Bibliography

Retrieve Jean-Daniel Zucker's bibliography as an EndNote file: JDZPublis.enl

Papers when available are either in PostScript or PDF format.

2003

  1. Bredèche, N., Y. Chevaleyre, et Zucker (2003). "A meta-learning approach to anchor visual percepts." Robotics and Autonomous System Journal l(special issue on Anchoring Symbols to Sensor Data in Single and Multiple Robot Systems.) (to appear).

2002

  1. Breton, L., P. Claudin, E. Clément, J-D. Zucker (2002). "Stress Response Function to a two-dimensional ordered packing of frictional beads." Europhysics Letter. PrePrint.pdf

  2. Drogoul, A. and J.-D. Zucker (2002). "Les premiers pas des robots sociaux." La Recherche(350): 91-94.

  3. Mustière, S. and J.-D. Zucker (2002). Généralisation Cartographique et apprentissage automatique à partir d'exemples. Généralisation et Représentations Multiples. A. Ruas. Paris, Hermès. Chapitre 20: 353-368.

  4. Chevaleyre, Y., N. Bredèche, et J-D. Zucker (2002). Learning Rules from Multiple-Instance Data: Issues and Algorithms. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, (IPMU), Annecy, France.

  5. Machado, A., G. Ramalho, A. Drogoul, J-D. Zucker (2002). Multi-Agent Patrolling: an Empirical Analysis of Alternative Architectures. Multiagnet Based Simulation, Bolona, Italy.

  6. Bournaud, I., M. Courtine, et J-D. Zucker (2002). Propositionalization for Clustering Symbolic Relational Descriptions. International Conference on Inductive Logic Programming (ILP'2002), Sidney, Aus.

  7. Zucker , J.-D., N. Bredèche, et L. Saitta (2002). Abstracting Visual Percepts to learn Concepts. Symposium on Abstraction Reformulation and Approximation, SARA'2002, Canada.

  8. Machado, A. P., A. L. Almeida, G. Ramalho, J-D. Zucker, A. Drogoul (2002). Multi-Agent Movement Coordination in Patrolling. First Workshop on Agents in Computer Games, at The 3rd International Conference on Computers and Games (CG'02), Edmonton, Canada.

2001

  1. Zucker , J.-D. (2001). Changements de représentation, Abstractions et Apprentissages.Mémoire d'habilitation à diriger des recherches, LIP6. Paris, Université Pierre et Marie Curie. HDR.pdf
  2. Saitta, L. and J.-D. Zucker (2001). “A Model of Abstraction in Visual Perception.” Special Issue on "Machine Learning in Computer Vision" ,volume 15, No. 8, of the "Applied Artificial Intelligence journal". 15(8).

  3. Chevaleyre, Y. and J.-D. Zucker (2001). A framework for learning Multiple-Instance Decision Trees and Rule Sets. European Conference on Machine Learning.

  4. J.-D. Zucker (2001). Solving Multiple-Instance and Multiple-Part Learning Problems with Decision Trees and Rule Sets. Application to the Mutagenesis Problem. Canadian Conference on AI 2001. pp. 204-214.

  5. Breton, L., J.-D. Zucker and E. Clement (2000). A Multi-agent approach for the resolution of equations in granular physics. Multi-agent systems and Agent-Based Simulation. Boston, USA, Springer

2000

  1. Wang, Jun, J.-D. Zucker (2000). Solving the Multiple-Instance Problem: A Lazy Learning Approach. International Conference in Machine Learning (ICML'2K), Morgan Kauffman. abstract. ICML2K.pdf

  2. Mustière, S., J.-D. Zucker, L. Saitta (2000). An Abstraction-Based Machine Learning approach to Cartographic Generalization. Spatial Data Handling (SDH'2000), Beijing, CHINA.. SDH2000.pdf

  3. Zucker, J.-D., S. Mustiere and L. Saitta. Learning Abstraction and Representation Knowledge: An Application to Cartographic Generalisation. in The fifth International Workshop on Multistrategy Learning (MSL'2000). 2000. Guimarães, Portugal.. MSL2000.pdf

  4. Bournaud, I., M. Courtine and J.-D. Zucker (2000). Abstractions for Knowledge Organization of Relational Descriptions. in Symposium on Abstraction, Reformulation and Approximation , (SARA 2000). Horseshoe Bay Resort and Conference Club, Lake LBJ, Texas.

