THE EMPEROR S NEW THEORY Editor-in-Chief Consciousness is regaining interest in the fields of natural sciences. For a long time, thinking (or even talking) about consciousness was regarded as unworthy of natural scientists. It was better to leave the subject of consciousness to philosophers and psychologists who are used to think about things that are hard to grasp. But now, all this has changed due to pioneers such as Francis Crick and Christof Koch who took the bold step to put the study of consciousness on the agenda of neurocomputational research. Nowadays, theorizing about consciousness is very common in the natural sciences and has even entered the bastion of physics. Sir Roger Penrose, renowned amongst others for his work in collaboration with Stephan Hawking on the origins and evolution of the universe, has given consciousness a central role in his theorizing about physics and the observing mind. In a recent lecture in Maastricht he talked about time in physics and psychology. His exposé on the fundamentals of physics revealed his great knowledge and erudition. With a nonchalance characteristic of a great scientist he presented a very clear story hardly affected by his chaotic handling of hand-drawn overhead sheets. His lecture focused on the gap between the two most powerful theories in physics: quantum theory and classical theory (including relativity theory). Although both theories have great explanatory power in their own domains (i.e., the very small versus the very large, respectively) present-day physics has not succeeded in merging them into a single theory. Penrose noted that in both theories time is symmetrical whereas in our experience time is perceived as asymmetrical. The theory linking quantum and classical theory should therefore account for the arrow of time as perceived by conscious agents. After discussing his hypothesis on how the asymmetry of time arises, Penrose outlined his hypothesis on the origin of consciousness. According to this hypothesis, consciousness resides at the level of synapses where quantum effects are present and are affecting the mental processes at the larger scale of neural networks. The nonlocality of quantum mechanics, now an active subject of study in the context of quantum computing (see also Edwin de Jong s report on the CASSYS 99 conference on page 136 of this newsletter) could arise from these synaptic quantum effects. In Penrose s view, consciousness emerges from the quantum level and affects the neural processes underlying mental processes. The heroic efforts by Penrose and others have great appeal as they address one of the oldest riddles of science. Consciousness is central to ourselves, our culture, and our language; it deserves to be taken seriously as a subject of study. However, the route followed by Penrose seems to be a futile one. Explaining consciousness in terms of the gap between quantum and classical theory is in some sense like reducing visual perception in terms of the gap between fotons and nerve impulses. The point is that consciousness is a not a physical concept. Conscious experience probably arose as an evolutionary adaptation to the Paleolithic, some 200,000 years ago. Since then, it probably underwent many changes due to changing cultural (and environmental) demands. In this view the basis of conscious experience should primarily be sought in the complexity of the environment (i.e., other conscious agents), rather than in the complexity of the brain alone. How do these reflections on consciousness relate to artificial intelligence? Currently, studies of (artificial) consciousness are very rare if present at all, but I expect them to (re-)emerge soon. The emphasis on autonomous agents and robots is bound to lead to a reconsideration of consciousness in artificial-intelligent systems. Although I consider it much too early to put forward any sensible question related to consciousness, I nevertheless encourage our members to start thinking along these lines. After all, that is what science is all about. Cover illustration: the BNAIC 99 logo (designed by Hans Hoornstra) The agenda and financial overview for the BNVKI Board meeting (to be held at the BNAIC on Thursday November 4 from 12.35 to 13.45 hours) can be found via the BNVKI website http://www.cs.unimaas.nl/~bnvki/ BNVKI newsletter 124
TABLE OF CONTENTS The Emperor s New Theory (Editor in Chief)...124 Table of Contents...125 BNVKI-Board News (Joost Kok)...126 BNAIC 99 Update (Floris Wiesman and Eric Postma)...126 BNAIC 99 Programme...127 Logic and Gambling (Jaap van den Herik)...130 Book Review: Function, Selection, and Innateness (Bart de Boer)...130 The Sixteenth International Joint Conference on Artificial Intelligence (Edwin de Jong)...132 Casys 99: Computing Anticipatory Systems (Edwin de Jong)...136 Nouvelle AI (Rens Kortmann)...138 Gecco 99 Student Workshop (William Langdon)...143 Verslag van de Landelijke KION-dag (Ida Sprinkhuizen Kuyper)...144 Section Knowledge Systems in Law and Computer Science (Radboud Winkels)...145 Boekbesprekingen De Bouw van Juridische Kennissystemen (Bart Verheij)...145 Hercules of Karneades Hard Cases in Recht en Rechtsinformatica (Kees de Vey Mestdagh)...149 Section Computational Linguistics (Antal van den Bosch)...151 BNVKI Tutorials on AI and Language Processing...151 Section SIKS (Richard Starmans)...152 Call for Papers...154 LOFT 4 (Wiebe van der Hoek)...154 Call for Participation...155 The Jurix 99 Conference...155 Conferences, Symposia, Workshops...156 Email-adresses, Board Members/ Editors BNVKI newsletter/ How to become a member?/ Submission...157 Back Issues/ Change of Address...157 The BNVKI is sponsored by AHOLD and by BOLESIAN The photographs on pages 143 and 144 are provided by William Langdon BNVKI newsletter 125
In 1999, 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 126
BNVKI-BOARD NEWS Joost Kok Chairman BNVKI One of the nice things of AI research is that it generates many new technological ideas. Some of these ideas are surprising. The dial-a-coke concept from Finland is a good example. The concept is explained in the following press release by NOKIA. Singapore (June 5, 1998) Nokia today announced the Dial A Coke concept a new application for the Nokia Card Phone designed to extend greater convenience to the consumer. The innovative application displayed at CommunicAsia 98 integrates the wireless data capabilities of a Nokia Card Phone with a Coca-Cola vending machine, and allows consumers to purchase a drink simply by using their mobile phone. To purchase a beverage through Dial A Coke, customers simply use their mobile phone to dial a phone number indicated on the vending machine. The drink pops out automatically and the purchase is confirmed through a short message to the customer s mobile phone bill. The Dial A Coke concept works with all mobile phones. It does not take too much imagination to elaborate on this concept. Whatever the end result of this elaboration may be, it is clear that it involves a convergence of mobile-phone, television, credit-card and computer technologies. Intelligent technology is certainly needed for integrating these different technologies. So, interesting challenges lie ahead for Artificial Intelligence. NOKIA s innovation could serve a much better cause than satisfying the thirst of rich people. Imagine that we install Dial a cup of Rice machines in