| Knowledge-Based Systems (KBSs) that operate in the real-world must reason about their actions from information that is inherently uncertain, imprecise, and occasionally incorrect. Consequently, control-related information can be viewed as imperfect evidence that can potentially support or refute hypotheses about which actions are the most appropriate to pursue. Moreover, previous research has not thoroughly exploited the fact that knowledge about the degree to which evidence is certain, precise, and correct can significantly influence the choice of any alternative. It follows that the ability to make effective decisions is critically dependent upon the underlying technologies and knowledge that is brought to bear upon the task of choosing a course of action on the basis of evidential information.;This dissertation, therefore, is concerned with the problem of developing an automated approach to reasoning about control that is well suited for real-world situations. The Dempster-Shafer (DS) theory of evidence is the mathematical foundation of an evidential reasoning (ER) technology upon which our proposed approach is based. Control-related evidence is derived from evidential measures such as ignorance, ambiguity, and dissonance that reflect the certainty, precision, and accuracy of domain knowledge and hypotheses of interest. These and other measures were also used to help characterize and reason about the current state and problem solving capabilities of a KBS. Dempster's rule is used to form a consensus opinion about the truth of hypotheses that the evidence directly impacts. An inference engine is used to infer the truth and falsity of the remaining dependent hypotheses, thus, inference-based control is a core paradigm in our approach. Evidential decision measures such as decisiveness were developed to help choose an action based upon the results of the inference process.;A high-level computer vision KBS called OCULUS was built as a testbed for conducting a large number of control experiments. The results demonstrate that an evidential approach to control can significantly improve the system's ability to correctly interpret images by as much as 30%, and in most cases with less than a 10% increase in effort. They also suggest that the domain independent control strategies that were developed and used by the system can be very effective in other real-world task domains. |