  5. Saitta, L. and J.-D. Zucker (2000). Abstraction and Phase Transitions in Relational Learning. in Symposium on Abstraction, Reformulation and Approximation (SARA 2000). 2000. Horseshoe Bay Resort and Conference Club, Lake LBJ, Texas.. SARA2000

  6. Breton L., J-D. Zucker, Clément Eric (2000). GranuLab : un laboratoire virtuel d'experimentations pour la decouverte scientifique en physique granulaire. 12eme congrès AFRIF-AFIA, Reconnaissance des Formes et Intelligence Artificielle, RFIA 2000, Paris, France.

  7. Mustière, S., J.-D. Zucker , L. Saitta (2000). Abstraction et Changement de Langage pour Automatiser la Généralisation Cartographique. pp. 411-418, Vol. 3, 12eme congrès AFRIF-AFIA, Reconnaissance des Formes et Intelligence Artificielle, RFIA 2000, Paris, France SBRFIA2000.pdf.

  8. Chevaleyre, Y. and J.-D. Zucker (2000). Noise-Tolerant Rule Induction from Multi-Instance Data. Proceedings of the ICML-2000 Workshop on Attribute-Value and Relational Learning: Crossing the Boundaries", L. D. Raedt and S. Kramer Eds, Stanford, USA.ICMLWRK.pdf

  9. Chevaleyre, Y. and J.-D. Zucker (2000). 'Solving multiple-instance and multiple-part learning problems with decision trees and decision rules. Application to the mutagenesis". Internal Report 2000-18. lip6.2000.018.ps.gz

1999

  1. Breton L. & Zucker J-D., Clément Eric. Une approche multi-agents pour la résolution d’équations en physique des milieux granulaires. to appear in the proceedings of JFIADSMA'99, Hermès, Paris, France. VirtualLab.pdf

  2. Mustière, S., J.-D. Zucker , L. Saitta (1999). Cartographic Generalization as a Combination of Representing and Abstracting Knowledge. ACM/GIS'99, Texas, USA. 99-SM-ACMGIS99.pdf

  3. Perny, P. & J.-D. Zucker (1999). Collaborative Filtering Methods based on Fuzzy Preference Relations. EUROFUSE-SIC'99, Budapest.. Collaborative.pdf

  4. Meyer, C. & J.-D. Zucker (1999). " Mind-Reading Machines " Modélisation des adversaires et anticipation dans les jeux à information complète et imparfaite. Journées Française de l'apprentissage, JFA'99, Palaiseau, France.Cap99.pdf

1998

  1. Zucker, J.-D., J.-G. Ganascia, Isabelle Bournaud (1998). "Relational Knowledge Discovery in a Chinese Characters Database." Applied Artificial Intelligence Journal 12(5), pp.455-488.

  2. Bournaud, I. and Zucker J.-D.(1998). Integrating Machine Learning Techniques in A Guided Discovery Tutoring Environment for Chinese Characters. International Journal of Chinese and Oriental Languages Information Processing Society, 8(2).

  3. Drogoul A. & Zucker, J.-D. (1998). Methodological Issues for Designing Multi-Agent Systems with Machine Learning Techniques: Capitalizing Experiences from the RoboCup Challenge. Rapport Interne du LIP6 N°41.(abstract). ADJDZama98.pdf

  4. Zucker, J.-D.  (1998). Abstraction for Concept Representation. The Fourth International Workshop on Multistrategy Learning (MSL'98), Desenzano del Garda (Brescia, Italy).

