VR-43-XN. ISSN Volume, 17, number 3 June, Wiebe van der Hoek (member) Radboud Winkels (section editor)

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1 ISSN Volume, 17, number 3 June, 2000 BOARD BNVKI: Joost Kok (chair) Rineke Verbrugge (member) Wiebe van der Hoek (member) Yao-Hua Tan (member) Eric Postma (member) Luc DeHaspe (member) Walter Daelemans (member) Gert-Jan Beijer (member) EDITORIAL BOARD: Eric Postma (Editor in Chief) Jaap van den Herik Bart de Boer Shan-Hwei Nienhuys-Cheng Cees Witteveen Antal van den Bosch (section editor) Edwin de Jong (editor Belgium) Richard Starmans (section editor) Radboud Winkels (section editor) BNVKI-SECRETARY: EDITORIAL ADDRESS BNVKI newsletter Rineke Verbrugge Joke Hellemons Rijksuniversiteit Groningen Universiteit Maastricht Cognitive Science and Engineering FdAW, Department of Computer Science Grote Kruisstraat 2/1 P.O.Box 616, 6200 MD Maastricht 9712 TS Groningen Telephone: / Fax: unimaas.nl/~bnvki Segmentation Unit Character Isolator Character Recognizer VR-43-XN Syntactical Analyzer

2 KEEP IT SIMPLE, STUPID EDITOR-IN-CHIEF Trivial ideas can have an enormous impact when applied in a new context. Consider the case of a Dutch teacher who had a trivial idea in the context of the Internet. He created a webpage called the Start Pagina (Starting Page) containing a list of hyperlinks to often-wanted sites, such as the telephone book, travel information agencies and so forth. Despite its simplicity, the Start Pagina is one of the most popular sites in the Netherlands. Both newcomers and experienced users of the web consult the Start Pagina for finding information amidst the chaos of the Internet. The interested reader may take a look at Statistics on the popularity of the site (and other sites) can be found at the NetStat page Recently, a large Dutch publishing organisation bought the Start Pagina for several tens of millions of guilders. The interesting aspect of the success of the Start Pagina is that it is based on an extremely simple idea. The power of simplicity is often emphasised in the context of (scientific) writing. KISS is a well-known acronym that refers to the phrase Keep It Simple, Stupid" and indicates that simple formulations are to be preferred over difficult ones. The KISS principle applies to science and technology as well. Whereas, in modelling and theorising Occham s Razor should be applied to keep the complexity of the model or theory as simple as possible, in the development of innovative ideas the KISS principle serves as an apt guideline. AI researchers think very hard about the problem of dealing with the information overload on the Internet. Their answers are sophisticated and potentially powerful. However, as of yet their impact on daily Internet usage is very small when compared to the impact of the Start Pagina or similar sites. Applying the KISS principle to Internet research facilitates a matching of technology to the needs of Internet users. Trivial ideas are not always as trivial to implement as the Start Pagina. Therefore, they may still pose technological challenges for applied AI research. The twelfth Belgium-Netherlands Conference on Artificial Intelligence (BNAIC 2000) will be held on November 1 and 2 (see the Call for Papers and Demonstrations on page 32 of this newsletter). As last year, there is a special submission class for applications and demonstrations. Simple and trivial ideas that are turned into successful AI applications can be submitted in this class. In case your idea is not in that stage yet, you may consider to write a paper about it and submit it to the regular-papers or compressed-contributions class. The deadline for submissions is May 31, For more information on the BNAIC 2000 or other AI events, your Start Pagina is Cover photograph: Longneck (Langnek) in the Efteling near Tilburg, the location of the BNAIC See the Call for Papers on page 32 of this newsletter. BNVKI newsletter 30

3 TABLE OF CONTENTS Keep it Simple, Stupid (Editor-in-Chief)...30 Table of Contents...31 BNVKI-Board News (Joost Kok)...32 Call for Papers BNAIC 2000 (Antal van den Bosch)...32 Reports BNAIC 99 (Gertjan Beijer)...33 A View on Ph.D.s (Jaap van den Herik)...35 Thesis Reports...35 Classification using Decisions Trees and Neural Nets (Wim Pijls)...35 Local Learning Techniques for Modelling, Prediction and Control (Yang Quan Chen)...37 SIKS (Richard Starmans)...39 Section Knowledge Systems in Law and Computer Science (Radboud Winkels)...39 Verslag Wetenschappelijk deel Jurix Bijeenkomst 9 februari 2000, UvA (Luuk Mathijssen)...39 Jurix and Public Counter 2000 (Ronald Leenes)...41 Knowledge Management (in een Juridische Context) (Martin Apistola)...43 The Evolution of Language Conference (Bart de Boer)...45 Section AI in Business...46 Towards an Online Distribution Structure (Kees Jonkheer)...46 Call for Participation...49 Playstation meets organism: emulation in a cultural context...49 Conferences, symposia, Workshops addresses, Board Members/ Editors BNVKI newsletter / How to become a member?/ Submissions...51 Advertisement...52 The BNVKI is sponsored by AHOLD and by BOLESIAN In 2000, the publication of the BNVKI newsletter is also supported by the Division of Computer Science Research in the Netherlands (previously called SION, now ACI) BNVKI newsletter 31

