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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.