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Because of the environments in which they will operate, future autonomous systems must be capable of reconfiguring quickly and safely following faults or environmental changes. Past research has shown how, by considering autonomous systems to perform phased missions, reliability analysis can support decision making by allowing comparison of the probability of success of different missions following reconfiguration. Binary decision diagrams (BDDs) offer fast, accurate reliability analysis that could contribute to real-time decision making. However, phased mission analysis using existing BDD models is too slow to contribute to the instant decisions needed in time-critical situations. This paper investigates 2 aspects of BDD models that affect analysis speed: variable ordering and quantification efficiency. Variable ordering affects BDD size, which directly affects analysis speed. Here, a new ordering scheme is proposed for use in the context of a decision-making process. Variables are ordered before a mission, and reordering is unnecessary no matter how the mission configuration changes. Three BDD models are proposed to address the efficiency and accuracy of existing models. The advantages of the developed ordering scheme and BDD models are demonstrated in the context of their application within a reliability analysis methodology used to support decision making in an unmanned aerial vehicle. [ABSTRACT FROM AUTHOR]

Dental trauma in children and adolescents is a common problem, and the prevalence of these injuries has increased in the last 10-20 years. A dental injury should always be considered an emergency and, thus, be treated immediately to relieve pain, facilitate reduction of displaced teeth, reconstruct lost hard tissue, and improve prognosis. Rational therapy depends upon a correct diagnosis, which can be achieved with the aid of various examination techniques. It must be understood that an incomplete examination can lead to inaccurate diagnosis and less successful treatment. Good knowledge of traumatology and models of treatments can also reduce stress and anxiety for both the patient and the dental team. Knowledge-based Systems (KBS) are a practical implementation of Artificial Intelligence. In complex domains which humans find difficult to understand, KBS can assist in making decisions and can also add knowledge. The aim of this paper is to describe the structure of a knowledge-based system for structured examination, diagnosis and therapy for traumatised primary and permanent teeth. A commercially available program was used as developmental tool for the programming (XpertRule, Attar, London, UK). The paper presents a model for a computerised decision support system for traumatology. [ABSTRACT FROM AUTHOR]