4 BNVKI BOARD NEWS Joost Kok Chairman BNVKI Last week I visited the ACM Symposium on Applied Computing in Como, Italy. Although the weather and the food were nice, the town Como is a bit boring at the end of the winter. The conference was held in a beautiful villa at lake Como. Last year, a G7-meeting was held in the same villa. The conference was interesting and contained a large track on evolutionary algorithms. In addition, there were discussions on Computing Curricula 2001, organised by The Joint Task Force on Computing Curricula of the IEEE Computer Society and the Association for Computing Machinery. The last curriculum was proposed in 1991 and every ten years a new curriculum is put together. Citing the Computing Curricula website: The Joint IEEE Computer Society/ACM Task Force on the Year 2001 Model Curricula for Computing (CC-2001) was formed to review the 1991 curricula and develop a revised and enhanced version for the Year 2001 that addresses developments in computing technologies in the past decade and will sustain through the next decade. The draft version of the Curriculum proposal contains a section on Intelligent Systems, including topics such as Fundamental issues in intelligent systems, Search and optimisation methods, Knowledge representation and reasoning, Learning, Agents, Computer vision, Natural language processing, Pattern recognition, Advanced machine learning, Robotics, Knowledge-based systems, Neural networks, and Genetic algorithms. The draft report can be found at indexe.html. It is still possible to give comments on the proposal. Contact addresses can be found on the web site. Finally, I would like to encourage our members to submit contributions reporting or demonstrating on their research progress to the BNAIC 2000, to be held at the Efteling in Tilburg. A beautiful location to listen to research, talk about research, and to dream of fairytales. CALL FOR PAPERS AND DEMONSTRATIONS Antal van den Bosch TWELFTH BELGIUM-NETHERLANDS CONFERENCE ON ARTIFICIAL INTELLIGENCE November 1-2, 2000 De Efteling, Kaatsheuvel The twelfth Belgium-Netherlands Conference on Artificial Intelligence (BNAIC 00) is organised by the Infolab and the Induction of Linguistic Knowledge (ILK) research group at Tilburg University. The School for Information and Knowledge Systems (SIKS) has given its auspices to the conference. BNAIC is the annual conference of the BNVKI/AIABN, the Belgian-Dutch Association for Artificial Intelligence, and will be held at the Efteling, Kaatsheuvel, on November the 1 st and 2 nd, The conference aims at presenting an overview of state-of-the artresearch in artificial intelligence in Belgium and The Netherlands, and to further the interaction between researchers in both countries. We have distinguished three classes of submission, each with its own refereeing procedure. The classes are A for regular paper submissions, B for compressed contributions, and C for demonstrations and applications. Class A. The essence of every conference is the presentation of new and original work. This class accommodates the submission of such work. Submitted papers should not exceed a length of 8 pages. Class B. Work that has been reviewed and subsequently published elsewhere can be submitted as a compressed contribution. Authors are invited to submit the officially published version (without BNVKI newsletter 32

5 restrictions on the number of pages) together with a one or two-page abstract (to appear in the BNAIC proceedings). Class C. Proposals for demonstrations will be evaluated based on submitted demonstration summaries (in English) stating the following: the purpose of the system to be demonstrated, its user groups, the organisation or project for which it is developed, the developers, and the technology used. In addition, the system requirements and the duration (not exceeding 30 minutes) should be mentioned. Especially researchers from industry are encouraged to submit their successful applications to this class. The maximum size of demonstration summaries is 2 pages. For all classes, possible topics of