  5. Saitta, L. and J.-D. Zucker (1998). Semantic Abstraction for Concept Representation and Learning. Symposium on Abstraction, Reformulation and Approximation (SARA98), Asilomar Conference Center, Pacific Grove, California LSJDZSARA98.pdf.

  6. Zucker, J.-D. and Y. Chevaleyre (1998). Comprendre et résoudre les problèmes d’apprentissage multi-instances et multi-parties. 13èmes Journées Francophones sur l'Apprentissage, Arras, France. Jfa98MIP.pdf

  7. Zucker, J.-D. and J.-G. Ganascia (1998). Learning Structurally Indeterminate Clauses. The Eighth International Conference on Inductive Logic Programming (ILP'98), Madison, Wisconsin,. ZuckerFinalILP98.pdf

  8. Bournaud I. & Zucker J.-D. (1998) Discovery in Chinese Phonetics: a Machine Learning Approach, ECAI'98, Discovery Workshop, Brighton, Royaume Uni, Aout 1998.


1997

  1. Meyer, C., J.-G. Ganascia, et Jean-Daniel Zucker. (1997). Learning Strategies in Games by Anticipation. International Joint Conference on Artificial Intelligence (IJCAI), Nagoya, Japan.

  2. Meyer, C., J.-G. Ganascia, et Jean-Daniel Zucker. (1997). Modélisation de stratégies humaines par Apprentissage et Anticipation génétiques. Journées Française de l'apprentissage, JFA'97, Roscoff, France..JFA97.pdf


1996

  1. Zucker, J.-D., & Ganascia, J.-G. (1996). Changes of Representation for Efficient Learning in Structural Domains. In International Conference in Machine Learning, Bary, Italie: Morgan Kauffman. JDZml96.pdf

  2. Zucker, J.-D. (1996) Appariements et Changements de Représentation pour l'Apprentissage Symbolique. Thèse de Docteur en Sciences (PhD. Thesis), Spécialité Informatique, Université Paris VI.


1995

  1. Zucker, J.-D. (1995). Integrating Machine Learning Techniques in a Guided Discovery Tutoring Environment: MEMOCAR. In Emerging Computer Technologies in Education (pp. 189-206).

  2. Zucker, J.-D. (1995). Matching-based Representation Changes for concept learning of structural descriptions. In International Workshop on Inteligent Adaptative System 95, . Melbourne, Florida:

  3. Zucker, J.-D. (1995). Reformulation of Examples in Concept Learning of Structural Descriptions. In 4th Congress of the Italian Association for Artificial Intelligence, AI*IA'95, LNAI N¡992 (pp. 377-388). Florence, Italie: Springer Verlag.

  4. Bournaud, I., & Zucker, J.-D. (1995). Découverte de similitudes entre objets structurés par exploration d'un espace de généralisation. In Environnements Interactifs d1Apprentissage avec Ordinateurs, (pp. 67-78). Cachan: Eyrolles.

  5. Zucker, J.-D. (1995). Integrating Machine Learning Techniques in a Guided Discovery Tutoring Environment: MEMOCAR No. 95/07). LAFORIA-IBP.

  6. Bellassen, J., Zucker, J.-D., Pittore, M.-J., & Marnef, L. (1995). The Chinese, Didactics and New Technologies tripod: An academic initiative : the CDT group. In International Conference on New Technologies in Teaching and Learning Chinese, . San Francisco, USA.

1994

  1. Zucker, J.-D., & Ganascia, J.-G. (1994). Selective Reformulation of Examples in Concept Learning. In W. Cohen (Ed.), International Conference on Machine Learning (ICML-94), (pp. 352-360). New-Brunswick: Morgan Kaufmann Publishers. JDZICML94.pdf (not ready yet)

  2. Zucker, J.-D., & Ganascia, J.-G. (1994). REMO: A moriological approach to representation shift in Concept Learning. In Pacific Rim International Conference on Artificial Intelligence, .

