Abstracts:

Branko Soucek
STAR SERVICE S. p. A., Via Amondola 162/1, 70126 Bari, Italy

The evoked potentials have been generated in response to auditory stimuli to a person, and light stimuli to inseCTS, resulting in two datasets, HUMAN and INSECT. In both datasets the responses are composed of several peaks with variable latencies. The brain-window logic is used to explain the evoked responses. Brain-windows are generated through mutual coupling of biological oscillators, and modulated by the memory that stores the past history and the present behavior. Latencies of the peaks provide necessary information to discriminate between normal subject and pathological states resulting from injury, tumor or multiple sclerosis. The holographic neural network classifies the subjeCTS, based on the peak latencies. Combining brain-window theory with the holographic learning opens new possibilities for neurological diagnoses, as well for a new kind of fuzzy neural networks.(pp. 109-114)

Keywords: Brain-windows, evoked potentials, holographic learning, diagnoses, neurology

Miroslav Kubat
Ludwig-Boltzmann Institute of Medical Informatics and Neuroinformatics, Department of Medical Informatics of Biomedical Engineering, Graz University of Technology, Brockmanngasse 41, A-8010 Graz, Austria; mirek@dpmi.tu-graz.ac.at
Simon Parsons
Advanced Computation Laboratory, Imperial Cancer Research Fund, P. O. Box 123, Lincoln's Inn Fields, London WC2A 3PX, United Kingdom AND Department of Electronic Engineering, Queen Mary and Westfeild College, Mile End Road, London E1 4NS, United Kingdom; sp@acl.lif.icnet.uk

This paper is concerned with establishing a common language that can be use to communicate between the different members of a multi-agent system. We suggest that this may be done by successively approximating the concepts that each agent in the system deals with, and the paper gives algorithms which make this possible. Along the way we introduce the notion of a description language cut, or dl-cut, which is an abstraction to which a rich class of languages may be mapped. The idea of a dl-cut is the used to introduce rough concepts---rough descriptions of the concepts used by the agents. Finally we discuss the way in which rough concepts can be logically combined and used in deductive reasoning, also debating the scope of the validity of inferences using the concepts.(pp. 115-129)

Keywords: Artificial Intelligence, abstraction, granularity of knowledge, dl-cut, rough concepts

Vladmir A. Formichov
Moscow State Institute of Electronics and Mathematics (Technical University), Moscow State University, Bolshoj Vuzovsky pereulok, 3/12, 109028 Moscow, Russia; root@onti.miem.msk.su
Olga S. Fomichova
Moscow Children and Teenagers Place for Creative Work, Russia

Important advancements in the theory of teaching and practical teaching young children foreign languages are described and discussed in a broad context of finding the most effective ways of conveying information in various areas of human activity. It is reported about the development of a new theory of teaching called the theory of dynamic conceptual mappings (the DCM-theory). The basic principles of the DCM-dynamic conceptual theory are set forth, and its composition is shortly described. The DCM-theory may be characterized as a theory of bridging gaps between conceptual systems of a teacher and a learner. It became the ground for creating new, highly effective methods of teaching young children and teenagers to read and communicate in English. These methods are called the methods of emotionally-imaginative teaching (the EIT-methods) and are based on ideas of artificial intelligence and cognitive science.(pp. 130-148)

Keywords: artificial intelligence, cognitive science, theory of teaching, dynamic conceptual mapping, teaching children foreign languages, emotionally-imaginative teaching English, intelligent tutoring system, knowledge archives

Anton P. Zeleznickar
Volariceva ulica 8, 61111 Ljubljana, Slovenia; anton.p.zeleznikar@ijs.si

In this paper- the phenomenon of informational Being-in, that is, includedness is studied in a formally recursive (informational) way, dealing with basic definitions of includedness (informational involvement em-bedding) and their consequences. It seems that the information includedness is a phenomenon of informational entities, which involves them in a perplexedly recursive way and offers the richness of the infomationally spontaneous parallelism, serilism, and circularity. In this respect, together with its informational openness and recursiveness, informational Being-in can come semantically as close as possible to its philosophical notion (concept) [2, 1]. Some includable structured phenomena of inference or reasoning (deduction, induction, abduction modus ponens, tollens, rectus, and obliquus) are shown in a formal manner. The disposed formal apparatus enables an unbounded and even deepened philosophical investigation of the phenomenon of Being-in and its consequences. So, a formalistic investigation of informational Being-in can enrich its philosophicalunderstanding.(pp. 149-173)

