Typicality, graded membership, and vagueness

This paper addresses theoretical problems arising from the vagueness of language terms, and intuitions of the vagueness of the concepts to which they refer. It is argued that the central intuitions of prototype theory are sufficient to account for both typicality phenomena and psychological intuitions about degrees of membership in vaguely defined classes. The first section explains the importance of the relation between degrees of membership and typicality (or goodness of example) in conceptual categorization. The second and third section address arguments advanced by Osherson and Smith (1997), and Kamp and Partee (1995), that the two notions of degree of membership and typicality must relate to fundamentally different aspects of conceptual representations. A version of prototype theory—the Threshold Model—is proposed to counter these arguments and three possible solutions to the problems of logical selfcontradiction and tautology for vague categorizations are outlined. In the final section graded membership is related to the social construction of conceptual boundaries maintained through language use

Fuzzy expert systems in civil engineering

Imperial Users onl

A web-based collaborative decision making system for construction project teams using fuzzy logic

In the construction industry, the adoption of concurrent engineering principles requires the development of effective enabling IT tools. Such tools need to address specific areas of need in the implementation of concurrent engineering in construction. Collaborative decision-making is an important area in this regard. A review of existing works has shown that none of the existing approaches to collaborative decision-making adequately addresses the needs of distributed construction project teams. The review also reveals that fuzzy logic offers great potential for application to collaborative decision-making. This thesis describes a Web-based collaborative decision-making system for construction project teams using fuzzy logic. Fuzzy logic is applied to tackle uncertainties and imprecision during the decision-making process. The prototype system is designed as Web-based to cope with the difficulty in the case where project team members are geographically distributed and physical meetings are inconvenient/or expensive. The prototype was developed into a Web-based software using Java and allows a virtual meeting to be held within a construction project team via a client-server system. The prototype system also supports objectivity in group decision-making and the approach encapsulated in the prototype system can be used for generic decision-making scenarios. The system implementation revealed that collaborative decision-making within a virtual construction project team can be significantly enhanced by the use of a fuzzybased approach. A generic scenario and a construction scenario were used to evaluate the system and the evaluation confirmed that the system does proffer many benefits in facilitating collaborative decision-making in construction. It is concluded that the prototype decision-making system represents a unique and innovative approach to collaborative decision-making in construction project teams. It not only contributes to the implementation of concurrent engineering in construction, but also it represents a substantial advance over existing approaches

Semantics of fuzzy quantifiers

The aim of this thesis is to discuss the semantics of FQs (fuzzy quantifiers), formal semantics in particular. The approach used is fuzzy semantic based on fuzzy set theory (Zadeh 1965, 1975), i.e. we explore primarily the denotational meaning of FQs represented by membership functions. Some empirical data from both Chinese and English is used for illustration. A distinguishing characteristic of the semantics of FQs like about 200 students and many students as opposed to other sorts of quantifiers like every student and no students, is that they have fuzzy meaning boundaries. There is considerable evidence to suggest that the doctrine that a proposition is either true or false has a limited application in natural languages, which raises a serious question towards any linguistic theories that are based on a binary assumption. In other words, the number of elements in a domain that must satisfy a predicate is not precisety given by an FQ and so a proposition con¬ taining one may be more or less true depending on how closely numbers of elements approximate to a given norm. The most significant conclusion drawn here is that FQs are compositional in that FQs of the same type function in the same way to generate a constant semantic pattern. It is argued that although basic membership functions are subject to modification depending on context, they vary only with certain limits (i.e. FQs are motivated—neither completely predicated nor completely arbitrary), which does not deny compositionality in any way. A distinctive combination of compositionality and motivation of FQs makes my formal semantic framework of FQs unique in the way that although some specific values, such as a norm, have to be determined pragmatically, semantic and inferential patterns are systematic and predictable. A number of interdisciplinary implications, such as semantic, general linguistic, logic and psychological, are discussed. The study here seems to be a somewhat troublesome but potentially important area for developing theories (and machines) capable of dealing with, and accounting for, natural languages

Improving the Analyst and Decision-Maker’s Perspective through Uncertainty Visualization