submissions include, but are not limited to: multi-agent systems, neural networks, knowledge-based systems, natural language processing, games, search, machine learning, robotics, knowledge representation, knowledge management, ontologies, logic programming, optimisation, intelligent agents, and evolutionary algorithms. CLIN 2000 Immediately after BNAIC 00, on November 3, 2000, the eleventh Computational Linguistics in the Netherlands meeting (CLIN) will be held in Tilburg. CLIN is organised by a team headed by Walter Daelemans (UIA Antwerpen, KUB). The overlap in research interests of the BNVKI and CLIN communities will be strengthened furthermore by an invited speaker (Yorick Wilks) and a special BNAIC session on computational linguistics / language technology. SUBMISSIONS Papers and demonstration summaries are to be submitted as postscript file and sent by to Alice Kloosterhuis ). A LaTeX style and a MS Word template will be made available through the BNAIC 00 Web site (see below). All submissions should be accompanied by a message stating the submission class (A, B, or C). Proper receipt of submissions will be acknowledged by . The deadline for submissions is May 31, Accepted papers will be published in the Proceedings. Authors keep the copyright of their submissions. INVITED SPEAKERS Yoav Shoham (Stanford University) Yorick Wilks (University of Sheffield) PROGRAMME COMMITTEE Antal van den Bosch (co-chair, KUB) Hans Weigand (co-chair, KUB) CONTACT INFORMATION Alice Kloosterhuis (KUB) Infolab, Tilburg University P.O. Box LE Tilburg The Netherlands Telephone: Fax: IMPORTANT DATES Deadline for submissions: May 31, 2000 Notification of acceptance: July 15, 2000 BNAIC 00 HOMEPAGE For all up-to-date information on BNAIC, please visit (and bookmark) REPORTS BNAIC 99 DEMONSTRATIONS Report by Gertjan Beijer Bolesian I hereby present my postponed summary of the session Demonstrations 1 presented during the BNAIC 1999 (!), which was held in Maastricht last year. All three contributions consisted of a short presentation on the backgrounds followed by a demonstration. IGUANA: A WEB CRAWLER AIMED AT CREATING A DOMAIN SPECIFIC WEB ENGINE R.. Ekkelenkamp TNO-TPD Mr. Van Breukelen presented this session s lecture and demo instead of the author is R. Ekkelenkamp. BNVKI newsletter 33

6 Iguana is a webcrawler aimed at crawling domain specific websites. In order to make this possible the program contains a number of text categorisation algorithms to perform its main task. Amongst them are vector-space index search and fuzzy noun phrase index-based search. Iguana, which was written in Java, has proven its value in a number of applications, amongst others for the Dutch Milleniumplatform and a community of a number of European cities. That is why its core engine contains features for automatic language understanding and disposes a user interface in four languages. The actual demo showed a number of Iguana s features like searching on phrases, finding (translated) German webpages, sorting on suitability and the search similar function. Text mining is a hot topic in academic and industrial environments. It is still hard to prove that a certain approach is the best regarding the characteristics of the data, such as distribution of items, distribution of categories, subject of text etc etc. It is still hard to even simply compare all different approaches! At the end of the demonstration questions were asked about the measures for calculating similarity and distance between documents. Unfortunately there was not much time left. NEURAL VISION 2.0 Michiel van Weezel CWI The winner of the Best Demonstration Award was Michiel van Weezel with his lecture about Neural Vision 2.0. This demonstration was chosen to be the best because it most clearly demonstrated how advances in science can almost be put into everyday practice. Neural Vision is a software package intended for exploratory data analysis using projection techniques to display multidimensional data on two-dimensional screens. The techniques used come from neural network theory and statistics. With Neural vision one can visually discover clusters of data or relationships between data-elements before deciding to use advanced (i.e., costly and time-consuming) analysis techniques to (hopefully) discover new information or knowledge. The sheets presented some backgrounds on the application such as the partners who worked on the project (RUL, CWI, RWS) and a short history (it all started in 1996 ). Techniques applied are Sanger s Neural-Network based Principal Components Analysis (PCA) projection technique yielding the maximal amount of variance in a small number of dimensions, Independent Components Analysis (ICA) as a second projection technique that goes further than Principal Components Analysis (it tries to find dimensions that are statistically independent) and finally a Frequency Sensitive Competitive Neural Network which divides input space in clusters minimising within-cluster variance. The application was written in Borland C++ Builder 3.0. The demo showed the application together with two data sets. One data set was used for the classification of Iris flowers. Training and testing was demonstrated for this 4 dimensional data set. The second data set represented various types of data regarding roads, amongst others its use and maintenance costs. This 11 dimensional data set was used for a number of visual explorations with colourings, polygonplots and additional statistics. AGENT BASED CUSTOMER SERVICE BNVKI newsletter 34

7 Jan Heemskerk KPN research The third and final session of the Demonstrations 1 sessions was chosen second best in the Best Demonstration Competition for it s clarity and was presented by Jan Heemskerk of KPN Research. His short and professional presentation contained some background of the company he worked for which provides work for 500 researchers! Jan is primarily focussed on agent-based technology. Within KPN he is responsible for the agent-based research program which consists of 12 projects. The demonstration describes one of these projects. The business environment in which the solutions should work is today s customer contact infrastructure. It depends on a technical system environment in which agent building blocks will finally get their place. The demonstration showed some nice features of the highly-distributed solution by showing applications that are investigated and developed for interpreting customer questions, personalising information supply and serving as a onestop shop for the customer. Agent characteristics like autonomy, selfknowledge and knowledge about its environment emerge and are applied A generic architecture, based on the FIPAarchitecture, is used in which messages from the user propagate through the system via an interface agent reaching different agents in the background. Natural language is translated to ACL and an ontology is used to disambiguate phrases. Each agent has its own problem solving task such as translating natural language to meaningful phrases, planning activities or determining the right answer to a posed question. The agents applications are developed in Java and Jess (Java expert system shell). A new development area entering KPN research is machine learning in combination with agent technology. A VIEW ON Ph.D.s Jaap van den Herik IKAT, Universiteit Maastricht Within the period , the year 2000 will not be the most productive year of all Ph.D. students. It seems that the millenium transition has encouraged many people to finish their Ph.D. research still in the previous century. So be it. A new generation starts slowly and I assume that we have to wait until 2002 before we are back on the old level of approximately 25 Ph.D. students a year. This time we are happy to announce four defences. They are all four from different areas, namely logic, software engineering, modelling, and knowledge retrieval. Niesink and Van der Pol are SIKSpromovendi. The Ph.D. theses of Kamps and Hulstijn are results of large projects which are carried out at the universities mentioned. The Editor congratulates three successful doctores and wishes the last one (Van der Pol) much strength with his defence. Moreover, we have two Ph.D. reviews. The first review is written by Wim Pijls and gives an account of the Ph.D. thesis by Rob Potharst (EUR) titled: Classification using Decision Trees and Neural Nets. The second review is written by Yang Quan Chen (and introduced by Edwin de Jong) and discusses Gianluca Bontempi s thesis Local Learning Techniques for Modelling, Prediction and Control. We look forward to other Ph.D. reviews for the next issue. New announcements on Ph.D. defences are also welcome. J. Kamps (March 10, 2000) A Logical Approach to Computational Theory Building. Universiteit van Amsterdam. Promotor: Prof.dr. W.E. Saris, copromotor: dr. J.M.F. Masuch. F. Niesink (March 28, 2000) Perspectives on Improving Software Maintenance. Vrije Universiteit Amsterdam. Promotor: Prof.dr. J.C. van Vliet. J. Hulstijn (April 7, 2000) Dialogue Models for Inquiry and Transaction. Universtiteit Twente. Promotor: Prof. dr.ir. A. Nijholt. BNVKI newsletter 35