Keywords: abduction, Being-in-the-world, circularity, decomposition, deduction, externalism,informational includedness (involvement, embedding), induction, inference, information modi (ponens, tollens, rectus, obliquus), internalism, metaphysicalism, parallelism, phenomenalism, reasoning, serialism

Joze Nemec
University of Maribor, College of Agriculture, 62000 MAribor, Vrbanska 30, Slovenia; joze.nemec@uni-mb.si
Janez Grad
University of Ljubljana, Faculty of Economics, 61109 Ljubljana, Slovenia

Determination of higher order normal forms for a relational database (RDB) is frequently a time-consuming process. We can solve this problem by applying graph theory. In the paper the necessary characteristics of graphs that represent a given RDB are analyzed. The connectivity matrices for these graphs and the properties the must satisfy are also introduced and discussed. The established RDB graphs and the corresponding relationship matrices form an important basis of the algorithm for designing RDB with no redundant relations.(pp. 175-182)

Keywords: relational database, relations, normal forms, normalization process, DB graphs, matrices

Igor Kononenko
University of Ljubljana, Faculty of electrical engineering & computer science, Trzaska 25, SI-61001 Ljubljana, Slovenia; igor.kononenko@ninurta.fer.uni-lj.si

In the paper the contribution of the work on Bayesian neural networks is discussed with respect to previous, current, and potential future research in machine learning. The discrete and the continuos Bayesian neural network model is compared with Hopfield'smodels. It is shown that the Bayesian neural network's equations are analogous to equations used to describe Hopfield's model, one based on Shannon's entropy (probability) and the other based on Good's plausibility (odds). A generalization of the Naive-Bayesian classifier is described that enable the basic algorithm to detect the dependencies between neurons.(pp. 183-195)

Keywords: Bayesian neural network, Hopefield's neural network, naive Bayesian classifier, continuous neural network, probability, entropy, machine learning, artificial intelligence, overview

Xindong Wu
Department of Computer Science, James Cook University, Townsville, QLD 4811, Australia ; xindong@cs.jcu.edu.au

"Machine learning is a major area in artificial intelligence (AI), and has seen sustained research and a growing presence in lecture syllabuses over recent years. It has been commonly recognized as a feasible solution to the so called knowledge bottleneck problem in transforming knowledge form human experts to knowledge-based systems. Also, as learning is the essence of human intelligence, only when we have computer systems that can learn can we have real AI. Researchers have devised quite a few sound and efficient learning algorithms (such as ID3 and HCV); a number of universities have opened machine learning courses in their AI-related undergraduate and/or Master of Science programs. This document contains a compressed set of lecture notes designed for the Machine Learning in our Advanced Artificial Intelligence course at James Cook University. They are biased towards exposition of the practical symbolic approaches only."(pp. 197-218)

Keywords: Artificial intelligence, machine learning, symbolic approaches, educational paper

David A. Spuler
Department of Computer Science, James Cook University, Townsville, QLD 4811 Australia
A. Sayed Muhammed Sajeev
Department of Software Development, Monash University,Caulfield East, VIC 3145 Australia

The determination of whether an operation in a procedural language such as Pascal causes side effects has important applications in static error detection, program optimization, program verification and other software tool issues. For simple operations, such as assignment, there is obviously a side effect. However, to detect whether a function call causes a side effect, the body of the function must be analyzed. A function call can produce a side effect in a number of ways, such as: modifying one of its pass-by-reference arguments, producing output, consuming input etc. This paper explains the algorithms and data structures used to determine if a function call causes a side effect.(pp. 219-227)

Keywords: Side effect, function calls, static analysis

Libero Nigro
Dipartmento di Elettronica, Informatica e Sistemistica, Universita' della Calabria, I-87036 Rende (CS) - Italy; nigro@ccusc1.unical.it
Giuseppe Veneziano
Dipartmento di Elettronica, Informatica e Sistemistica, Universita' della Calabria, I-87036 Rende (CS) - Italy

This paper shows that Modula-2 extended with general control abstraction called a thread, supports the construction of programmer-defined control modules. As an example, a realistic control regime providing advanced backtracking is presented.(pp. 229-243)

Keywords: Control abstractions, Modula-2, reusable modules, backtracking, simulation