poor countries which can be called from any country in the world. Then, we can offer direct help to hungry people in third-world countries. Fortunately, we do not have to await these future developments to offer help. If you want to do something nice today, please click on the button on http://www.thehungersite.com/ and donate some rice to a hungry person. After you have done this, you can dial 043 3883477, the BNAIC registration desk and register for the conference in case you have not done so already. As any reader can imagine I look forward to the demonstrations of our BNAIC in Vaeshartelt, Maastricht. See you at the Conference. BNAIC UPDATE Floris Wiesman and Eric Postma www.cs.unimaas.nl/~bnvki/bnaic99 The BNAIC 99 is rapidly approaching and we are busy preparing the conference. In the previous newsletter we reported on our main sponsor Lucent Technologies and that there will be three invited lectures by Tom Mitchell, Edward Rietman, and Jonathan Schaeffer. Now we are happy to announce a fourth, albeit short, invited lecture. Professor Frans Groen will present an introduction to the Robot Soccer demonstration entitled Robot Soccer: Game or Science? The RoboCup soccer demonstration is sponsored by Tryllian, a young start-up company specializing in mobile software agents. The demonstration takes place in one of the smaller conference rooms. To allow all participants to have a close look at the robots and their actions, the demonstration will be repeated several times. The demonstration and the happy hour (sponsored by Bolesian) take place at the same time so that participants can visit the demonstration in small groups. Below, we give a global overview of the BNAIC programme. Full details can be found on the following pages. We look forward to seeing you in Maastricht in the first week of November! A GLOBAL OVERVIEW TUESDAY NOVEMBER 2 17.00 18.00 hours Early bird reception at the Maastricht town hall WEDNESDAY NOVEMBER 3 09.45 10.20 hours opening 10.20 11.20 hours Tom Mitchell BNVKI newsletter 127
11.20 11.35 hours coffee break 11.35 12.25 hours parallel sessions 12.35 13.30 hours lunch 13.30 14.45 hours parallel sessions 14.45 15.00 hours coffee break 15.00 16.15 hours parallel sessions 16.15 16.30 hours coffee break 16.30 17.30 hours Edward Rietman (Lucent) 17.30 18.00 hours Frans Groen 18.00 20.00 hours RoboCup demo (Tryllian) Happy Hour (Bolesian) 20.00 23.00 hours Conference Dinner THURSDAY NOVEMBER 4 09.00 09.50 hours parallel sessions 09.50 10.05 hours Jonathan Schaeffer 11.05 11.20 hours coffee break 11.20 12.35 hours parallel sessions 12.35 13.45 hours lunch and BNVKI Board meeting 13.45 15.00 hours parallel sessions 15.00 15.15 hours coffee break 15.15 16.30 hours parallel sessions 16.30 17.00 hours awards and closing BNVKI newsletter 128
PROGRAMME BNAIC 99 Tuesday November 2 17.00 18.00 Early-bird reception (at the Maastricht town hall) Wednesday November 3 09.00 09.45 Reception and coffee 09.45 10.20 Opening 10.20 11.20 Invited lecture by Tom Mitchell: Extracting Information from the World Wide Web 11.20 11.35 Coffee break 11.35 12.25 Three Parallel Sessions (11.35 12.00; 12.00 12.25) LOGIC AND REASONING 1 Shan-Hwei Nienhuys-Cheng The Complexities of a Refinement Operator for Prenex Conjunctive Normal Forms A.Bos, N. Roos and C. Witteveen Computing with Computational Histories EVOLUTIONARY COMPUTATION 1 D.D.B. van Bragt, C.H.M. van Kemenade and J.A. La Poutré The Influence of Evolutionary Selection Schemes on the Iterated Prisoner's Dilemma B.G.W. Craenen, A.E. Eiben and E. Marchiori Solving Constraint Satisfaction Problems with Heuristic-based Evolutionary Algorithms MACHINE LEARNING AND NEURAL NETWORKS 1 W. Pijls and J.C. Bioch Mining frequent itemsets in memoryresident databases L.F.A. Wessels, M.J.T. Reinders, R. Baldocchi and J. Gray Statistical analysis of gene expression data 12.25 13.30 Lunch 13.30 14.45 Three Parallel Sessions (13.30 13.55; 13.55 14.20; 14.20 14.45) BELIEF NETWORKS N. Peek, V. Coupé and J. Ottenkamp Focused quantification of a belief network using sensitivity analysis S. Renooij and L.C. van der Gaag Exploiting Non-monotonic Influences in Qualitative Belief Networks J. Donkers, R. Ferreira, J. Uiterwijk, and H.J. van den Herik VAS: Quantifying a Qualitative Network SEARCH E. Marchiori and A. Steenbeek A Genetic Local Search Algorithm for Random Binary Constraint Satisfaction Problems D.M. Breuker, J.W.H.M. Uiterwijk, and H.J. van den Herik Investigating pn 2 Search P. van Bael, D. Devogelaere, and M. Rijckaert HESSA solves the Job Shop Scheduling Problem AGENT TECHNOLOGY 1 K.V. Hindriks, F.S. de Boer, W. van der Hoek, and J.-J. Meyer A Formal Semantics of the Core of AGENT-0 K. Jonkheer Intelligent agents, markets and competition - consumers' interests and functionality of destination sites C. Castelfranchi, F. Dignum, C.M. Jonker, and J. Treur Deliberate Normative Agents: Principles and Architecture BNVKI newsletter 129
14.45 15.00 Coffee Break 15.00 16.15 Three Parallel Sessions (15.00 15.25; 15.25 15.50; 15.50 16.15) KNOWLEDGE REPRESENTATION AND SYSTEMS A. Mittal and K.K. Biswas A Knowledge-Based Framework For Satellite Video Indexing P. Beys and M. Jansen Automatic Reuse of Knowledge: A Theory A. ten Teije and F. van Harmelen Describing Problem Solving Methods using Anytime Performance Profiles AI AND LAW (JURIX SESSION) L. Mommers Transfer of knowledge in the legal domain R. Winkels, D.J.B. Bosscher, A.W.F. Boer and J.A. Breuker Generating Exception Structures for Legal Information Serving B.Verheij Automated Argument Assistance for Lawyers ROBOTICS AND VISION 1 J. Van Looveren Multiple Word Naming Games S.H.G. ten Hagen, D. l' Ecluse and B. Kröse Q-Learning for Mobile Robot Control E.D. de Jong Autonomous Concept Formation 16.15 16.30 Coffee Break 16.30 17.30 Invited lecture by Edward Rietman (Lucent Technologies): AI Techniques in Manufacturing of Integrated Circuits 17.30 18.00 Introduction to the RoboCup soccer demonstration by Frans Groen: Robot Soccer: Game or Science? 18.00 19.30 Happy hour sponsored by Bolesian and RoboCup soccer demonstration sponsored by Tuyllian 20.00 23.00 Conference Dinner THURSDAY NOVEMBER 4 09.00 09.50 Three Parallel Sessions (09.00 09.25; 09.25 09.50) LOGIC AND REASONING 2 H.van Ditmarsch The Logic of Knowledge Games: showing a card J. Kamps On Criteria for Formal Theory Building EVOLUTIONARY COMPUTATION 2 A.J.M.M. Weijters and J. Paredis Discovering Rules with a Genetic Sequential Covering Algorithm (GeSeCo) I.G. Sprinkhuizen-Kuyper, C.A. Schippers, and A.E. Eiben On the real arity of multiparent recombination MACHINE LEARNING AND NEURAL NETWORKS 2 M. Schuemie and J. van den Berg Information Retrieval Systems using an Associative Conceptual Space and Self- Organising Maps W. Peng, J. Nijhuis, and L. Spaanenburg Notes on Embedding a Trained Neural Network 09.50 10.05 Coffee Break BNVKI newsletter 130