8 R.W. van der Pol (September 14, 2000) Knowledge-based Query Formulation in Information Retrieval. Universiteit Maastricht. Promotores: Prof.dr. H.J. van den Herik, Prof.dr.ir. A. Hasman, and Prof.dr.ir. J.L.G. Dietz CLASSIFICATION USING DECISION TREES AND NEURAL NETS Proefschrift van R. Potharst Erasmus Universiteit Rotterdam Bespreking door W. Pijls In juni 1999 promoveerde Rob Potharst aan de Erasmus Universiteit Rotterdam op een proefschrift getiteld: "Classification using Decision Trees and Neural Nets". Promotor was Prof. A. de Bruin met als co-promotor en belangrijkste begeleider Dr. J.C. Bioch. Zoals de titel reeds aangeeft, is classificatie het centrale thema in dit proefschrift. Onder classificatie wordt verstaan: het onderbrengen van objecten, op basis van de attributen van dit object, in één van een aantal vooraf gedefinieerde klassen. Twee bekende classificatietechnieken, te weten beslisbomen en neurale netwerken, worden bestudeerd, waarbij de eerstgenoemde techniek duidelijk meer aandacht krijgt dan de tweede. De classificatiesystemen zijn zogenaamde lerende systemen, d.w.z. ze classificeren op basis van indelingen in het verleden. De systemen krijgen een zogenaamde trainingsset aangeboden, een set van objecten gedefinieerd in termen van een aantal attributen. AFBEELDING VAN BESLISBOMEN OP NEURALE NETWERKEN Beslisbomen spelen, zoals gezegd, een belangrijke rol in dit proefschrift. De hoofdstukken 2, 3 en 4 handelen over het construeren (ook wel aangeduid als het induceren ) van beslisbomen op basis van een gegeven dataset (de trainingsset). De beslisboom wordt gebruikt om de elementen van een andere set (de testset) te classificeren. Dit onderwerp behoort tot het gebied van "supervised learning". Ofschoon beslisbomen reeds veel aandacht gekregen hebben in de literatuur, weet de auteur in genoemde hoofdstukken enkele belangwekkende nieuwe resultaten te presenteren. De hoofdstukken 5 en 6 hebben een andere invalshoek. In hoofdstuk 5 worden Bayesiaanse neurale netwerken besproken. In hoofdstuk 6 wordt een verband gelegd tussen beslisbomen en neurale netwerken. MONOTONIE Na een inleidend eerste hoofdstuk, begint in het omvangrijke tweede hoofdstuk de bespreking van beslisbomen. Een belangrijk begrip in dit hoofdstuk is het begrip monotone dataset. Stel dat alle attributen (inclusief de klasse) ordinale waarden hebben. Een dataset heet monotoon, indien objecten met hogere attribuutwaarden ook tot een hogere (althans niet lagere) klasse behoren. Een classificatiesysteem heet monotoon, indien objecten met hogere attribuutwaarden ook hoger geclassificeerd worden. De meest bekende algoritmen voor het induceren van beslisbomen zijn ID3, C4.5 en CART. Deze hebben alle het bezwaar dat ze geen monotone beslisboom induceren, d.w.z. de beslisboom geïnduceerd door een monotone trainingsset garandeert geen monotone classificatie voor uitbreidingen van de trainingsset (bij voorbeeld een uitbreiding met de testset). In hoofdstuk 2 worden enkele nieuwe algoritmen gepresenteerd die wél monotone beslisbomen induceren. Dit alles wordt op een gedegen theoretische manier onderbouwd. Het nadeel van de nieuwe algoritmen is wel dat de beslisbomen groter zijn en dat het induceren van de bomen relatief duur is. Verder wordt in dit hoofdstuk ook nog een algoritme beschreven, dat nagaat of een gegeven beslisboom wel of niet monotoon BNVKI newsletter 36

9 classificeert. Alle algoritmen zijn uitvoerig experimenteel getest. QUASIMONOTONIE Ofschoon men monotone datasets niet zeldzaam kan noemen, is het construeren van een monotoon classificatiesysteem toch een zware eis. Daarom wordt het begrip quasimonotonie geïntroduceerd, een uitbreiding van het begrip quasipositiviteit, eerder ingevoerd door Makino voor classificatie in het geval van slechts twee klassen. In hoofdstuk 3 wordt een algoritme geformuleerd, dat een zogeheten quasimonotone beslisboom induceert. Ofschoon dergelijke bomen ook geen monotonie kunnen garanderen op uitbreidingen van de trainingsset, vertonen ze wel de zwakkere quasimonotonieeigenschap. Ook in dit hoofdstuk wordt alle theorie in formeel opzicht uiterst gedegen behandeld, inclusief correctheidsbewijzen. Tevens wordt het geheel met experimenten toegelicht. BIVARIATE BESLISBOMEN In hoofdstuk 4 worden bivariate beslisbomen besproken. De vorige hoofdstukken beschouwden slechts univariate bomen. Deze voeren per knoop een test op slechts één attribuut uit. Bivariate bomen kunnen in een knoop ook een test met twee variabelen uitvoeren. De bekendste in deze categorie zijn CART en OC1. Twee nieuwe algoritmen, BIT1 en BIT2 geheten, worden gepresenteerd. Evenals de twee oudere algoritmen testen de beide nieuwe slechts lineaire combinaties van attribuutwaarden. De rekentijd nodig voor het induceren van dergelijke beslisbomen is nauwelijks ongunstiger dan in het geval van univariate bomen. Overigens speelt in dit hoofdstuk het verschijnsel monotonie geen rol meer. BAYESIAANSE