Abstracts

Jiri Slechta
Member of the New York Academy of Sciences, 18 Lidgett Hill, Leeds 8, LS8 1PE, U. K.

A quantum-statistical theory of pair interaction memory traces (MTs) in the brain is presented and the basic formulas for its strength are derived. It is shown that the interaction between two memory traces in proportional to the size of the contents of the (free) pieces of information (FPIs) exchanged between them and the number of such exchanges during a given period. The Green function of the propagation of both MTs and the quanta of FPIs exchanged between the two MTs, and their properites, are introduced and studied by means of an elementry Feynman technique. It is shown for example, that the 'blown up' brain cells found in the brains of people suffering from schizophrenia may be caused by a resonance interaction with them within a ring of MTs (a laser of them) storing a piece of too-simple (crystal-like) information.(pp. 109-115)

Keywords: brain, information, informational contents (coupling, difference, quantum), memory traces, pair interaction, quantum statistical theory

Wilhelm Rossak
Systems Integration Laboratory Department of Computer and Information Science New Jersey Institute of Technology University Heights Newwark, NJ 07102, USA; rossak@pluto.njit.edu
Tamar Zemel
Systems Integration Laboratory Department of Computer and Information Science New Jersey Institute of Technology University Heights Newwark, NJ 07102

Domain analysis is proposed as an essential activity for the integrated development of large, complex systems with in an application domain. As an extension of traditional system-analysis methods, it is used a means to assure global,inter-project coordination. Domain analysis provides a universal, comprehensive, and non-constructive domain model. This domain model is used as a common basis for understanding by all developers in the domain and as essential input for the requirements specification phase in each project. Since an application domain is perceived differently by the many entities who have different relations to that domain, we propose building the domain model as an integration of these perceptions. Each perception represents the phenomena of the domain from the viewpoint of a specific group of users, managers, customers, or authorities. To facilitate this type of domain modeling, we propose using a. domain modeling schema (domain schema) that consists of pre-specified element-types (modeling primitiires) for the domain. This domain schema can be specialized and adapted to support capturing different perceptions and (re)integration of all perceptions into one comprehensive domain model. The proposed approach generalizes and extends existing system analysis methods and is compatible with object-oriented concepts.(pp. 117-136)

Keywords: domain analysis, systems integration, software engineering

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

Abstract: Although the history of data base systems research is one otexceptional productivity and startfing economic impact, many advanced applications have revealed deficiencies of the conventional data base management systems (DBMSS) in representing and processing complex objects and knowledge. Object-oriented approaches are currently very popular in processing structurally complex objeCTS, while deductive data bases or logic data bases have been proposed as a solution to those applications where both knowledge and data models are needed. However, it has been characteristic of the current deductive data bases that only actual data is represented explicitly in logic, while the data schema is implicitly described in form of predicates. In this paper, we present a Prolog-based representation. It binds the actual data and data schema together in a natural and fiexible way. In addition to expressing all the information which can be represented in the entity-relationship (E-R) model, the representation can represent other kinds of semantic information as well.(pp. 137-144)

Keywords: knowledge representation, Prolog, deductive databases, semantic information

Jon Kieffer
Interdisciplinary Engineering Program, Australian National University
Ramesh Bale
Interdisciplinary Engineering Program, Australian National University

This paper introduces a class of threelink, two-motor, planar bipeds that have mass centers invariantly-fixed at the hip axis and bodies that serve as reaction wheels. The principle advantage of these bipeds is that, they are governed by exceptionally simple dynamic equations. This paper derives the governing equations for singl-leg support and support leg transfer as well as step-to-step boundary conditions for periodic walking. Closed-form periodic gait trajectories are synthesized which ensure that the body's spin does not build up in the course of periodic walking. Examples show that a realistic model can walk on both flat and inclined surfaces.(pp. 145-155)

Keywords: bipeds, equations, simple dynamics

Dragan Gamberger , Sanja Sekuiak , Aleksandar Sabljic'
Rudjer Boskovic Insittue, P.O.B.1016, 41001 Zagreb, Croatia

In this paper a novel rule-generation system for learning from examples and its application for modelling biodegradation of chemicals are presented. Two rules for biodegradation prediction are generated: the first one for all binary descriptors and a learning set of 48 examples, and the second one with some descriptors extended to integer and fioating-point values and a learning set of 160 examples. The results of prediction of test examples by the generated rules are compared with the measured values and the results of two known models: classical fitting model, based on molecular connectivity indices, and a neural network model. Besides good prediction results, the generated rules have the unique characteristic of pointing out some logical dependencies that might infiuence the better understanding of the biodegradation process.(pp. 157-166)

Keywords: inductive learning, biodegradation

Igor Kononenko
University of Ljubljana, Faculty of Electrical & Computer Engineering, Trzaska 25, 61001 Ljubljana, Slovenia; igor.kononenko@ninurta.fer.uni-ljs.si

The naive Bayesian classifier is fast and incremental can deal with discrete and continuous atttibutes, has excellent performance in real-life problems andcan explain its decisions. as the sum of informational gains. However, its naivety may resuit in poor performance in domains with strong dependencies among attributes. In this paper, thealgorithm of the naive Bayesian classifier is applied successively enabling it to solve also non-linear problems while retaining all advantages of naive Bayes. The comparison of performance in various domains confirms the advantages of successive learning and suggests its application to other learning algorithms.(pp. 167-174)

Keywords: naive Bayesian classifier, successive learning, non-linear problems, empirical learning, empirical evaluation

Ines Sarazin Lovrecic
Health Care Institute of Slovenia, Mikloiiteva 24, 61000 Ljubljana, Slovenia
Janez Grad
Department of Economics, University of Ljubljana, Kardeljeva pl. 17, 61000 Ljubljana, Slovenia

Moral hazard problems in the field of humanitarian health aid delivery can be difficult to solve, especially in outstanding circumstances caused by human or natural factors. In this paper, we present a solution, to this problem by means of preference-ranking methods. The idea of a pseudo-model is also included, where standard input is considered as well as subjective elements.(pp. 175-182)

Keywords: moral hazard, preference ranking, pseudo-model

Koichi Furukawa
Faculty Of Environmental Information, Keio University, 5322 Endo, Fujisawa-shi, Kanagawa, 252 Japan; furukawa@icot.or.jp

In this article, we give a short overview of the FGCS project and describe the research and,development of the sequential inference machinePSI. Then, we present our research results on constraint logic programming. Finally, we discuss our research activities in the field of parallel inference from both hardware and software aspects.(pp. 183-199)

Keywords: concurrent and constraint logic programming, fith generation computer, follow-on project, forecasts, overview, parallel inference system, personal sequential inference machine