INTERACTIVE KNOWLEDGE CONSTRUCTION IN THE COLLABORATIVE BUILDING OF AN ENCYCLOPEDIA

One of the major challenges of applied artificial intelligence is to provide environments where high-level human activities like learning, constructing theories, or performing experiments are enhanced by artificial intelligence technologies. This paper starts with the description of an ambitious project: EnCOrE.1 The specific real-world EnCOrE scenario, significantly representing a much wider class of potential applicative contexts, is dedicated to the building of an encyclopedia of organic chemistry in the context of virtual communities of experts and students. Its description is followed by a brief survey of some major AI questions and propositions in relation with the problems raised by the EnCOrE project. The third part of the paper starts with some definitions of a set of “primitives” for rational actions, and then integrates them in a unified conceptual framework for the interactive construction of knowledge. To end with, we sketch out protocols aimed at guiding both the collaborative construction process and the collaborative learning process in the EnCOrE project. The current major result is the emerging conceptual model supporting interaction between human agents and AI tools integrated in Grid services within a socio-constructivist approach, consisting of cycles of deductions, inductions, and abductions upon facts (the shared reality) and concepts (their subjective interpretation) submitted to negotiations, and finally converging to a socially validated consensus.

[1]  L. Vygotsky Mind in Society: The Development of Higher Psychological Processes: Harvard University Press , 1978 .

[2]  Stefan Decker,et al.  Creating Semantic Web Contents with Protégé-2000 , 2001, IEEE Intell. Syst..

[3]  Stephen Muggleton,et al.  Inductive Logic Programming: Issues, Results and the Challenge of Learning Language in Logic , 1999, Artif. Intell..

[4]  Stephen Potter,et al.  COLLABORATION IN THE SEMANTIC GRID: A BASIS FOR e-LEARNING , 2005, Appl. Artif. Intell..

[5]  E. Mark Gold,et al.  Language Identification in the Limit , 1967, Inf. Control..

[6]  Gary James Jason,et al.  The Logic of Scientific Discovery , 1988 .

[7]  Boris Motik,et al.  User-Driven Ontology Evolution Management , 2002, EKAW.

[8]  Simon J. Cox,et al.  Towards a Knowledge-Based Approach to Semantic Service Composition , 2003, SEMWEB.

[9]  Riichiro Mizoguchi,et al.  Deployment of an ontological framework of functional design knowledge , 2004, Adv. Eng. Informatics.

[10]  A MusenMark,et al.  Creating Semantic Web Contents with Protégé-2000 , 2001 .

[11]  C. Mellish,et al.  Aspects of Speech Act Categorisation: Towards Generating Teachers' Language , 2000 .

[12]  T. B. Ismail,et al.  Best Paper Award , 2016, Journal of Failure Analysis and Prevention.

[13]  Stefano A. Cerri Open Learning Service Scenarios on GRIDs , 2003, LeGE-WG 3.

[14]  Amedeo Napoli,et al.  An Experiment on Knowledge Discovery in Chemical Databases , 2004, PKDD.

[15]  M. Felisa Verdejo,et al.  Collaborative Discovery Learning of Model Design , 2002, Intelligent Tutoring Systems.

[16]  Vincent Duquenne,et al.  Latticial Structures in Data Analysis , 1999, Theor. Comput. Sci..

[17]  Pat Langley,et al.  The computational support of scientific discovery , 2000, Int. J. Hum. Comput. Stud..

[18]  Pierluigi Ritrovato,et al.  Towards the Learning Grid - Advances in Human Learning Services , 2005, Towards the Learning Grid.

[19]  Andrew Wilkinson Compendium of Chemical Terminology , 1997 .

[20]  Rachel M. Pilkington,et al.  Investigation by Design: Developing Dialogue Models to Support Reasoning and Conceptual Change , 2000 .

[21]  Michel C. A. Klein,et al.  Ontology versioning on the Semantic Web , 2001, SWWS.

[22]  Michel C. A. Klein,et al.  Ontology Evolution: Not the Same as Schema Evolution , 2004, Knowledge and Information Systems.

[23]  V. Gold Compendium of chemical terminology , 1987 .

[24]  T. Kuhn,et al.  The Structure of Scientific Revolutions. , 1964 .

