief of the Neural Computing and Artificial Intelligence
book series. He is area scientific editor of the Journal of Applied Logic
(Logics and Neural Networks), area editor of the Journal of Logic and Computation
(Reasoning and Learning, with L. Valiant), associate editor of the International Journal on Artificial Intelligence Tools
, and the Journal of AI Research (JAIR)
, special track on deep learning and symbolic reasoning, and member of the editorial boards of The Logic Journal of the IGPL
and The International Journal of Hybrid Intelligent Systems
. Garcez is a member of the advisory board of the Cognitive Technologies
book series. He is Associate Member of Behavioral and Brain Sciences
, was founding co-chair of the International Workshop on Neural-Symbolic Learning and Reasoning
(NeSy), held yearly since 2005, and co-organiser of Dagstuhl seminar 14381 on Neural-Symbolic Learning and Reasoning
and Dagstuhl seminar 17192 on Human-like Neural-Symbolic Computing
, Wadern, Germany, September 2014 and May 2017.
Garcez has acted as reviewer for a number of international journals on Logic, Cognitive Science, Neural Computation and Artificial Intelligence, including IEEE Transactions on Neural Networks and Learning Systems, Artificial Intelligence, Machine Learning, Cognitive Systems Research, AI Communications, Cognitive Science, Neurocomputing, Information and Computation. He has guest-edited two journal special issues, and co-edited two research monographs. He has served on the Programme Committee or Organizing Committee of a large number of international conferences and workshops, including IJCAI, NIPS, AAAI, AAMAS, ECAI, ICANN, ASE and IJCNN.Garcez was awarded a two-year Nuffield foundation research grant in the area of neural-symbolic integration (2002-2004). He was Principal Investigator in the EU-funded research project BioGrid (2003-2004) and industry-funded projects RoboCup Physical Visualization League (2007) and Dynamic Fraud Prevention (2009). He was co-investigator in the EU-funded research project Genestream (2003), was awarded a Daiwa Foundation Grant (2006), and has been consistently awarded conference travel grants by The Royal Society (2002, 2003, 2004, 2005, 2007, 2010). Currently, he is Principal Investigator in the EPSRC/Technology Strategy Board project EP/M50712X/1 Advancing Consumer Protection through Machine Learning (with Bet Buddy Ltd.), and the EPSRC/Technology Strategy Board project EP/M507064/1 FareViz: On the Design of Real-Time Data Exploration Tools (with Placr Ltd, Digital MR, and Raileasy).
Selected Publications (complete list available here; google scholar profile here):
- S. Tran and A. S. d'Avila Garcez. Deep Logic Networks: Inserting and Extracting Knowledge from Deep Belief Networks. IEEE Transactions on Neural Networks and Learning Systems. To appear, 2016.
- M. Franca, G. Zaverucha and A. S. d'Avila Garcez. Fast Relational Learning using Bottom Clause Propositionalization with Artificial Neural Networks, Machine Learning 94(1):81-104, Springer, 2014.
- R. V. Borges, A. S. d'Avila Garcez and L. C. Lamb. Learning and Representing Temporal Knowledge in Recurrent Networks. IEEE Transactions on Neural Networks, 22(12):2409 - 2421, December 2011.
- L. de Penning, A. S. d'Avila Garcez, L. C. Lamb and J. J. Meyer. A Neural-Symbolic Cognitive Agent for Online Learning and Reasoning. In Proc. IJCAI'11, Barcelona, Spain, July 2011.
- Artur S. d'Avila Garcez, L. C. Lamb and D. M. Gabbay. Neural-Symbolic Cognitive Reasoning. Cognitive Technologies, Springer, ISBN 978-3-540-73245-7, 2009.
- Artur S. d'Avila Garcez, D. M. Gabbay, O. Ray and J. Woods. Abductive Reasoning in Neural-Symbolic Learning Systems. Topoi: An International Review of Philosophy, 26:37-49, March 2007.
- Artur S. d'Avila Garcez, L. C. Lamb and D. M. Gabbay. Connectionist Modal Logic: Representing Modalities in Neural Networks. Theoretical Computer Science, 371(1-2):34-53, February 2007.
- Artur S. d'Avila Garcez, L. C. Lamb and D. M. Gabbay. Connectionist Computations of Intuitionistic Reasoning. Theoretical Computer Science, 358(1):34-55, July 2006.
- Artur S. d'Avila Garcez and L. C. Lamb. A Connectionist Computational Model for Epistemic and Temporal Reasoning. Neural Computation, 18(7):1711-1738, July 2006.
- Artur S. d'Avila Garcez, K. Broda and D. M. Gabbay. Neural-Symbolic Learning Systems: Foundations and Applications, Perspectives in Neural Computing, Springer, ISBN 1-85233-512-2, 2002.
- Artur S. d'Avila Garcez, K. Broda and D. M. Gabbay. Symbolic Knowledge Extraction from Trained Neural Networks: A Sound Approach. Artificial Intelligence, 125(1-2):153-205, January 2001.
Artur d'Avila Garcez, FBCS
Professor of Computer Science
Department of Computer Science
City, University of London, EC1V 0HB, UK
Tel: + 44 (0)20 7040 8344