10.05 11.05 Invited lecture by Jonathan Schaeffer: The Games Computers (and People) Play 11.05 11.20 Coffee break 11.20 12.35 Four Parallel Sessions (11.20 11.45; 11.45 12.10; 12.10 12.35) DEMONSTRATIONS 1 R. Ekkelenkamp IGUANA: A Web Crawler Aimed at Creating a Domain Specific Web Search Engine M.C. van Wezel, J. Sprenger, R. van Stee, J.A. La Poutré, and J.B.M. van Wieringen Neural Vision 2.0-Exploratory Data Analysis with Neural Networks J.N.H. Heemskerk, R. Klopman, M.R. Vonder and R. de Wit Agent Based Customer Service AI IN MEDICINE (SPECIAL SESSION) N.L.W. Keijsers, M.W.I.M. Horstink, and C.C.A.M. Gielen Detection and Assessment of the Severity of Levodopa Induced Dyskenesia in Patients with Parkinson's Disease by Neural Networks D.M.H. Van Hyfte, P.A. de Clercq, T.B. Tjandra-Maga, F.G. Zitman, and P.F. de Vries Robbé Modelling the psychoactive drug selection application domain at the knowledge level P.A. de Clercq, J.A. Blom, A. Hasman, and H.H.M. Korsten GuiDE: an architecture for the acquisition and execution of clinical guidelineapplication AGENT TECHNOLOGY 2 R.M. van Eijk, F.S. de Boer, W. van der Hoek, and J.-J.Ch. Meyer Open Multi-Agent Systems: Agent Communication and Integration A.G. Pérez and V.R. Benjamins Overview of Knowledge Sharing and Reuse Components: Ontologies and Problem-Solving Methods C.M. Jonker, R.A. Lam, and J. Treur A Multi-Agent Architecture for an Intelligent Website in Insurance ROBOTICS AND VISION 2 P. Vogt Grounding a Lexicon in a Coordination Task On Mobile Robots R. Kortmann, E. Postma, and H.J. van den Herik The trade-off between spatial and temporal resolution in visual systems 12.35 13.45 Lunch and BNVKI meeting 13.45 15.00 Four Parallel Sessions (13.45-14.10; 14.10-14.35; 14.35-15.00) DEMONSTRATIONS 2 S. Spreeuwenberg and R. Gerrits, A Knowledge Based Tool to Validate and Verify an Aion Knowledge Base J.H. van Lieshout and E.C. van de Stadt KMD-MATE-An analysis and design environment for Knowledge Management Support J.I. van Hemert and A.E. Eiben Mondriaan Art by Evolution EVOLUTIONARY COMPUTATION 3 J. Eggermont, A.E. Eiben and J.I. van Hemert Comparing genetic programming variants for data classification BNVKI newsletter 131
W.B. Langdon Size Fair Tree Genetic Programming Crossover P.A.N. Bosman and D. Thierens On the Modelling of Evolutionary Algorithms AGENT TECHNOLOGY 3 M. Albers, C.M. Jonker, M. Karami and J. Treur An Electronic Market Place: Generic Agent Models, Ontologies and Knowledge D.E. Herlea, C.M. Jonker, J. Treur and N.J.E. Wijngaards Specification of Behavioural Requirements within Compositional Multi-Agent System Design L. van der Torre and Yao-Hua Tan Rights, Duties and Commitments between Agents DEMONSTRATIONS 3 P.A.N. Bosman and D. Thierens Interactive and continuous visualizations of EAs: The EA Visualizer W. Teepe "Wij kiezen partij voor u" Online Voting Advise L. Hulzebos (Special talk of Bolesian) Promising Practical Fruits of AI LOGIC AND LEARNING THEORY G. Bontempi and M. Birattari A bound on the cross-validation estimate for algorithm assessment H. Jurjus and H. de Swart Implication-with-possible-exceptions M. Bertolino, S. Etalle, and C. Palamidessi The Replacement Operation for CCP Programs MACHINE LEARNING AND NEURAL NETWORKS 3 ROBOTICS AND VISION 3 T. Belpaeme Evolving Visual Feature Detectors A. Kröse, R. Bunschoten, N. Vlassis, and Y. Motomura Appearance based robot localization P. Andras, E. Postma, and H.J. van den Herik Dealing with Environmental Dynamics 15.00-15.15 Coffee Break 15.15-16.30 Three Parallel Sessions (15.15 15.40; 15.40 16.05; 16.05 16.30) M. Bot and W.B. Langdon Application of Genetic Programming to Induction of Linear Classification Trees S.M. Bohté H. La Poutré and J.N. Kok Unsupervised Classification in a Layered RBF Network of Spiking Neurons F. Verdenius and M.W. van Someren Top-down Design and Construction of Knowledge-Based Systems with Manual and Inductive Techniques 16.30-17.00 Closing and Awards. LOGIC AND GAMBLING Jaap van den Herik IKAT, Universiteit Maastricht BNVKI newsletter 132
The world is full of structure. For any Ph.D. student it is a real challenge to reveal part of the structure to her/his supervisor. Those who succeed in doing so are bestowed with the honour of being listed in the pages of the BNVKI newsletter and as a consequence they receive the degree of Doctor after a successful thesis defence. This is logical and therefore full of logic. But how about finding structures in Chaos and Gambling? Is there any structure in chaos? Can gambling help us to find structures in chaos? Or should we use Informed Gambling? Of the six thesis defences announced below, four are a repetition of the announcement in the August issue. Two are new. These are the ones of which the dates have already passed. The Editorial Board congratulates H. Vandecasteele and G. Zwaneveld with the receipt of their Doctor titles. The same holds for A. de Moor (October 1). We wish Fernandez de Montessinos, Lenting, and Willemse much energy with the preperation of their defence. Moreover, we are happy to announce that we publish three reviews. The review below by Bart de Boer is not specifically on a Ph.D. thesis, but on a book written by Simon Kirby which resulted from a Ph.D. Thesis. A propos, this thesis has never been announced in the BNVKI Newsletter. Two Ph.D. theses which have been announced in our pages, are now reviewed. First, Marnix Weusten s thesis is reviewed by Bart Verhey, and second, Ronald Leenes thesis by Kees de Vey Mestdagh. Both reviews are placed in the Section Knowledge Systems in Law and Computer Science. Readers interested in applications of AI in the real world, especially in the world of Law are encouraged to read the reviews. They constitute one more sign of progress in a world which consisted only a decade ago mostly out of disbelievers of Artificial Intelligence. The change is clearly present in this world: Internet, ICT, and AI are combining their forces. H. Vandecasteele (May 26, 1999) Constraint Logic Programming: Application and Implementation. Katholieke Universiteit Leuven. Promotor: Prof.dr. Danny De Schreye. Additional promotor: Prof.dr.ir. Maurice Bruynooghe G. Zwaneveld (June 4, 1999) Kennisgrafen in het Wiskundeonderwijs. Open Universiteit Nederland Promotor: Prof.dr. J. van Craats. Additional promotor: dr. A. van Streun A. de Moor (October 1, 1999) Empowering Communities: a Method for the Legitimate User-Driven Specification of Network Information Systems. Katholieke Universiteit Brabant. Promotor: Prof. dr. R.A. Meersman; co-promotor: Dr. H. Weigand. M. Aznar Fernandez de Montessinos (October 25, 1999) Aeroplane Design: AI applications. Technische Universiteit Delft. Promotor: Prof. dr. H. Koppelaar. J.H.J. Lenting (December 3, 1999) Informed Gambling: Conception and Analysis of a multi-agent mechanism for discrete reallocation. Universiteit Maastricht. Promotor: Prof. dr. H.J. van den Herik; co-promotor: dr.p.j. Braspenning. W.J. Willemse (December 3, 1999) Computational Intelligence: Life without Tables for the Actuary. Technische Universiteit Delft. Promotor: Prof.dr. H. Koppelaar. Book Review FUNCTION, SELECTION, AND INNATENESS BNVKI newsletter 133