This thesis constructs the Taxonomy of Uncertainty and an approach for enhancing the information in decision support systems. The hierarchical categorization of numerous causes for uncertainty defines the taxonomy, which fostered the development of a technique for visualizing uncertainty. This technique is fundamental to expressing the multi-dimensional uncertainty that can be associated with any object. By including and intuitively expressing uncertainty, the approach facilitates and enhances intuition and decision-making without undue information overload. The resulting approach for enhancing the information involves recording uncertainty, identifying the relevant items, computing and visualizing uncertainty, and providing interaction with the selection of uncertainty. A prototype embodying this approach to enhancing information by including uncertainty was used to validate these efforts. Evaluation responses of a small sample space support the thesis that the decision-maker\u27s knowledge is enhanced with enlightening information afforded by including and visualizing uncertainty, which can improve the decision-making process. Although the concept was initially conceived to help decision support system users deal with uncertainty, this methodology and these ideas can be applied to any problem where objects with many potential reasons for uncertainty are the focus of the decision-making

Cogitator : a parallel, fuzzy, database-driven expert system

The quest to build anthropomorphic machines has led researchers to focus on knowledge and the manipulation thereof. Recently, the expert system was proposed as a solution, working well in small, well understood domains. However these initial attempts highlighted the tedious process associated with building systems to display intelligence, the most notable being the Knowledge Acquisition Bottleneck. Attempts to circumvent this problem have led researchers to propose the use of machine learning databases as a source of knowledge. Attempts to utilise databases as sources of knowledge has led to the development Database-Driven Expert Systems. Furthermore, it has been ascertained that a requisite for intelligent systems is powerful computation. In response to these problems and proposals, a new type of database-driven expert system, Cogitator is proposed. It is shown to circumvent the Knowledge Acquisition Bottleneck and posess many other advantages over both traditional expert systems and connectionist systems, whilst having non-serious disadvantages.KMBT_22

Optimization and inference under fuzzy numerical constraints

Εκτεταμένη έρευνα έχει γίνει στους τομείς της Ικανοποίησης Περιορισμών με διακριτά (ακέραια) ή πραγματικά πεδία τιμών. Αυτή η έρευνα έχει οδηγήσει σε πολλαπλές σημασιολογικές περιγραφές, πλατφόρμες και συστήματα για την περιγραφή σχετικών προβλημάτων με επαρκείς βελτιστοποιήσεις. Παρά ταύτα, λόγω της ασαφούς φύσης πραγματικών προβλημάτων ή ελλιπούς μας γνώσης για αυτά, η σαφής μοντελοποίηση ενός προβλήματος ικανοποίησης περιορισμών δεν είναι πάντα ένα εύκολο ζήτημα ή ακόμα και η καλύτερη προσέγγιση. Επιπλέον, το πρόβλημα της μοντελοποίησης και επίλυσης ελλιπούς γνώσης είναι ακόμη δυσκολότερο. Επιπροσθέτως, πρακτικές απαιτήσεις μοντελοποίησης και μέθοδοι βελτιστοποίησης του χρόνου αναζήτησης απαιτούν συνήθως ειδικές πληροφορίες για το πεδίο εφαρμογής, καθιστώντας τη δημιουργία ενός γενικότερου πλαισίου βελτιστοποίησης ένα ιδιαίτερα δύσκολο πρόβλημα. Στα πλαίσια αυτής της εργασίας θα μελετήσουμε το πρόβλημα της μοντελοποίησης και αξιοποίησης σαφών, ελλιπών ή ασαφών περιορισμών, καθώς και πιθανές στρατηγικές βελτιστοποίησης. Καθώς τα παραδοσιακά προβλήματα ικανοποίησης περιορισμών λειτουργούν βάσει συγκεκριμένων και προκαθορισμένων κανόνων και σχέσεων, παρουσιάζει ενδιαφέρον η διερεύνηση στρατηγικών και βελτιστοποιήσεων που θα επιτρέπουν το συμπερασμό νέων ή/και αποδοτικότερων περιορισμών. Τέτοιοι επιπρόσθετοι κανόνες θα μπορούσαν να βελτιώσουν τη διαδικασία αναζήτησης μέσω της εφαρμογής αυστηρότερων περιορισμών και περιορισμού του χώρου αναζήτησης ή να προσφέρουν χρήσιμες πληροφορίες στον αναλυτή για τη φύση του προβλήματος που μοντελοποιεί.Extensive research has been done in the areas of Constraint Satisfaction with discrete/integer and real domain ranges. Multiple platforms and systems to deal with these kinds of domains have been developed and appropriately optimized. Nevertheless, due to the incomplete and possibly vague nature of real-life problems, modeling a crisp and adequately strict satisfaction problem may not always be easy or even appropriate. The problem of modeling incomplete knowledge or solving an incomplete/relaxed representation of a problem is a much harder issue to tackle. Additionally, practical modeling requirements and search optimizations require specific domain knowledge in order to be implemented, making the creation of a more generic optimization framework an even harder problem.In this thesis, we will study the problem of modeling and utilizing incomplete and fuzzy constraints, as well as possible optimization strategies. As constraint satisfaction problems usually contain hard-coded constraints based on specific problem and domain knowledge, we will investigate whether strategies and generic heuristics exist for inferring new constraint rules. Additional rules could optimize the search process by implementing stricter constraints and thus pruning the search space or even provide useful insight to the researcher concerning the nature of the investigated problem

