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Qualitative representations concentrate on general behaviours rather than numerical accuracy. This thesis introduces methods for producing qualitative descriptions of dynamically changing quantities. A distinction is made between scalar and vector representations of quantities, and several qualitative vector operations are defined, including a qualitative calculus. These operations correspond closely to their normal numerical counterparts. A systematic approach to a model-based method is presented for the analysis of physical systems, which allows the derivation of behaviour for a range of operational conditions. A simple electrical example is used to illustrate completeness of results. The use of qualitatiave reasoning for design support is shown with reference to thermal conditions in a chemical reactor. Qualitative methods are examined in the context of steady-state conditions, instabilities, and potential fault indicators. Application to control problems is illustrated by a system of coupled tanks. Progressively more complex controllers are introduced using different strategies to improve control. The problem of scaling qualitative relationships to external conditions is related to equivalent work in fuzzy logic. Detection of slow trends in system behaviour is shown through a qualitative representation of a car suspension system, which relates changes in component values to changes in system behaviour.

The increasing complexity of systems has made the design task extremely difficult without the help of an expert's knowledge. The major goal of this dissertation is to develop an intelligent software shell, termed the Knowledge-Based Design Support Environment (KBDSE), to facilitate multi-level system design and performance evaluation. KBDSE employs the technique, termed Knowledge Acquisition based on Representation (KAR), for acquiring design knowledge. With KAR, the acquired knowledge is automatically verified and transformed into a hierarchical, entity-based representation scheme, called the Frame and Rule Associated System Entity Structure (FRASES). To increase the efficiency of design reasoning, a Weight-Oriented FRASES Inference Engine (WOFIE) was developed. WOFIE supports different design methodologies (i.e., top-down, bottom-up, and hybrid) and derives all possible alternative design models parallelly. By appropriately setting up the priority of a specialization node, WOFIE is capable of emulating the design reasoning process conducted by a human expert. Design verification is accomplished by computer simulation. To facilitate performance analysis, experimental frames reflecting design objectives are automatically constructed. This automation allows the design model to be verified under various simulation circumstances without wasting labor in programming math-intensive models. Finally, the best design model is recommended by applying Multi-Criteria Decision Making (MCDM) methods on simulation results. Generally speaking, KBDSE offers designers of complex systems a mixed-level design and performance evaluation; knowledge-based design synthesis; lower cost and faster simulation; and multi-criteria design analysis. As with most expert systems, the goal of KBDSE is not to replace the human designers but to serve as an intelligent tool to increase design productivity.

The basic objective of this research is to develop an architecture for systems capable of highly autonomous behavior by combining decision (intelligence), perception (sensory processing), and action (effector) components. The major challenge of this dissertation is the integration of high-level symbolic models with low-level dynamic (control-theoretic) models into a coherent model base. The systematic inclusion of dynamic and symbolic models each dedicated to support a single function such as planning, operations, diagnosis or perception allows us to extend existing multi-layered control and information architectures. A knowledge-based simulation environment is employed to simulate and verify the proposed integrated model-based architecture. The constructed working simulation version of an autonomous robot-managed laboratory demonstrates the use of multiple model families for experiment planning and execution. Tools to support the development and integration of such model families are also developed. The developed model-based architecture is elaborated by incorporating time-based simulation and causal propagation model families supporting diagnosis, repair, and replanning. This involves tools to automatically extract such models from more detailed dynamic models and structural knowledge. Systems with high levels of autonomy are critical for unmanned, and partially manned, space missions. The utility of the proposed high autonomy system will be demonstrated with models of a robot-managed fluid handling laboratory for International Space Station Freedom to be used for research in life sciences, microgravity sciences, and space medicine. NASA engineers will be able to base designs of intelligent controllers for such systems on the architecture developed in this dissertation. They will be able to employ our tools and simulation environment to verify such designs prior to their implementation.

An optimal guidance law for a missile flight is one which determines appropriate controls to produce a flight path such that some mission objective will be achieved in the most efficient manner. Optimal Control Theory is often used to accomplish this task. One must bear in mind, however, that the usefulness of optimal control is sharply divided between two distinct classes of dynamical systems, namely, linear systems and nonlinear systems. For linear systems, the theory is complete in the sense that given a quadratic cost, a closed-loop feedback guidance law may be determined. For nonlinear systems, generally the best one can do is to determine an open-loop guidance law numerically using a software package such as MISER (1). (Some notable exceptions exist where a complete analytical synthesis of the closed-loop control may be obtained for nonlinear systems, e.g., in (2).) Although open-loop optimal guidance laws for nonlinear systems can now be computed quite efficiently with the advances of sophisticated numerical techniques along with high-speed digital computers, the highly-nonlinear and complex dynamics of missiles precludes the possibility of on-line implementation of open-loop optimal control. It has always been realized that if optimal closed-loop solutions could be obtained for comprehensive nonlinear systems such as missiles, then guidance laws based on such results would be superior to any other guidance laws available today. This superiority is due to, among other things, the elimination of some of the restrictive, and in many cases unrealistic assumptions made in the derivation of most current guidance laws in use such as, for instance, "tail-chase", unbounded control, simplified dynamics and/or aerodynamics, and non-maneuvering target, to name a few. In this study, an optimal closed-loop control law is obtained off-line by means of a Neural Network which is then used as an on-line controller for a generic missile. In the nonlinear case, the missile/target scenario is set up as a mathematical model using realistic dynamics. Then, given a Performance Index, the open-loop control is obtained by solving the problem using the optimal control software MISER for a number of different initial configurations. These open-loop solutions are then used to "teach" a neural network via backpropagation. Through simulation, it is then demonstrated how well the neural network performs as a feedback controller. The miss distance as well as the value of the Performance Index are used as measures of performance to be compared under the original open-loop control and the neural network closed-loop control. This problem is further extended to include a time lag in the missile dynamics. The effect of this time delay in the overall performance of the optimal controller is then examined.

This study addressed the free recall and recognition memory processes of elementary school children. It has been discovered that when children recall items from episodically related collections, a non-monotonic relationship is found between the memory strengths of those items and the order in which they are recalled. This relationship is known as cognitive triage, and it is not understood if the same phenomena would occur with recall involving semantic memory. Regarding recognition memory, experiments have tapped children's tendency to falsely remember words whose gist is the same as the gist of newly learned items. These past studies have focused primarily on a reversal of the standard false-recognition effect, where related distractors were easier to reject than unrelated distractors under some conditions. No research to date has ignored reversals and clearly examined the false-recognition effect itself. This study examined kindergarten, third, and sixth grade children's free recall organization and false-recognition of related distractors. The cognitive triage experiment examined semantic memory through having children recall exemplars from categories in Battig and Montague's (1969) lists. The recognition experiment examined developmentally the differential rate of false-recognition for related and unrelated distractors. Fuzzy Trace Theory (FTT) contends that the ability to inhibit interference increases with age. Hence, younger children were hypothesized to show a weaker cognitive triage effect and more false-recognition than older children. The latter result was found, whereas the former result was not. False-recognition did decrease with age but although a triage effect was observed for category exemplar production, the effect did not vary developmentally. The principle difference between this triage study and previous research is that lists offering preexperimental measures of memory strength were employed. These lists were normed on adults and therefore cognitive triage may have been different for children. Thus, this study indicates that developmental effects are found for false-recognition of related distractors but not for category exemplar production when using Battig and Montague's (1969) lists.

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