SIMON KIRBY A review by Bart de Boer AI Lab, VUB Sometimes I encounter a book that is a real intellectual adventure to read. Such a book must be related to my own research interests, there must be lots of fascinating material in it, but there must also be enough in it that I do not agree with. That way I am constantly faced with the question why I do not agree with it, and what arguments I can find to defend my own position. Simon Kirby s book Function, Selection and Innateness, The Emergence of Language Universals from Oxford University Press is just such a book. The book is based on Kirby s Ph.D. thesis and has been published in a series of books on the evolution of language, in which Andrew Carstairs-McCarthy s book The Origins of Complex Language has also been published. UNIVERSAL PROPERTIES In his book, Kirby addresses the problem of how universal properties of human languages can emerge. At first sight, this question might seem to be remotely related to research in the field of artificial intelligence, but Kirby s solution to this problem as well as his methodology are of interest to the AI-community. Kirby investigates what aspects of language can be explained by functional pressures (and are therefore learnt) and what aspects must be innate, as well as what mechanism can explain how functional constraints can make language to be maximally functional. The debate of what is learnt and what is innate is also central in artificialintelligence research. Also, Kirby uses computer simulations of populations of language learners in order to test his hypotheses. Kirby begins his book by posing the central question: how is it possible that languages tend to be close to optimal from a functional point of view? He calls this The puzzle of fit (borrowing a phrase from Gary Cziko) or the Problem of linkage. If we look at a large sample of languages, we find they have universal properties that make them easier to process. How can it be explained that constraints of processing influence the form of language? KIRBY S HYPOTHESIS In the second chapter, Kirby presents the hypothesis that constructions that are harder to process are harder to learn. The basis of this hypothesis is that the difficulty of parsing linguistic input determines how often it will be presented to the language acquisition device. The fact that a new generation has to learn the language from the previous one under these pressures then might cause some constructions to disappear from the language and some to be favoured. Kirby tests this with a computer simulation. In his model, certain constructions are assigned an a-priori parsing difficulty (based on the complexity of the parsing tree). He then generates a population of language users that all have a random language. The probability of learning a construction is based on the difficulty of parsing. He shows that under these circumstances the most functional type of language will be preferred, and that the composition of the language changes over time follows the same sigmoid curve as language change observed in human languages. Kirby recognises that his simulation has a problem: it will generally result in only one language type emerging, while in reality, different types co-exist. He therefore introduces what he calls competing motivations in his third chapter. Competing motivations are in fact pressures that work in opposite directions. BNVKI newsletter 134
Kirby uses p-complexity (parsing complexity) and m-complexity (morphological complexity). If a sentence is hard to parse, this can be compensated for by introducing more morphology (inflections, cases etc.) so that the listener is aided in understanding the sentence. By using m-complexity and p-complexity of which the relative influence change randomly over time and by using a spatial distribution of agents, so that only agents that are neighbours in a grid communicate, it is shown that co-existing different language types emerge. LIMITS Then Kirby argues that there are limits to the explanatory power of functional constraints. Using examples from different languages, he shows that sometimes functionally advantageous constructions do not occur in human language, and that there might even be phenomena that are maladaptive. These, according to Kirby, are the result of the innate properties of the Universal Grammar or Language Acquisition Device shared by all humans. Universals of human language can therefore be explained as an interaction between functional constraints that tend to push languages in a certain direction, and the universal grammar, that causes some languages to be learnable and others not. He then goes on in the last chapter before his conclusions to show how it is possible for Darwinian evolution in combination with functional constraints on language use to influence the human Language Acquisition Device through the Baldwin effect. In this way it becomes possible that the human capacity for language becomes adapted for acquiring functional languages only. Kirby finally combines the different influences on language universals in one diagram, with a functional side and an innatist side. Functional pressure on the one side causes some languages to be preferred over others. The language acquisition device on the innatist side, causes some languages to be learnable and others not. Furthermore, evolutionary pressure causes the language acquisition device to become more and more adapted to functional languages. This way, Kirby argues, the puzzle of fit is (at least partly) solved. Kirby provides a laudable synthesis between functionalist and innatist approaches to explaining language universals. He shows that these two schools of thought are not at all incompatible with each other, but that they are, on the contrary, both needed in order to explain language universals. He even goes beyond creation of mere theories, and puts his to the test with computer simulations. I think in combining innate and functional criteria Kirby is right on the mark, and that his ideas are applicable to other domains of cognition, so that his book is not only interesting to people interested in language universals or the origins of language. CRITIQUE Still, as I mentioned in my introduction, I did not agree with everything Kirby has done. First of all, the computer simulations Kirby uses are rather simple and ad hoc from the perspective of an AI-researcher such as myself. In principle there is nothing wrong with simple computer simulations, in fact they are to be preferred over overly complex ones, as it is clearer to investigate their behaviour. However, Kirby has simplified his model almost too far, in my opinion. What is left over of a very interesting and plausible theory of the influence of parsing complexity on language is basically a difference equation in which the emerging behaviour (the sigmoid transition towards preferred language types) can be predicted from the equations by direct mathematical analysis. BNVKI newsletter 135
