Usuària:MT. Guilera/proves

Biography

Luc Steels obtained a Masters in Computer Science at MIT, specializing in AI under the supervision of Marvin Minsky and Carl Hewitt. He obtained a Ph.D at the University of Antwerp with a thesis in computational linguistics on a parallel model of parsing. In 1980, he joined the Schlumberger-Doll Research Laboratory in Ridgefield (US) to work on knowledge-based approaches to the interpretation of oil well logging data and became leader of the group who developed the Dipmeter Advisorwhich he transferred into industrial use while at Schlumberger Engineering, Clamart (Paris). In 1983, he was appointed tenured professor in Computer Science with a chair in AI at the Free University of Brussels (VUB). The same year he founded the VUB Artificial Intelligence Laboratory and became the first chair of the VUB Computer Science Department from 1990 to 1995. The VUB AI Lab focused initially on knowledge-based systems for various industrial applications (equipment diagnosis, transport scheduling, design) but gradually focused more on basic research in AI, moving along the cutting edge of the field.

More than 30 doctoral students graduated under his direction and many since developed distinguished careers in academic (e.g. Pattie Maes, Tony Belpaeme, Frederic Kaplan, Pierre-yves Oudeyer, [more]), industrial (e.g. Michael Spranger (https://ai.sony/people/Michael-Spranger/), Jean-Pierre Briot, Pieter Wellens, more <>) or governmental functions (e.g. Walter Van de Velde [eu]).

In 1996 Luc Steels founded the Sony Computer Science Laboratory (CSL) in Paris and became its acting director. This laboratory was a spin-off from the Sony Computer Science Laboratory in Tokyo directed by Mario Tokoro and Toshi Doi. The laboratory targeted cutting edge research in AI, particularly on the emergence and evolution of grounded language and ontologies on robots <>, the use of AI in music <>, and contributions to sustainability <>. The CSL music group was directed by Francois Pachet and the sustainability group by Peter Hanappe.

In 2011 Luc Steels became fellow at the Institute for Research and Advanced Studies (ICREA) and research professor at the Universitat Pompeu Fabra (UPF) in Barcelona, embedded in the Evolutionary Biology Laboratory (IBE). There he pursued further his fundamental research in the origins and evolution of language through experiments with robotic agents.

Throughout his career Luc Steels spent many research and educational visits to other institutions. He was a regular lecturer at the Theseus International Management Institute in Sophia Antipolis, developed courses for the Open University in the Netherlands, was Fellow at the Wissenschaftskolleg in Berlin during the years ? and ? <>, Fellow at Goldsmiths College London (computer science department) from ?, visiting scholar or lecturer at La Sapienza University Rome (physics department), Politecnico di Milano, and the universities of Ghana and Beijing (Jiaotong University).

Luc Steels was member of the New York Academy of Sciences, and elected member of the Academia Europea, and the Royal Belgian Academy of Arts and Sciences (Koninklijke Vlaamse Academie voor Wetenschappen en Kunsten),  where he serves as vice-director of the Natural Science section.

Contributions to science:

The scientific work of Luc Steels has always been highly transdisciplinary, using concepts, methods and techniques coming from such diverse fields as linguistics (mainly cognitive semantics and construction grammar), biology (particularly evolutionary theory) and physics (study of self-organizing systems) with computer science as overarching glue. But all this work has always been oriented towards a single focus: understand intelligence by building artificial systems that exhibit features ascribed to human and animal intelligence.

Contributions to knowledge engineering:

Based on his experience with the pioneering work at Schlumberger on geophysical expert systems, Luc Steels initiated in 1983 a group at the VUB Artificial Intelligence Laboratory he had just created in order to push forward the state of the art in knowledge engineering. The group had three types of activities.

First, it was constructing, often in collaboration with industry, operational expert systems designed to assist experts in various technical tasks.

Among the principle achievements were systems for the troubleshooting of digital circuit boards (with Alcatel Bell in Antwerp and lead by Johan Van Welkenhuysen)[1], the scheduling of railway traffic for the whole of Belgium (with the Belgian Railway Company NMBS lead by Kris Van Marcke[2], the monitoring of subway traffic (with Brussels subway company MIVB) lead by ?), the diagnosis of power stations[3], specifically nuclear power plants (with TRACTEBEL lead by ?).

