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There is a trend in manufacturing towards fully automated production facilities in which all operations are integrated by computer based information systems. The current generation of industrial inspection systems lack the necessary flexibility to operate in these environments. AI based Image Understanding systems have the necessary level of generality, achieved through the use of domain specific object models. These models are used to guide early visual processing, and must be supplied to the system. Current theories in cognitive psychology call for a reevaluation of the role of such 'auxiliary' knowledge in early visual processing. Recent work suggests that very general cognitive processes may build up a hierarchical representation of the world. The emphasis is currently on such generic cognitive processes rather than on the use of world knowledge. A novel approach to image processing, in which emphasis is placed on generic low and intermediate level techniques, is proposed in this thesis. This approach, termed the descriptor approach, delays the use of domain specific models until a full description of the image has been produced. A prototype industrial inspection system has been implemented, based on the descriptor approach: the Hierarchical Scene Description (HISD) system. General image features are extracted from images of populated PCBs, and subsequently transformed into a database of prolog facts by an interface subsystem. Finally the intermediate level vision subsystem uses rules to reason about these features, building up a semantic net based description of the scene. HISD successfully builds up hierarchical descriptions of real industrial PCB images in terms of geometric shapes, their coordinates, and spatial relationships between shapes. The results are displayed graphically and are achieved without the use of any object models, thus avoiding the problems of inflexibility and lack of generality associated with more complex model based systems.

With the advent of reasoning problems in dynamic environments, there is an increasing need for automated reasoning systems to automatically adapt to unexpected changes in representations. In particular, the automation of the evolution of their ontologies needs to be enhanced without substantially sacrificing expressivity in the underlying representation. Revision of beliefs is not enough, as adding to or removing from beliefs does not change the underlying formal language. General reasoning systems employed in such environments should also address situations in which the language for representing knowledge is not shared among the involved entities, e.g., the ontologies in a multi-ontology environment or the agents in a multi-agent environment. Our techniques involve diagnosis of faults in existing, possibly heterogeneous, ontologies and then resolution of these faults by manipulating the signature and/or the axioms. This thesis describes the design, development and evaluation of GALILEO (Guided Analysis of Logical Inconsistencies Lead to Evolution of Ontologies), a system designed to detect conflicts in highly expressive ontologies and resolve the detected conflicts by performing appropriate repair operations. The integrated mechanism that handles ontology evolution is able to distinguish between various types of conflicts, each corresponding to a unique kind of ontological fault. We apply and develop our techniques in the domain of Physics. This an excellent domain because many of its seminal advances can be seen as examples of ontology evolution, i.e. changing the way that physicists perceive the world, and case studies are well documented – unlike many other domains. Our research covers analysing a wide ranging development set of case studies and evaluating the performance of the system on a test set. Because the formal representations of most of the case studies are non-trivial and the underlying logic has a high degree of expressivity, we face some tricky technical challenges, including dealing with the potentially large number of choices in diagnosis and repair. In order to enhance the practicality and the manageability of the ontology evolution process, GALILEO incorporates the functionality of generating physically meaningful diagnoses and repairs and, as a result, narrowing the search space to a manageable size.

This thesis deals with a real world problem of an optimal planning of transferring goods between stores of a certain retail company. The aim is to propose a system which, based on customer orders and current stock items availability, should be capable of devising an optimal plan. In this thesis, following a thorough analysis of the problem, there is a formalised problem of automated logistics. After a brief overview of existing approaches in the area of logistics problem solving, there are subsequently designed solution methods based on programming with limited conditions and mixed integer programming. Both methods are compared to each other experimentally, by taking into account the quality of the found solution and the methods' efficiency.

Necessary conditions for stability of coupled autonomous vehicles in R are established in this thesis. The focus is on linear arrays with decentralized vehicles, where each vehicle interacts with only a few of its neighbors. Decentralized means that there is no central authority governing the motion. Instead, each vehicle registers only velocity and position relative to itself and bases its acceleration only on those data. Explicit expressions are obtained for necessary conditions for asymptotic stability in the cases that a system consists of a periodic arrangement of two or three different types of vehicles, i.e. configurations as follows: ...2-1-2-1 or ...3-2-1-3-2-1. Previous literature indicated that the (necessary) condition for stability in the case of a single vehicle type (...1-1-1) held that the first moment of certain coefficients of the interactions between vehicles has to be zero. Here, we show that that does not generalize. Instead, the (necessary) condition in the cases considered is that the first moment plus a nonlinear correction term must be zero.