The behaviour is therefore no longer really emergent. This is probably also a cause why Kirby has to introduce what are in my opinion rather ad hoc solutions, such as a randomly changing influence of m- complexity and p-complexity and a spatial distribution in order to get different language types in one population. STRUGGLING WITH ARTE FACTS Also, I think that some problems Kirby needs to explain are caused by the adherence to certain aspects of generative grammar, such as for example the autonomy of syntax. Of course, as generative grammar is very influential in linguistics, it is necessary to argue that a theory is not in conflict with it. However, to a relative outsider like myself, it seems as if some of the problems Kirby struggles with are artefacts of the generative theory, rather than real problems. CONCLUSION Concluding, I think Kirby s book is a valuable contribution to the question how linguistic universals can be explained as well as to the wider debate of the role of innateness versus functionalism in explaining cognitive phenomena. His use of computer simulations in investigating his theories provides good additional support to his arguments. Furthermore, it is compact and very well written. I would therefore recommend it to linguists, as well as cognitive scientists and artificial intelligence researchers. THE SIXTEENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE A report by Edwin de Jong VUB AI Lab http://arti.vub.ac.be/~edwin From July 31 to August 6, I visited the Sixteenth International Conference on Artificial Intelligence (IJCAI) in Stockholm. IJCAI is one of the oldest international AI events. It is also one of the largest conferences on the subject; this year s edition featured 29 workshops, 20 tutorials, and 194 out of 750 submitted conference papers. Clearly, this report will not cover more than a fraction of the papers that were presented; it describes the presentations that I found most interesting of the ones I have visited. THE STATISTICAL MACHINE LEARNING FOR LARGE SCALE OPTIMIZATION WORKSHOP The workshop Statistical Machine Learning for Large Scale Optimization was based on the observation that techniques from machine learning are becoming sufficiently powerful to be utilized for real world optimization problems. One of the application domains listed by the organizers is Computer Aided Design (CAD). A frequently recurring task within this domain is that of graph partitioning, where nodes of different classes have to be separated from each other, cuts are associated with costs, and the total costs of the cuts have to be minimized. Recent methods addressing this problem start by finding a clustering of the nodes of the graph. The initial problem is then transcribed into that of partitioning the graph of clusters, which decreases the problem size by an order of magnitude while maintaining an adequate performance. Graph partitioning methods were presented by Wray Buntine and by Ted Carson. COUNTING LOCAL MINIMA BNVKI newsletter 136
Rich Caruana of JustResearch presented a method for estimating the number of local minima in large complex search spaces. His method was inspired by a problem familiar in statistics, called the birthday problem: how many people must be in a room for the probability of two or more people sharing the same birthday to exceed ½ 1. A variation of this is the Martian Year problem: how long is a Martian year if on average we have to coax k Martians into a room before two of them have the same birthday? Analogously, one may randomly sample a search space and count the number of local minima before a duplicate is found to obtain an estimate of the total number of local minima. The lower bound provided by this creative technique may for example be used for selecting an appropriate solution method; the required information becomes available during optimization anyway, and does not incur much extra computational costs. OPTIMAL STOPPING Benjamin Van Roy presented a method developed by John Tsitsiklis and himself that approximates solutions of optimal stopping problems. The problems consist of deciding when to stop a Markov chain that returns values at each time step and a terminal reward at the end such that the expected discounted sum of rewards is maximized. The talk started with a clear and well-presented overview of Temporal Difference learning. Temporal Difference (TD) methods, named such by Richard Sutton, learn to predict a temporally distant value by making adjustments based on differences between temporally successive intermediate estimates, rather then the final outcome, and are the basis for most modern reinforcement learning methods. General convergence results for reinforcement learning methods require the use of lookup tables, which limits the scope to small problems. Autonomous systems need to use approximations of the value function, which may cause divergence. Van Roy envisioned a time when spreadsheet programs will have built-in support for reinforcement learning problems which can be activated by the 1 The answer is surprisingly low: at least 23 people proverbial pushing of a button, given selected basis functions. As a step towards this ambitious goal, he presented a TD approach to optimal stopping, along with theoretical results concerning its convergence and approximation error. INSTRUCTION SCHEDULING Andrew Barto, a main contributor to the increased formal understanding of reinforcement learning that has spurred developments in this branch of research, presented an application of reinforcement learning to instruction scheduling. Instruction scheduling is the problem of how the instructions in a basic block of machine code may be reordered in order to minimize their execution time. TD learning was shown to yield good schedulers without using any heuristics. The benefit of the method does not lie in the overall performance of the learned schedulers, which is a bit lower than that of commercial schedulers, but rather in the fact that it provides a means of quickly generating good schedulers. In addition, it outperformed a commercial scheduler when trained and tested on specific problems, suggesting a potential for generating high performance special purpose schedulers. The approach may save development time in settings where schedulers for new architectures are frequently required. REGRESSION TREES Tom Dietterich presented a regression tree approach to value function approximation. A novel aspect of this work is the use of three error terms: the supervised training error, a Bellman error term, and an advantage error term, all of which were found to be important for obtaining good performance. These terms are combined into a single composite error by weighting. Experimental results with a job-shop scheduling task indicated that performance of the regression tree method was comparable to an earlier neural network approach to the same problem, suggesting a potential for regression trees as approximators of the value function. But several problems need to be overcome first; the current method requires estimates of state values in order to compute the supervised error term, regression trees are not well suited for incremental learning, and all features are assumed to be equally relevant and uncorrelated. PANEL SESSION Organizer Wray Buntine opened the panel session by stressing the importance of turnaround time as a BNVKI newsletter 137