NEURALE NETWERKEN De laatste twee hoofdstukken richten zich op neurale netwerken als instrument voor classificatie. In hoofdstuk 5 wordt een zogeheten Bayesiaanse benadering voor het bouwen van classificerende netwerken besproken. Deze benadering zou gunstig zijn voor het voorkomen van overfitting. In het kader van zijn onderzoek heeft de auteur een volledig netwerk gebaseerd op de Bayesiaanse aanpak geïmplementeerd. Op zich een taak waarmee je heel wat overhoop haalt. Als apriori-verdelingen worden standaard-normale verdelingen gekozen. Voor de berekening van de aposteriori verdelingen moeten enkele numerieke integralen opgelost worden. Hiervoor wordt de recent geïntroduceerde Markov Chain Monte Carlo (MCMC) methode gebruikt. AFBEELDING VAN BESLISBOOM OP GEWICHTSPARAMETERS Het laatste hoofdstuk handelt indirect toch weer over beslisbomen. Volgens een door Park ontworpen methode kan men de beslisboom afbeelden op de gewichtsparameters van de onderste laag van een neuraal netwerk met één hidden layer. De auteur toont aan dat, in tegenstelling tot wat Park beweert, de classificatie van het neuraal netwerk niet geheel overeenkomt met die van de beslisboom. Wel zijn de beide classificaties bij benadering identiek. De kwaliteit van de benadering is instelbaar via parameters. De afbeelding van een beslissingsboom op een neuraal netwerk is mogelijk relevant voor het ontwerpen van een geschikte architectuur van het neurale netwerk. VIJFTIG-PLUSSER Het proefschrift is fraai uitgevoerd en maakt een zeer leesbare indruk. Zoals boven reeds opgemerkt wordt voor de theorie steeds een gedegen formele BNVKI newsletter 37

10 verantwoording gegeven. Het overwegend theoretische karakter van het proefschrift heeft de auteur er niet van weerhouden uitvoerige experimentele uitkomsten te vermelden. De promovendus is zelf verbaasd over zijn eigen productiviteit. Hij is ruim vijf jaar geleden als vijftigplusser de onderzoekswereld binnengestapt. Het voorwoord opent met de volgende zin: If a fortune-teller would have told me You will defend your PhD thesis before your 55 th birthday five years ago, I would have ridiculed her'. Dr. Potharst blijkt ongelijk gehad te hebben en geeft dit ruiterlijk toe. Op overtuigende wijze heeft hij aangetoond vijf jaar geleden een juiste stap gezet te hebben. LOCAL LEARNING TECHNIQUES FOR MODELLING, PREDICTION AND CONTROL Thesis by Gianluca Bontempi Iridia ULB INTRODUCTION Edwin de jong, VUB Gianluca Bontempi is researcher at the Philips Research Laboratories in Eindhoven, The Netherlands. From 1997 to March 2000, he was a Marie Curie fellow at the IRIDIA lab of the Universite Libre de Bruxelles, headed by Hugues Bersini and Marco Dorigo. On December , he defended his Ph.D. in Applied Sciences, for which he received la plus grande distinction (grootste onderscheiding). His paper with Mauro Birattari received the Best Paper Award at the previous BNAIC. Dr. Yangquan Chen, senior engineer at Seagate R&D in Singapore and expert on learning control, provided the following review of Gianluca s thesis. Thesis review by Yang Quan Chen Singapore Science Park Design Centre Seagate Technology International "Learning" is a quite general concept in engineering and in science. It is more like an "elephant" surrounded by a group of "blind men". One has to be brave enough to choose a Ph.D. thesis title containing the word "learning". I am however excited to read Gianluca Bontempi's Ph.D. thesis and would like to say that the framework of "local learning", or more specifically, "lazy learning", advocated in Gianluca's work is on the right track! Yes, we have to be "local" and we have to be "lazy" because in most of the cases we have no choice! Actually, nothing is wrong with the "blind men" just mentioned. They have no choice because of their visual sensor failure and they have to approach the "elephant identification problem" by local information only. This may be a passive example. Let us consider a non-passive one. Suppose you want to cross the road, you do not need the traffic information of this road 50 miles away. Your decision to across the road is based on the local information. In our real world, the loop of "sensing-decision-control" is largely "local". THE WHOLE LOOP In AI research, people are now facing the so-called complex system which is asked to perform a complex task in a complex environment - "CCC". Global thinking is restricted by our socio-technological limits such as man and computational powers etc. Most naturally, people may be asked the question "why global when a cheap local scheme is good enough?" Another issue in AI research is the incomplete or broken loop of "sensing-decision-control". We generally have two objectives for our activities: "realise the world" and (then) "change the world". It seems that most of the AI research objectives are just to "realise the world", i.e., only the "sensingdecision" part. Gianluca's work under the label of "Supervised Learning in AI", however, touched the whole loop of "sensing-decision-control". This distinguishes his work, which includes modelling, prediction and control using BNVKI newsletter 38

