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

Massively parallel reasoning in transitive relationship hierarchies

This research focuses on building a parallel knowledge representation and reasoning system for the purpose of making progress in realizing human-like intelligence. To achieve human-like intelligence, it is necessary to model human reasoning processes by programs. Knowledge in the real world is huge in size, complex in structure, and is also constantly changing even in limited domains. Unfortunately, reasoning algorithms are very often intractable, which means that they are too slow for any practical applications. One technique to deal with this problem is to design special-purpose reasoners. Many past Al systems have worked rather nicely for limited problem sizes, but attempts to extend them to realistic subsets of world knowledge have led to difficulties. Even special purpose reasoners are not immune to this impasse. In this work, to overcome this problem, we are combining special purpose reasoners with massive We have developed and implemented a massively parallel transitive closure reasoner, called Hydra, that can dynamically assimilate any transitive, binary relation and efficiently answer queries using the transitive closure of all those relations. Within certain limitations, we achieve constant-time responses for transitive closure queries. Hydra can dynamically insert new concepts or new links into a. knowledge base for realistic problem sizes. To get near human-like reasoning capabilities requires the possibility of dynamic updates of the transitive relation hierarchies. Our incremental, massively parallel, update algorithms can achieve almost constant time updates of large knowledge bases. Hydra expands the boundaries of Knowledge Representation and Reasoning in a number of different directions: (1) Hydra improves the representational power of current systems. We have developed a set-based representation for class hierarchies that makes it easy to represent class hierarchies on arrays of processors. Furthermore, we have developed and implemented two methods for mapping this set-based representation onto the processor space of a Connection Machine. These two representations, the Grid Representation and the Double Strand Representation successively improve transitive closure reasoning in terms of speed and processor utilization. (2) Hydra allows fast rerieval and dynamic update of a large knowledge base. New fast update algorithms are formulated to dynamically insert new concepts or new relations into a knowledge base of thousands of nodes. (3) Hydra provides reasoning based on mixed hierarchical representations. We have designed representational tools and massively parallel reasoning algorithms to model reasoning in combined IS-A, Part-of, and Contained-in hierarchies. (4) Hydra\u27s reasoning facilities have been successfully applied to the Medical Entities Dictionary, a large medical vocabulary of Columbia Presbyterian Medical Center. As a result of (1) - (3), Hydra is more general than many current special-purpose reasoners, faster than currently existing general-purpose reasoners, and its knowledge base can be updated dynamically

3D Object Recognition Based On Constrained 2D Views

The aim of the present work was to build a novel 3D object recognition system capable of classifying man-made and natural objects based on single 2D views. The approach to this problem has been one motivated by recent theories on biological vision and multiresolution analysis. The project's objectives were the implementation of a system that is able to deal with simple 3D scenes and constitutes an engineering solution to the problem of 3D object recognition, allowing the proposed recognition system to operate in a practically acceptable time frame. The developed system takes further the work on automatic classification of marine phytoplank- (ons, carried out at the Centre for Intelligent Systems, University of Plymouth. The thesis discusses the main theoretical issues that prompted the fundamental system design options. The principles and the implementation of the coarse data channels used in the system are described. A new multiresolution representation of 2D views is presented, which provides the classifier module of the system with coarse-coded descriptions of the scale-space distribution of potentially interesting features. A multiresolution analysis-based mechanism is proposed, which directs the system's attention towards potentially salient features. Unsupervised similarity-based feature grouping is introduced, which is used in coarse data channels to yield feature signatures that are not spatially coherent and provide the classifier module with salient descriptions of object views. A simple texture descriptor is described, which is based on properties of a special wavelet transform. The system has been tested on computer-generated and natural image data sets, in conditions where the inter-object similarity was monitored and quantitatively assessed by human subjects, or the analysed objects were very similar and their discrimination constituted a difficult task even for human experts. The validity of the above described approaches has been proven. The studies conducted with various statistical and artificial neural network-based classifiers have shown that the system is able to perform well in all of the above mentioned situations. These investigations also made possible to take further and generalise a number of important conclusions drawn during previous work carried out in the field of 2D shape (plankton) recognition, regarding the behaviour of multiple coarse data channels-based pattern recognition systems and various classifier architectures. The system possesses the ability of dealing with difficult field-collected images of objects and the techniques employed by its component modules make possible its extension to the domain of complex multiple-object 3D scene recognition. The system is expected to find immediate applicability in the field of marine biota classification

Idiom treatment experiments in machine translation

Idiomatic expressions pose a particular challenge for the today\u27;s Machine Translation systems, because their translation mostly does not result literally, but logically. The present dissertation shows, how with the help of a corpus, and morphosyntactic rules, such idiomatic expressions can be recognized and finally correctly translated. The work leads the reader in the first chapter generally to the field of Machine Translation and following that, it focuses on the special field of Example-based Machine Translation. Next, an important part of the doctoral thesis dissertation is devoted to the theory of idiomatic expressions. The practical part of the thesis describes how the hybrid Example-based Machine Translation system METIS-II, with the help of morphosyntactic rules, is able to correctly process certain idiomatic expressions and finally, to translate them. The following chapter deals with the function of the transfer system CAT2 and its handling of the idiomatic expressions. The last part of the thesis includes the evaluation of three commercial systems, namely SYSTRAN, T1 Langenscheidt, and Power Translator Pro, with respect to continuous and discontinuous idiomatic expressions. For this, both small corpora and a part of the extensive corpus Europarl and the Digital Lexicon of the German Language in 20th century were processed, firstly manually and then automatically. The dissertation concludes with results from this evaluation.Idiomatische Redewendungen stellen für heutige maschinelle Übersetzungssysteme eine besondere Herausforderung dar, da ihre Übersetzung nicht wörtlich, sondern stets sinngemäß erfolgen muss. Die vorliegende Dissertation zeigt, wie mit Hilfe eines Korpus sowie morphosyntaktischer Regeln solche idiomatische Redewendungen erkannt und am Ende richtig übersetzt werden können. Die Arbeit führt den Leser im ersten Kapitel allgemein in das Gebiet der Maschinellen Übersetzung ein und vertieft im Anschluss daran das Spezialgebiet der Beispielbasierten Maschinellen Übersetzung. Im Folgenden widmet sich ein wesentlicher Teil der Doktorarbeit der Theorie über idiomatische Redewendungen. Der praktische Teil der Arbeit beschreibt wie das hybride Beispielbasierte Maschinelle Übersetzungssystem METIS-II mit Hilfe von morphosyntaktischen Regeln befähigt wurde, bestimmte idiomatische Redewendungen korrekt zu bearbeiten und am Ende zu übersetzen. Das nachfolgende Kapitel behandelt die Funktion des Transfersystems CAT2 und dessen Umgang mit idiomatischen Wendungen. Der letzte Teil der Arbeit beinhaltet die Evaluation von drei kommerzielle Systemen, nämlich SYSTRAN, T1 Langenscheidt und Power Translator Pro, in Bezug auf deren Umgang mit kontinuierlichen und diskontinuierlichen idiomatischen Redewendungen. Hierzu wurden sowohl kleine Korpora als auch ein Teil des umfangreichen Korpus Europarl und des Digatalen Wörterbuchs der deutschen Sprache des 20. Jh. erst manuell und dann maschinell bearbeitet. Die Dissertation wird mit Folgerungen aus der Evaluation abgeschlossen