Journals/Conference Proceedings :

• Learning Temporal Association Rules on Symbolic Time Sequences [pdf]
  Mathieu Guillame-Bert and James L. Crowley
  In Proceedings of the 2012 4th Asian Conference on Machine Learning, Singapore, 2012
  


• Planning with Inaccurate Temporal Rules [pdf]
  Mathieu Guillame-Bert and James L. Crowley
  In Proceedings of the 2012 IEEE 24rd International Conference on Tools with Artificial Intelligence, Athens, Greece, 2012
  


• New Approach on Temporal Data Mining for Symbolic Time Sequences: Temporal Tree Associate Rules [pdf]
  Mathieu Guillame-Bert and James L. Crowley
  In Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, Boca Raton, Florida USA, 2011
  


• Predicting Home Service Demands from Appliance Usage Data [pdf]
  Kaustav Basu, Mathieu Guillame-Bert, Hussein Joumaa, Stephane Ploix and James Crowley
  In Proceedings of the 3rd International Conference on Information and Communication Technologies and Applications ICTA 2011, Orlando, Florida, USA, 29 November - 2 December 2011
  


• First-order Logic Learningin Artificial Neural Networks [pdf]
  Mathieu Guillame-Bert, Krysia Broda and Artur d'Avila Garcez
  In Proceedings of 23rd International Joint Conference on Neural Networks IJCNN 2010, Barcelona, Spain, 18-23 July 2010
  2010 IEEE World Congress On Computational Intelligence
  

Reports :

• PhD's Thesis, Learning Temporal Association Rules on Symbolic Time Sequences [Non final version] [pdf]
  Under the supervision of Pr. James L. CROWLEY
  PRIMA Team – INRIA Lab. – Grenoble - France, 2012
  Committee: Pr. Malik Gha llab, Pr. Paul Lukowicz, Dr. Artur Dubrawski, Pr. Augustin Lux
  
• Master's Thesis, Connectionist Artificial Neural Networks [pdf]
  With the supervision of Krysia Broda
  Imperial College of London, 2009
  Distinguished MSc project [page]
  


• Bs.C work, I-Terms unification [pdf]
  With the supervision of Nicolas Peltier
  ENSIMAG, 2008
  Download implementation (C++) [.zip]
  

Presentations :

• Learning Temporal Association Rules on Symbolic Time Sequences [pdf]
  Extract from the thesis defence