11 local learning techniques, from other AI research. CONTRIBUTION The major contribution of this thesis is first in this thesis itself. It is well organised with a systematic presentation of local learning techniques. The carefully selected appendices made this thesis self-contained and yet the main body of the thesis can lead the readers to the cutting edge of this research area. It has been actually a well prepared book, to my point of view, ready for publication as a research monograph. Technically, the main contributions of the thesis are three-fold: (1) supervised learning methods; (2) modelling and timeseries prediction; and (3) non-linear dynamic system control. The thesis presents a set of original local modelling techniques and their applications to a number of experimental problems in regression, time series prediction and adaptive control. It has been successfully shown that the local paradigm is not only a powerful idea to design learning algorithms, but also a fruitful way of reasoning in different domains of applications. In local learning methods, the key problem is the bandwidth selection. Gianluca proposed a datadriven technique for bandwidth selection which, for each query point, (i) generates a number of local model candidates with different numbers of neighbours, (ii) validates them via cross-validation and finally (iii) selects or combines the most promising ones. The set of models taken into consideration are local linear models centred in the query point and fitted with an increasing number of neighbours. A spatial sequence of neighbours that are incrementally considered as making part of the local model centred in the query point are fed into the proposed spatial recursive least-squares algorithm. Once the candidate models have been generated, the PRESS statistic, an efficient local cross-validation procedure, is used to assess the generalisation ability of each of them. This validation does not involve any significant computational overload, since the PRESS statistic uses partial results returned by the recursive least squares algorithm. Once the models have been generated and assessed, a model selection procedure is required to choose the best one. Both a competitive and a co-operative approach were explored in the thesis. The competitive approach is based on a winner-takes-all strategy while the cooperative strategy is based on the theory of the combination of estimators. Clearly, this kind of local learning algorithm is lazy due to the fact that it defers the whole learning procedure to the moment when a prediction is required. This is referred to as a Lazy Learning (LL) method. From a number of supervised learning benchmarks presented in this thesis, we can see that the Lazy Learning algorithm is able to compete with global and local state-of-the-art approaches. It is worth repeating here that the Lazy Learning method also took part in the Third International Erudit competition on supervised learning where it was awarded as a runner-up among 21 participants. LAZY LEARNING The thesis contributes to long-term timeseries prediction based on the iteration of a one-step-ahead predictor. Gianluca advanced a Lazy Learning approach with local validation and bandwidth selection which is performed on the basis of an iterated formulation of the PRESS crossvalidation criterion. This is an efficient way for assessing the generalisation performance of a multi-step predictor on a horizon longer than a single step, which is totally new according to existing local modelling literature. The effectiveness of the proposed LL prediction method has been demonstrated by the successful results in the prediction of the chaotic Mackey Glass time series, in the prediction of two time series from the Santa Fe competition, as well as in the International Competition on Time Series organised by the International Workshop on Advanced Black-box techniques for non-linear modelling in Leuven, Belgium. In the competition, two prediction results obtained by using the proposed Lazy Learning algorithm ranked second and fourth, respectively. NON LINEAIR CONTROL As for the contribution to non-linear control, Gianluca investigated three major BNVKI newsletter 39

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