These systems were developed on the innovative Symbolics LISP machines and featured always a core rule-based inference engine, extensive rule bases acquired from experts, domain models, and an interactive graphical interface that represents problems and their solution in ways familiar to domain experts.

The company Knowledge Technologies[4] (was created as a VUB AI lab spin-off in Brussels to channel these developments into practical industrial use. It operated from 1990 to 1995 with Kris Van Marcke as CEO.

Second, the group constructed various tools for knowledge engineering, the most important one being KRS (Knowledge Representation System), a frame-based object-oriented extension of LISP designed to support the knowledge representation aspects of expert systems. KRS was designed and implemented by Luc Steels and Kris Van Marcke[5] [6] in 1984 and steadily improved based on actual usage until ?.

Among the key novel features of KRS were facilities for meta-representation and computational reflection[7] developed by Pattie Maes[8] and for truth maintenance acquisition[9].

Third, Steels and his group initiated methodological innovations in knowledge engineering based on taking a `knowledge level' perspective on problem solving and expert knowledge, originally proposed by Allen Newell[10]. At the knowledge level expertise is composed into different aspects: ontologies, domain models,

problem solving strategies and concrete rules[11].

This knowledge level lead to strong interactions with the group of Bob Wielinga in Amsterdam and the Common Kads methodology, to so called second generation expert systems[12] that feature not only heuristic rules but principled domain models from which new rules could be derived[13] and the first attempts to construct large-scale ontologies that have lead to today's semantic web. To propagate the idea of knowledge-level expert system design and second generation expert systems, Steels organized various influential workshops, one in Portugal co-organized with John McDermott[14] and educational materials with televised courses for the EUROPACE[15] distance education network and for the Dutch Open University[16].

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[1] Reference to Johan's article

[2] Van Marcke, K. & Tubbax, B. SKAI: A Knowledge Based Environment for Scheduling Traction Equipment and Personnel. In proc. of Fourth International Conference on Computer Aided Design, Manufacture and Operation in Railway and other Mass Transit Systems, Comprail'94. September 7-9. Madrid.https://www.witpress.com/Secure/elibrary/papers/CR94/CR94019FU.pdf

[3] Steels, L. (1989). Diagnosis with a function-fault model. Applied Artificial Intelligence an International Journal, 3(2-3), 213 https://www.tandfonline.com/doi/abs/10.1080/08839518908949925

[4] Kris Van Marcke's CV - computers and education Dida*El)

[5] Steels, L., & Van Marcke, K. (1989). The KRS Tutorial. Technical Report: AI-memo 1989-16.

[6] Van Marcke, K. (1988). The KRS manual. Technical report: AI-Memo 88-05.

[7] Maes, P. (1988). Computational reflection. The Knowledge Engineering Review, 3(1), 1-19. doi:10.1017/S0269888900004355 https://www.cambridge.org/core/journals/knowledge-engineering-review/article/abs/computational-reflection/4060F80936C92A16536D554C0709C423

[8] https://www.media.mit.edu/people/pattie/overview/

[9] Van Marcke, K. (1986). A Consistency Maintenance System based on Forward Propagation of Proposition Denials. Technical Report: AI-memo 1986-1.[ref to paper by Kris VM PhD Thesis].

[10] A. Newell. The Knowledge Level. Artificial Intelligence, 18(1):87-127, 1982.

[11]Steels, L. (1990). Components of expertise. AI magazine, 11(2), 28-28.

https://ojs.aaai.org/index.php/aimagazine/article/view/831

[12] Steels, L. (1985). Second generation expert systems. Future generation computer systems, 1(4), 213-221. https://www.sciencedirect.com/science/article/abs/pii/0167739X8590010X

[13] van de Velde, W. (1986). Learning Heuristic Rules from Deep Reasoning. In: Machine Learning. The Kluwer International Series in Engineering and Computer Science, vol 12. Springer, Boston, MA.https://link.springer.com/chapter/10.1007/978-1-4613-2279-5_71 - citeas

[14] Steels, L., & Mcdermott, J. (1993). The knowledge level in expert systems. Conversations and Commentary. Boston: Academic Press.

[15] Weimer, W. A. (1994). EuroPACE: The Lessons Learned So Far. Industry and Higher Education, 8(1), 19–28. https://doi.org/10.1177/095042229400800103

[16] Steels, L. (1992) Kennissystemen. Amsterdam: Addison-Wesley.