Although it has contributed to remarkable improvements in some specific areas, attempts to develop a universal design theory are generally characterized by failure. This paper sketches arguments for a new approach to engineering design based on Semiotics - the science about signs. The approach is to combine different design theories over all the product life cycle stages into one coherent and traceable framework. Besides, it is to bring together the designer's and user's understandings of the notion of 'good product'. Building on the insight from natural sciences that complex systems always exhibit a self-organizing meaning-influential hierarchical dynamics, objective laws controlling product development are found through an examination of design as a semiosis process. These laws are then applied to support evolutionary design of products. An experiment validating some of the theoretical findings is outlined, and concluding remarks are given.

In Dempster-Shafer belief theory, general beliefs are expressed as belief mass distribution functions over frames of discernment. In Subjective Logic beliefs are expressed as belief mass distribution functions over binary frames of discernment. Belief representations in Subjective Logic, which are called opinions, also contain a base rate parameter which express the a priori belief in the absence of evidence. Philosophically, beliefs are quantitative representations of evidence as perceived by humans or by other intelligent agents. The basic operators of classical probability calculus, such as addition and multiplication, can be applied to opinions, thereby making belief calculus practical. Through the equivalence between opinions and Beta probability density functions, this also provides a calculus for Beta probability density functions. This article explains the basic elements of belief calculus.

Although a number of Computer Aided Process Planni ng (CAPP) systems have been implemented, human planners are still irreplaceable for actual manufacturing. Because process planning requires multiple types of human expertise, there is a common trend to apply knowledge-based techniques for solving the process planning tasks. This circumstance is conducive to developing so-called CAPP Expert Systems (CAPPES). A few approaches to building CAPPES can be found through means-aids analysis of the research literature since 1980. At the same time, it can be seen that authors' efforts in those papers have mostly been made in special cases of CA PPES implementation, whereas the problem of "How to develop CAPPES" on the whole is still open. Se veral general conceptions and methodologies for CAPP have been published, but no fairly versatile technology is yet known. The aim of the paper is to consider the us age of logical models for development of a CAPPES building technology.

We propose a new measure of support (the number of occur- rences of a pattern), in which instances are more important if they occur with a certain frequency and close after each other in the stream of trans- actions. We will explain this new consecutive support and discuss how patterns can be found faster by pruning the search space, for instance using so-called parent support recalculation. Both consecutiveness and the notion of hypercliques are incorporated into the Eclat algorithm. Synthetic examples show how interesting phenomena can now be discov- ered in the datasets. The new measure can be applied in many areas, ranging from bio-informatics to trade, supermarkets, and even law en- forcement. E.g., in bio-informatics it is important to find patterns con- tained in many individuals, where patterns close together in one chro- mosome are more significant.

The ever higher complexity of manufacturing systems, continually shortening life cycles of products and their increasing variety, as well as the unstable market situation of the recent years require introducing grater flexibility and responsiveness to manufacturing processes. From this perspective, one of the critical manufacturing tasks, which traditionally attract significant attention in both academia and the industry, but which have no satisfactory universal solution, is production scheduling. This paper proposes an approach based on genetics-based machine learning (GBML) to treat the problem of flow shop scheduling. By the approach, a set of scheduling rules is represented as an individual of genetic algorithms, and the fitness of the individual is estimated based on the makespan of the schedule generated by using the rule-set. A concept of the interactive software environment consisting of a simulator and a GBML simulation engine is introduced to support human decision-making during scheduling. A pilot study is underway to evaluate the performance of the GBML technique in comparison with other methods (such as Johnson's algorithm and simulated annealing) while completing test examples.

The research results described are concerned with: - developing a domain modeling method and tools to provide the design and implementation of decision-making support systems for computer integrated manufacturing; - building a decision-making support system based on know-how and its software environment. The research is funded by NEDO, Japan.

In the late 90th, the complex of concepts, theories, technologies and software called knowledge-based systems has become a key point in development of many future-oriented manufacturing paradigms, such as Agile Manufacturing and Intelligent Manufacturing Systems. Besides, the progress from craft production, to automated and flexible production and to wards'next generation' production is now realized to be in many respects determined by the human/systems ability to handle the domain knowledge rather than simply by a given standard of knowledge in the domain. This is the motivation for a continuously growing research interest to utilization of manufacturing knowledge. It should be noted however, that while a great many reports on different theoretical and applied aspects of knowle dge utilization have been published, the issues of the specificity of manufacturing knowledge and the appropriateness of the methodologies brought into manufacturing from other domains to build knowledge-based systems have not been given due attention. One instance of this research lack is given in this paper with the phenomenon of know-how. It was discovered rather long ago that know-how plays an important role during the solving of professional tasks in manufacturing (e.g.

We propose a notion of a stable partition in a coalitional game that is parametrized by the concept of a defection function. This function assigns to each partition of the grand coalition a set of different coalition arrangements for a group of defecting players. The alternatives are compared using their social welfare. We characterize the stability of a partition for a number of most natural defection functions and investigate whether and how so defined stable partitions can be reached from any initial partition by means of simple transformations. The approach is illustrated by analyzing an example in which a set of stores seeks an optimal transportation arrangement.

Predictive learning has traditionally been a standard indu ctive learning, where different sub-problem formulations have been identified. One of the most re presentative is classification, consisting on the estimation of a mapping from the feature sp ace into a finite class space. Depending on the cardinality of the finite class space we are l eft with binary or multiclass classification problems. Finally, the presence or absence o r a "natural" order among classes will separate nominal from ordinal problems. Although two-class and nominal classification problems hav e been dissected in the literature, the ordinal sibling has not yet received a lot of attention, e ven with many learning problems involving classifying examples into classes which have a na tural order. Scenarios in which it is natural to rank instances occur in many fields, such as info rmation retrieval, collaborative filtering, econometric modeling and natural sciences. Conventional methods for nominal classes or for regression problems could be employed to solve ordinal data problems; however, the use of techniques designed specifically for ordered classes yields simpler classifiers, making it easier to inte rpret the factors that are being used to discriminate among classes, and generalises better. Alt hough the ordinal formulation seems conceptually simpler than nominal, some technical di fficulties to incorporate in the algorithms this piece of additional information - the order - may explain the widespread use of conventional methods to tackle the ordinal data problem. This dissertation addresses this void by proposing a nonpar ametric procedure for the classification of ordinal data based on the extension of the original dataset with additional variables, reducing the classification task to the well-known two-clas s problem.

This paper investigates the concept of digital city. First, a functional analysis of a digital city is made in the light of the modern study of urbanism; similarities between the virtual and urban constructions are pointed out. Next, a semiotic perspective on the subject matter is elaborated, and a terminological basis is introduced to treat a digital city as a self-organizing meaning-producing system intended to support social or spatial navigation. An explicit definition of a digital city is formulated. Finally, the proposed approach is discussed, conclusions are given, and future work is outlined.