The Use of Relation Valued Attributes in Support of Fuzzy Data

In his paper introducing fuzzy sets, L.A. Zadeh describes the difficulty of assigning some real-world objects to a particular class when the notion of class membership is ambiguous. If exact classification is not obvious, most people approximate using intuition and may reach agreement by placing an object in more than one class. Numbers or ‘degrees of membership’ within these classes are used to provide an approximation that supports this intuitive process. This results in a ‘fuzzy set’. This fuzzy set consists any number of ordered pairs to represent both the class and the class’s degree of membership to provide a formal representation that can be used to model this process. Although the fuzzy approach to reasoning and classification makes sense, it does not comply with two of the basic principles of classical logic. These principles are the laws of contradiction and excluded middle. While they play a significant role in logic, it is the violation of these principles that gives fuzzy logic its useful characteristics. The problem of this representation within a database system, however, is that the class and its degree of membership are represented by two separate, but indivisible attributes. Further, this representation may contain any number of such pairs of attributes. While the data for class and membership are maintained in individual attributes, neither of these attributes may exist without the other without sacrificing meaning. And, to maintain a variable number of such pairs within the representation is problematic. C. J. Date suggested a relation valued attribute (RVA) which can not only encapsulate the attributes associated with the fuzzy set and impose constraints on their use, but also provide a relation which may contain any number of such pairs. The goal of this dissertation is to establish a context in which the relational database model can be extended through the implementation of an RVA to support of fuzzy data on an actual system. This goal represents an opportunity to study through application and observation, the use of fuzzy sets to support imprecise and uncertain data using database queries which appropriately adhere to the relational model. The intent is to create a pathway that may extend the support of database applications that need fuzzy logic and/or fuzzy data

Modes of knowledge production: articulating coexistence in UK academic science

The notion of Mode 2, as a shift from Mode 1 science-as-we-know-it, depicts science as practically relevant, socially distributed and democratic. Debates remain over the empirical substantiation of Mode 2. In particular, our understanding has been impeded by the mutually exclusive framing of Mode 1/Mode 2. Looking at how academic science is justified to diverse institutional interests – a situation associated with Mode 2 – it is asked, “What happens to Mode 1 where Mode 2 is in demand?” This study comprises two sequential phases. It combines interviews with 18 university spinout founders as micro-level Mode 2 exemplars, and macro-level policy narratives from 72 expert witnesses examined by select committees. An interpretive scheme (Greenwood and Hinings, 1988) is applied to capture the internal means-ends structure of each mode, where the end is to satisfy demand constituents, both in academia (Mode 1) and beyond (Mode 2). Results indicate Mode 1’s enduring influence even where non-academic demands are concerned, thus refuting that means and ends necessarily operate together as a stable mode. The causal ambiguity inherent in scientific advances necessitates (i) Mode 1 peer review as the only quality control regime systematically applicable ex ante, and (ii) Mode 1 means of knowledge production as essential for the health and diversity of the science base. Modifications to performance criteria are proposed to create a synergy between modes and justify public investment, especially in the absence of immediate outcomes. The study presents a framework of Mode1/Mode 2 coexistence that eases the problem with the either/or perception and renders Mode 2 more amenable to empirical research. It is crucial to note, though, that this is contingent on given vested interests. In this study, Mode 1’s fate is seen through academic scientists whose imperative is unique from those of other constituents, thereby potentially entailing further struggles and negotiation