factor governing the applicability of machine learning methods to optimization. If learning methods can be rigged up quickly and produce answers within specified time limits, they may obtain a place within optimization practice, especially where traditional methods are troubled by large search spaces. Dietterich stressed the importance of using appropriate representations. Barto stated that learning value functions is hard, that no turn-key methods are available, and that new algorithms and theory are necessary. Boyan was more optimistic, and suggested that learning methods employing rollouts might become important. A rollout algorithm uses a given reasonable policy to estimate the values of different actions, and changes its policy according to these estimates. Although computationally intensive, rollouts can often give good performance. THE NEURAL, SYMBOLIC, AND REINFORCEMENT METHODS FOR SEQUENCE LEARNING WORKSHOP CLUSTERING TIME SERIES Tim Oates presented an interesting method for clustering times series with Hidden Markov Models (HMMs) and Dynamic Time Warping (DTW). The method automatically determines the number of HMMs that generated the data, along with their parameters. This determination was done with DTW. DTW computes a warping of two time series that minimizes the difference between the series. The remaining differences between the time series, computed as the area between them, is used as a distance measure in an agglomerative hierarchical clustering method. The rough clustering that results from this is then refined by a clustering method that iteratively trains an HMM on the set of sequences and throws out the sequences that do not fit the resulting HMM. This hybrid clustering method yielded promising experimental results, and is the subject of continued research. CONCEPT FORMATION Paul Cohen from the University of Massachusetts presented work with Michael Rosenstein on autonomous concept formation with a mobile robot. Events were detected based on correlations of the sensor time series with several templates. A time window around the event was then fed into a clustering procedure, and after computing signatures and performing a second clustering, a hierarchy of categories is the result. Although current results are interesting on themselves, I think a promising extension of the work would be to take relevance of concepts for the robot s behavior into account in the concept formation process. EMBEDDED LANGUAGES Mikael Bodén has investigated a recurrent neural network that learns a deeply embedded language. Whereas regular languages have been learned with success by several researchers (Pollack, Cleeremans, Elman and others) these more difficult languages may cause instability in networks. Bodén trained a set of networks on the task of learning the language a n b n. The hidden state that is necessary to recognize this language is encoded in oscillations of two hidden neurons, which requires considerable precision, especially for large n. An analysis of the error gradients revealed a spiky landscape, especially near the region of interest. Given the difficulties any gradient method will have on such an error surface, this explained the problematic behavior. Current work therefore considers whether choosing a different search space may improve learning performance. THE TECHNICAL PROGRAMME BOOSTING Robert Schapire delivered an invited lecture on the theory and practice of boosting. Boosting is a recent class of methods that builds strong classifiers by computing weighted combinations of simple decision rules. The simple decision rules are easily found using a weak classifier, and weighting is such that emphasis is put on the difficult cases that are classified wrongly by the simple rules. These cases often turn out to be outliers. Several problems that affected early boosting methods were fixed in later methods, such as AdaBoost [Freund and Schapire, 1995]. A counterintuitive result is that for certain problems, boosting does not only decrease the training set error (exponentially in the number of passes), but also the test set error, even when the training set error has already dropped to zero. This is a result of boosting s property of increasing the margins of the training examples, which links boosting to Support Vector Machines. However since there is no such thing as a free lunch, there must also be problems for which boosting does not work so well. As might be expected, this is the case for problems with substantial noise, as shown by Tom Dietterich [Dietterich, to appear]. BAYESIAN NETWORKS BNVKI newsletter 138
David Heckerman of Microsoft Research gave an overview of work on Bayesian networks. In the early days of Bayesian networks, they were constructed by hand, based on interviews with domain experts. Nowadays, it has become common practice to construct automatically Bayesian networks by learning them from data. An example is collaborative filtering, where the preferences of a large group of consumers are searched to find relationships between their interests. Bayesian networks are enjoying increased attention, as can be seen from the overwhelming presence of work in this area at the recent Uncertainty in AI conference (UAI) and the increasing number of commercial Bayesian network packages. Technically, the core idea of a Bayesian network is that a probability distribution over a set of random variables is dissected into a set of local distributions. The dependencies between the different variables are graphically represented in a directed graph. If these dependencies are sparse, it becomes feasible to compute the complete jointprobability distribution from the local distributions. The presentation described the construction of Bayesian networks by combining both expert knowledge and data. The focus was on how these construction techniques can be used to determine causal relationships from observational data. Although a long held view of statisticians is that causal relationships cannot be learned from observational data, Heckerman holds that this is possible under certain assumptions. The argument is based on the causal Markov assumption [Spirtes, 1993], which states that if a direct acyclic graph C is a causal graph for a set of variables, i.e. nodes correspond to variables and arcs to causal relationships, then C is also the structure of a Bayesian network for the joint physical probability distribution of those variables. Furthermore, a limited set of possible causal networks is assumed. If a particular Bayesian network can then be learned from the data with high probability, this rules out those causal networks that are inconsistent with the Bayesian network that was found. If the assumptions are correct, this elimination process may lead to a single causal model. CAUSAL RELATIONSHIPS Causal relationships were also the subject of Judea Pearl s lecture. Pearl received the IJCAI-99 award for research excellence, of which past recipients include John McCarthy, Alan Newell, Marvin Minsky and Herbert Simon. For the question of how a robot can acquire causal information from its environment, he referred to David Heckerman s work. The focus of Pearl s own presentation was on how a robot may receive information about causal relationships from its designer. Although this was not a question for Pearl himself anymore, he was was willing to attempt to convey his tranquility to the audience, as he put it himself. The central topic was a procedure for evaluating counterfactual questions, such as would A have been different if it were not for B? Based on the principle of mutilate and simulate, which involves forcing variables in the network to take particular values, these questions can in certain cases be answered. He gave several examples of this procedure, and mentioned actual applications to policy evaluation and troubleshooting. Unfortunately, when he came to the point of a mid-talk summary, the time allotted to the lecture was almost over. Judea Pearl s lecture was rewarded with a standing ovation. MEASURING MOTION Radu Horaud was originally interested in visual servoing and the coordination of vision and action. When he realized the importance of how motion is measured he began to use a vision system with two cameras instead of one so that a motion representation consistent with stereo vision could be investigated. Under the condition that the position of the cameras relative to each other is fixed, this