  3. Zucker, J.-D., Corruble, V., Thomas, J., & Ramalho, G. (1994). DICE: a Discovery Environment integrating Inductive Bias. In AAAI-94, Workshop on Knowledge Discovery in Databases, . Seattle, USA. JDZKDD94.ps.zip

  4. Zucker, J.-D., & Bournaud, I. (1994). Généralisation inductive et changement de représentation de connaissances structurées. In Premier Colloque Jeunes Chercheurs en Sciences Cognitives, (pp. 257). Grenoble: ARC et In Cognito. 

  5. Corruble, V., Zucker, J.-D., Thomas, J., & Ramalho, G. (1994). DICE: un environnement pour la decouverte. In Secondes Rencontres des Jeunes Chercheurs en Itelligence Artificielle, (pp. 127--134). Marseille.

  6. Zucker, J.-D., & Ganascia, J.-G. (1994). Selective Reformulation of Examples in Concept Learning No. 95-08). LAFORIA-IBP.

  7. Zucker, J.-D., & Ganascia, J.-G. (1994). Une approche moriologique de la reformulation d'exemples en apprentissage. In Neuvième Journées Française de l1Apprentissage, (pp. I1-I14). Strasbourg.

  8. Bournaud, I., & Zucker, J.-D. (1994). Détection de Similarités dans un environnement didactique. In Secondes Rencontres des Jeunes Chercheurs en Intelligence Artificielle. Marseille.

1993 and earlier

  1. Zucker, J.-D., Bournaud, I., Mathieu, J., & Bellassen, J. (1993). MEMOCAR: Un environnement coopératif d'apprentissage par la découverte des caractères chinois. In Sciences Cognitives Informatique et Apprentissage des Langues, . Clermont-Ferrand

  2. Zucker, J.-D., & Mathieu, J. (1993). Machine Learning Contributions to a Guided Discovery Tutoring Environment for Chinese Characters. In International Conference on Computers in Education, (pp. 25-30). Taipei, Taiwan R.O.C. JDZICCE93.ps.zip

  3. Bournaud, I., Mathieu, J., & Zucker, J.-D. (1993). COOPERE: Un formalisme de représentation des COnnaissances Organisées Pour l'Explication, la Résolution et l'Enseignement. In Environnements Interactifs d1Apprentissage avec Ordinateurs, (pp. 77-89). Cachan: Eyrolles.

  4. Zucker, J.-D. (1992). Une contribution de l1Apprentissage Symbolique Automatique au diagnostic et l'explication de connaissances liées aux caractères chinois (Memoire de stage du D.E.A. I.A.R.F.A.) LAFORIA Université Paris VI. 

  5. Derniame, J.-c., & Zucker, J.-D. (1991). La construction d'ateliers de logiciels en partant de modèles de procédés. In UNIVERDUSTRIE, (pp. 23-38). Nancy:

  6. Oquendo, F., & Zucker, J.-D. (1990). ALF: a Generic Software Process-centered CASE Environment. In TOOLS'90, . Toulouse,France:

  7. Oquendo, F., Zucker, J.-D., & Griffiths, P. (1990). ALF: a Generic Software Process-centered CASE Environment. In Fourth International Workshop on Computer-aided Software Engineering . Irvine, CA.

  8. Zucker, J.-D. (1989). ALF: Accueil de Logiciel Futur. In European CASE and Sofware Quality conference, . Milano, Italy.

  9. Zucker, J.-D. (1989). Advanced Software Engineering Environment Logistics Framework. In ESPRIT'89 conference, . Brussels, Belgium.

  10. Zucker, J.-D. (1990). Advanced Software Engineering Environment Logistics Framework. In ESPRIT'90 conference, . Brussels, Belgium.

  11. Zucker, J.-D. (1991). ALF: Accueil de Logiciel Futur. In International Conference on Software Engineering Environments (SEE'91), (pp. ?21-52). Aberystwyth, Wales:

  12. Zucker, J.-D. (1991), ALF: Accueil de Logiciel Futur. PCTE NEwsletter..