Sleep scoring involves classification of polysomnographic data into the various sleep stages as defined by Retschaffen and Kales. This process is time-consuming and laborious as it involves experts visually scoring the data. During recent years, there has been an increasing focus on automated sleep scoring systems and professional software programs are finding increased use. However, these systems are not relied on for scoring and are often used as a tool that facilitates easy visual scoring. This thesis proposes a neural network based approach to automatic sleep scoring using LabVIEW. Effort has been made to give the sleep expert more control over key parameters such as the frequency bands, and thus come up with scores that are more in agreement with the individual scorer than being a rigid interpretation of the R&K rules. Though this thesis is limited to the development of an offline software program, given the data acquisition facilites in LabVIEW, a complete system from data acquisition to sleep hypnograms is a fair possibility.

Software systems are ubiquitous in today's world and yet, remain vulnerable to the fallibility of human programmers as well as the unpredictability of their operating environments. The overarching goal of this dissertation is to develop algorithms to enable automated and efficient design and analysis of reliable programs. In the first and second parts of this dissertation, we focus on the development of programs that are free from programming errors. The intent is not to eliminate the human programmer, but instead to complement his or her expertise, with sound and efficient computational techniques, when possible. To this end, we make contributions in two specific domains. Program debugging --- the process of fault localization and error elimination from a program found to be incorrect --- typically relies on expert human intuition and experience, and is often a lengthy, expensive part of the program development cycle. In the first part of the dissertation, we target automated debugging of sequential programs. A broad and informal statement of the (automated) program debugging problem is to suitably modify an erroneous program, say P, to obtain a correct program, say P'. This problem is undecidable in general; it is hard to formalize; moreover, it is particularly challenging to assimilate and mechanize the customized, expert programmer intuition involved in the choices made in manual program debugging. Our first contribution in this domain is a methodical formalization of the program debugging problem, that enables automation, while incorporating expert programmer intuition and intent. Our second contribution is a solution framework that can debug infinite-state, imperative, sequential programs written in higher-level programming languages such as C. Boolean programs, which are smaller, finite-state abstractions of infinite-state or large, finite-state programs, have been found to be tractable for program verification. In this dissertation, we utilize Boolean programs for program debugging. Our solution framework involves two main steps: (a) automated debugging of a Boolean program, corresponding to an erroneous program P, and (b) translation of the corrected Boolean program into a correct program P'. Shared-memory concurrent programs are notoriously difficult to write, verify and debug; this makes them excellent targets for automated program completion, in particular, for synthesis of synchronization code. Extant work in this domain has focused on either propositional temporal logic specifications with simplistic models of concurrent programs, or more refined program models with the specifications limited to just safety properties. Moreover, there has been limited effort in developing adaptable and fully-automatic synthesis frameworks that are capable of generating synchronization at different levels of abstraction and granularity. In the second part of this dissertation, we present a framework for synthesis of synchronization for shared-memory concurrent programs with respect to temporal logic specifications. In particular, given a concurrent program composed of synchronization-free processes, and a temporal logic specification describing their expected concurrent behaviour, we generate synchronized processes such that the resulting concurrent program satisfies the specification. We provide the ability to synthesize readily-implementable synchronization code based on lower-level primitives such as locks and condition variables. We enable synchronization synthesis of finite-state concurrent programs composed of processes that may have local and shared variables, may be straight-line or branching programs, may be ongoing or terminating, and may have program-initialized or user-initialized variables. We also facilitate expression of safety and liveness properties over both control and data variables by proposing an extension of propositional computation tree logic. Most program analyses, verification, debugging and synthesis methodologies target traditional correctness properties such as safety and liveness. These techniques typically do not provide a quantitative measure of the sensitivity of a computational system's behaviour to unpredictability in the operating environment. We propose that the core property of interest in reasoning in the presence of such uncertainty is robustness --- small perturbations to the operating environment do not change the system's observable behavior substantially. In well-established areas such as control theory, robustness has always been a fundamental concern; however, the techniques and results therein are not directly applicable to computational systems with large amounts of discretized, discontinuous behavior. Hence, robustness analysis of software programs used in heterogeneous settings necessitates development of new theoretical frameworks and algorithms. In the third part of this dissertation, we target robustness analysis of two important classes of discrete systems --- string transducers and networked systems of Mealy machines. For each system, we formally define robustness of the system with respect to a specific source of uncertainty. In particular, we analyze the behaviour of transducers in the presence of input perturbations, and the behaviour of networked systems in the presence of channel perturbations. Our overall approach is automata-theoretic, and necessitates the use of specialized distance-tracking automata for tracking various distance metrics between two strings. We present constructions for such automata and use them to develop decision procedures based on reducing the problem of robustness verification of our systems to the problem of checking the emptiness of certain automata. Thus, the system under consideration is robust if and only if the languages of particular automata are empty. / text