approach yields accurate and reasonably fast estimations of motion, and practical applications of the method are expected. ROBOCUP Whereas in earlier robotic soccer events games were sometimes rather static and the purpose behind moves was not always clear, this year s Robocup event provided some quite spectacular shows. Several problems that had been encountered on earlier occasions had now been addressed. For example, some of the small size league teams had incorporated a solution for getting the ball out of the corner by spinning their small robot. The speed of the game in this league was sometimes truly amazing. The Robots from Korea for example were able to catch up with the ball, get behind it, and drive it into the goal of their opponents. The competition in this league was won by Cornell University. The team from Iran made up for last year s disappointment when they could not enter France in time to play in the Paris RoboCup by winning the mid size league competition. As Minoru Asada remarked during his presentation, engineering issues are still a concern in RoboCup. It is to be hoped that the technological progress that has been made will allow the teams to focus on strategy in the future. Asada has a clear ultimate goal in mind for robotic soccer; to beat a human BNVKI newsletter 139
team with a team of humanoid robots. As a video of the Honda humanoid robot showed, this will still take a while, but progress is being made. NEXT EDITION The Seventeenth IJCAI will be held August 5-10 2001 in Seattle, Washington. More information will become available through www.ijcai.org. REFERENCES [Dietterich, to appear] T.G. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning. (2000) [Freund and Schapire, 1995] Y. Freund and R.E. Schapire. A decision-theoretic generalization of on-line leaning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139, August 1997. [Spirtes et al., 1993] P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. Springer-Verlag, New York, 1993. CASYS 99: COMPUTING ANTICIPATORY SYSTEMS A report by Edwin de Jong VUB AI lab On August 9 and 10, I visited the first two days of CASYS'99, the third conference on Computing Anticipatory SYStems. The CASYS conference took place from August 9 to 14 in Liège, Belgium. There were 165 papers in four parallel tracks, so I could only attend a fraction of the presentations. Although artificial intelligence is in principle concerned with the construction of systems that anticipate, I had not come across the term anticipatory systems before in AI literature. Therefore, my main purpose in visiting the conference was to find out what computing anticipatory systems are and whether the research that is being done on them is relevant to AI. The conference programme listed many diverse subjects, and I was not sure what to expect; this might indicate that the field is well developed and has many different branches. However, my observations during the conference did not confirm this. Papers of widely varying quality and from different perspectives have been accepted for presentation. The relations between these different perspectives on anticipatory systems often remained unclear. In the following, a few presentations will be described that may serve as examples of different approaches to the subject. In an introductory paper on computing anticipatory systems Daniel Dubois, organizer of the CASYS conferences, describes two types of anticipatory systems. One class of anticipatory systems is that of systems with multiple potential future states for which the actual states the system visits are determined by the events at each current time". This describes the class of dynamical systems. The other class mentioned by Dubois is that of systems that use an expectation about the future in determining their current action. In computer science, such a system would be called an autonomous agent. Both classes are clearly of interest to AI. Tom Quick gave an interesting presentation on embodiment. The presentation is a good example of research at the conference that is aimed towards an increased understanding of anticipatory systems, since it discusses what properties of such systems are fundamental, so that research may focus on systems possessing those properties. His main point was that what matters is not whether autonomous systems are physical systems or software systems, but rather what the nature is of the interaction between the system and its environment. George Mobus presented work on foraging agents. The agents in his experiments had to learn causal relations to anticipate resources or threats, and were carried out on a robot and with software agents. These presentations are examples of research that can also be found at AI conferences such as ECAL or SAB. The BNVKI newsletter 140
following sections describe presentations that were more specifically aimed at CASYS. INCURSION The source of the term anticipatory systems is a book by Robert Rosen (1985) with the same title. In his own presentation, Dubois gave a review on computing incursive, hyperincursive and anticipative systems. He presented incursion and hyperincursion as two principles to be used for modeling, simulating and controlling anticipatory systems. At time t, the state of a system with incursion at the next timestep x(t+1) can be described as a function of among others x(t+1) itself. Hyperincursion is similar, but with the additional property that the equation has multiple solutions. In [1], Dubois defines an incursion as a relation that can be written as: x(t+1) = F[..., x(t-1), x(t), x(t+1),...] "where the value of a variable x(t+1) at time t+1 is a function of this variable at past, present and future times." Thus, x(t+k) denotes the state of the system at time t+k, as usual. The next state of the system may therefore depend on future states of the system. In some cases, e.g. the example given in Dubois s paper [1], such relations can be rewritten such that no knowledge about the future is necessary. In other cases it is not possible to determine these values at the current point in time. Dubois repeatedly mentioned the final causation principle of Aristotle in connection with anticipatory systems. In [2], this principle is described as follows: A future cause could produce an effect at the present time. Then the causality principle seems reversed. The definition of incursive relations may appear to be a formalization of the final causation principle. However, it is important to notice that not just any incursive relation represents a consistent system. This is because the variables x(t+k) are not arbitrary variables, but successive values of a single variable, and the relation thus specifies a system of equations when different values are substituted for t. In my view, systems that can be described by incursive relations are special in that successive states bear a particular relationship to each other, rather than demonstrations or explanations of how some future cause would affect the present. Furthermore, the definition of incursion does not take into account the idea that anticipation may involve a prediction about future states (be it of the system itself or its environment), since it only involves actual states. Its relevance to anticipation may therefore be questioned. QUANTUM MECHANICS Many papers at the conference referred to quantum mechanics. In the field of quantum computation, it is investigated whether quantum mechanical principles can be used to achieve a qualitative increase of the computational power of computers. In contrast, these were not the reasons for the interest in quantum mechanics at CASYS. Rather, the implicit assumption is that quantum mechanical effects may explain properties of anticipatory systems. In his abstract for the conference titled "Anticipation A spooky computation", Professor Mihai Nadin expresses his view that anticipatory processes are related to quantum non-locality. He states that "anticipation is, of course, different from expectation or from forecasting". Furthermore, "in the realm of the living, correlations among separated but entangled parts of a system defy the accepted notions of causality (at least in its classical deterministic sense) and of unidirectional time progression". BNVKI newsletter 141