[25]  Jean Piaget,et al.  Sagesse et illusions de la philosophie , 1992 .

[26]  Marc Eisenstadt,et al.  Dynamic Learning Agents and Enhanced Presence on the Grid , 2003, LeGE-WG 3.

[27]  Ehud Shapiro,et al.  Inductive Inference of Theories from Facts , 1991, Computational Logic - Essays in Honor of Alan Robinson.

[28]  Lev Vygotsky Mind in society , 1978 .

[29]  Germana Menezes da Nóbrega,et al.  Une approche dialectique à la formation de théories : aspects conceptuels, formels et pragmatiques dans le cadre de l'apprentissage humain , 2002 .

[30]  Angus McIntyre,et al.  Nobile: User Model Acquisition in a Natural Laboratory , 1992 .

[31]  J. V. Rauff,et al.  Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence , 2005 .

[32]  Jacqueline Bourdeau,et al.  Collaborative Ontological Engineering of Instructional Design Knowledge for an ITS Authoring Environment , 2002, Intelligent Tutoring Systems.

[33]  Mark A. Musen,et al.  Ontologies in Support of Problem Solving , 2004, Handbook on Ontologies.

[34]  John H. Holland,et al.  Induction: Processes of Inference, Learning, and Discovery , 1987, IEEE Expert.

[35]  Herbert A. Simon,et al.  Scientific discovery , 1993, BMJ : British Medical Journal.

[36]  L. Magnani Abduction, Reason, and Science. Process of Discovery and Explanation , 2001 .

[37]  F. C. Phillips,et al.  TRAITÉ ÉLEMENTAIRE DE CHIMIE. , 1897 .

[38]  Mark Guzdial,et al.  Computer support for learning through complex problem solving , 1996, CACM.

[39]  John Stuart Mill,et al.  Système de logique déductive et inductive, exposé des principes de la preuve et des méthodes de recherche scientifique , 2002 .

[40]  L. S. Vygotskiĭ,et al.  Mind in society : the development of higher psychological processes , 1978 .

[41]  Catherine Colaux,et al.  EnCOrE : Encyclopédie de Chimie Organique Electronique , 2006 .

[42]  Jean Sallantin,et al.  On the Social Rational Mirror: Learning E-commerce in a Web-Served Learning Environment , 2002, Intelligent Tutoring Systems.

[43]  Mario Alai,et al.  A.I., Scientific Discovery and Realism , 2004, Minds and Machines.

[44]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .

[45]  G. Bachelard,et al.  La formation de l'esprit scientifique. , 1940 .

[46]  Martin Stacey,et al.  Scientific Discovery: Computational Explorations of the Creative Processes , 1988 .

[47]  Jean Sallantin,et al.  Structural Machine Learning with Galois Lattice and Graphs , 1998, ICML.

[48]  Ian Horrocks,et al.  On-To-Knowledge: Semantic Web-Enabled Knowledge Management , 2003 .

[49]  G. Edelman Neural Darwinism: The Theory Of Neuronal Group Selection , 1989 .

[50]  C. Peirce,et al.  Philosophical Writings of Peirce , 1955 .

[51]  Jon Williamson,et al.  Abduction, Reason, and Science: Processes of Discovery and Explanation , 2003 .

[52]  Stéphane Crozat,et al.  An Ontological Approach for Design and Evaluation of Tutoring Systems , 2000, Intelligent Tutoring Systems.

[53]  Alain Krief,et al.  EnCOrE (Encyclope'die de Chimie Organique Electronique): an Original Way to Represent and Transfer Knowledge from Freshmen to Researchers in Organic Chemistry , 2003, LeGE-WG 3.

[54]  Steffen Staab,et al.  International Handbooks on Information Systems , 2013 .

[55]  Jean Sallantin,et al.  A Contradiction-driven Approach of Learning in Discovery Learning Environments , 2003 .

[56]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

[57]  Jacques Ferber,et al.  Multi-agent systems - an introduction to distributed artificial intelligence , 1999 .

[58]  Herbert A. Simon,et al.  Scientific discovery: compulalional explorations of the creative process , 1987 .

[59]  Asunción Gómez-Pérez,et al.  Ontological Engineering: A state of the Art , 1999 .