Nadin does not explain how anticipation in living systems might relate to quantum nonlocality, other than in broad and general terms. This is a pity. Although there is little doubt today that quantum effects play a role at the smallest scales in physical systems, I know of no experimental evidence that quantum effects (such as entanglement) in living systems are related to thinking or anticipation. If quantum mechanics is to be a basis for research into anticipation, it is important to clarify how its properties may be related to anticipation. FURTHERMORE Finally, there were papers that cannot easily be classified under an existing scientific dicispline. Maybe the best way to explain this is to give an example. To quote from one of the abstracts: The II-III transition to humanness, exhibiting language, holistic thinking, consciousness and sentiency, arises by en passant growth of Pavlovian nonholistic circuits into Steinbuch-Taylor (S-T) holistic matrix circuitry plus elaborations found in the cortex. In the context of the hamiltonian organization of man and animal, this transition from Pavlov and the von Neumann externally programmed computer to the self-programming S-T matrix computer and mankind requires only a few mutations. [emphasis in the original] difficult to discern any relations between them. Ideally, these relations would be clear from links with the underlying paradigm of anticipatory systems. However, the implications of the presented research on knowledge about anticipatory systems more often than not remained undiscussed. This can be a consequence of obscurities in or unfamiliarity with the subject of the conference, such as the unclear role of incursive relations, and of a lenient review process. Therefore, my recommendation for possible future editions of the conference would be to focus on presentations that clearly aim at an increased understanding of or knowledge about anticipatory systems. This could be implemented in the review process, and would have the side-effect that the duration of the conference (6 days) will decrease somewhat, which would be welcome considering the chronic lack of time of the modern researcher. [1] D.M. Dubois (1998). Computing Anticipatory Systems with Incursion and Hyperincursion. AIP Conference Proceedings 437, Computing Anticipatory Systems: CASYS-First International Conference 1997. D.M. Dubois (ed.). American Institute of Physics. [2] D.M. Dubois. Introduction to Computing Anticipatory Systems. DISCUSSION An extremely wide variety of presentations has been accepted for presentation at the conference, as can be judged from the titles of the presentations: these contain such diverse topics as "general axiomatic theory of everything", space flight, spooky computation, holism, diffusion in chemical systems, and the size of the Internet. In general, assembling researchers with different experience and viewpoints can lead to very fruitful events. At CASYS however, the differences between the presentations were often so large that it was BNVKI newsletter 142
NOUVELLE AI: A REPORT ON FOUR SEPTEMBER EVENTS EWNS-2, PhD course on behavior based robotics, ECAL-99, and EWLR-8 Rens Kortmann IKAT Universiteit Maastricht kortmann@cs.unimaas.nl The first three weeks of September featured four scientific events on what is sometimes referred to as Nouvelle AI, behaviour-based AI, or Artificial Life. There exist no exact definitions of these terms, so, hopefully, this report will give an impression of what is meant here. Also, hopefully, readers will feel inspired by the topics covered in the events, so that also here, in Belgium and the Netherlands, it will be possible to establish a Nouvelle AI tradition. First, the computer science department of the University of Stirling hosted the second European workshop on neuromorphic systems (ewns-2) from 3 to 5 September. As the call for papers says: `Neuromorphic systems are implementations in silicon of systems whose architecture and design are based on neurobiology.' Second to fourth, the Swiss federal institute of technology (EPFL) offered (2) a PhD course on behaviourbased robotics, (3) the fifth European conference on artificial life, (ECAL-99) and (4) the eighth European workshop on learning robots (EWLR-8). The common theme of all events was the situated approach to artificial intelligence. Rather than studying, e.g., disembodied reasoning systems, the situated approach investigates relatively simple, though robust, interaction patterns of artificial systems with the real world. Starting from this position, the situated approach aims to scale up to higher levels of intelligence. EWNS-2, SEPTEMBER 3-5 SILICON BRAINS AND ROBOT ANIMALS The programme of the second European workshop on neuromorphic systems (EWNS-2) covered five different topics: general papers, auditory systems, visual systems, robotics, and hardware for neural network models. The talks were spread out over three days and were followed by a panel discussion on Sunday afternoon. On the first day, we were welcomed on the scenic campus of the University of Stirling by Leslie Smith, the main organiser of the workshop. In his Scottish accent he announced Pedro Marijuan, professor at the University of Zaragoza, who gave an invited lecture on the quest for a neurodynamic optimization principle. The quest took us from Darwin to Ramon y Cajal, a famous Spanish physiologist and explained how animals developed a neural signaling system. Then, Barbara Webb (well-known for her robot model of cricket phonotaxis) proposed a framework for talking about bio-mimetic models. Very often, people discuss models of biological behaviour without adopting a common terminology. The careless use of terms, though, often causes misunderstandings that can be avoided when adopting a shared framework. The framework she proposed is not in its final stage yet, but very much worth further investigations. The last general talk was given by Catherine Breslin who is interested in the morphology of neurons and proposed a technique for investigating how the form of a cell affects its function. AUDITORY SYSTEMS The sessions on auditory systems mainly featured talks on techniques, inspired from biology, for detecting directionality of sound sources with a silicon pair of ears. The hardware solutions are particularly interesting when one needs cheap and robust devices that use little power. A drawback is that bio-inspired systems are usually very task-specific. BNVKI newsletter 143