Using reliability analysis to support decision making in phased mission systems

Due to 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 two 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

An Autonomous UAV Architecture for Remote Sensing and Intelligent Decision-making

Recently, the US Department of Transportation’s Federal Aviation Administration and other international organizations have proposed a set of requirements for small unmanned aerial vehicles (UAVs) to operate for nonrecreational purposes. However, existing UAV architectures fulfill only some of the established requirements, and not all in one solution. This article presents an event-driven service-oriented architecture that allows autonomous UAVs to satisfy all these requirements and to detect critical situations, performing real-time decision making

Decision-making, tacit knowledge, and motivation in semi-professional practice: Humanizing the environment through anthropomorphism in clinical laboratory science

The clinical laboratory science field requires an abundance of technical knowledge; however, the importance of implicit or tacit knowledge gained through observation and practice is often discounted in this field, even though it is a critical part of reflective thinking, critical thinking, and reflective practice. The “de-skilling” of laboratory practitioners may be a result of limited training opportunities in an overtaxed system. A deeper analysis of the decision-making skills by interviewing practicing medical laboratory scientists in this study may illuminate, for practitioners and the public sector, the complexity of the profession. This study adds to the body of knowledge in clinical laboratory science by specifically observing practitioners for behaviors that reflect the use of specialized technical knowledge in decision-making in the context of the laboratory. In addition, this research provides insight for medicine, nursing, and other allied healthcare disciplines to enhance their processes in the context of clinical training. The study used interview and observation techniques in a phenomenological approach to understand decision-making. A purposeful sample of five medical laboratory science practitioners was obtained. They have an average of 20 years’ experience and varying levels of technical and administrative experience and responsibilities in their current positions. The research question was as follows: How do medical laboratory scientists go about making decisions when confronted with problematic or unique situations in the clinical laboratory? Major findings included balancing the work environment, which contains routine and high-stakes decisions through strategies such as anthropomorphism. The use of anthropomorphism provides a new lens to look at the tension between decision-making as art (as opposed to “science”) for many different “semi-professional” fields. The results provided support that trainers and faculty should allow “gut intuition” to be a legitimate choice for trainees and students. Providing more time in practice for “pause” or reflection, and asking students to listen to their inner voice during problem-solving and express that explicitly in the moment, would build on reflective practice and the motivation to perform during stressful and routine situations

Vista goes online: Decision-analytic systems for real-time decision-making in mission control

The Vista project has centered on the use of decision-theoretic approaches for managing the display of critical information relevant to real-time operations decisions. The Vista-I project originally developed a prototype of these approaches for managing flight control displays in the Space Shuttle Mission Control Center (MCC). The follow-on Vista-II project integrated these approaches in a workstation program which currently is being certified for use in the MCC. To our knowledge, this will be the first application of automated decision-theoretic reasoning techniques for real-time spacecraft operations. We shall describe the development and capabilities of the Vista-II system, and provide an overview of the use of decision-theoretic reasoning techniques to the problems of managing the complexity of flight controller displays. We discuss the relevance of the Vista techniques within the MCC decision-making environment, focusing on the problems of detecting and diagnosing spacecraft electromechanical subsystems component failures with limited information, and the problem of determining what control actions should be taken in high-stakes, time-critical situations in response to a diagnosis performed under uncertainty. Finally, we shall outline our current research directions for follow-on projects

Software agents & human behavior

People make important decisions in emergencies. Often these decisions involve high stakes in terms of lives and property. Bhopal disaster (1984), Piper Alpha disaster (1988), Montara blowout (2009), and explosion on Deepwater Horizon (2010) are a few examples among many industrial incidents. In these incidents, those who were in-charge took critical decisions under various ental stressors such as time, fatigue, and panic. This thesis presents an application of naturalistic decision-making (NDM), which is a recent decision-making theory inspired by experts making decisions in real emergencies. This study develops an intelligent agent model that can be programed to make human-like decisions in emergencies. The agent model has three major components: (1) A spatial learning module, which the agent uses to learn escape routes that are designated routes in a facility for emergency evacuation, (2) a situation recognition module, which is used to recognize or distinguish among evolving emergency situations, and (3) a decision-support module, which exploits modules in (1) and (2), and implements an NDM based decision-logic for producing human-like decisions in emergencies. The spatial learning module comprises a generalized stochastic Petri net-based model of spatial learning. The model classifies routes into five classes based on landmarks, which are objects with salient spatial features. These classes deal with the question of how difficult a landmark turns out to be when an agent observes it the first time during a route traversal. An extension to the spatial learning model is also proposed where the question of how successive route traversals may impact retention of a route in the agent’s memory is investigated. The situation awareness module uses Markov logic network (MLN) to define different offshore emergency situations using First-order Logic (FOL) rules. The purpose of this module is to give the agent the necessary experience of dealing with emergencies. The potential of this module lies in the fact that different training samples can be used to produce agents having different experience or capability to deal with an emergency situation. To demonstrate this fact, two agents were developed and trained using two different sets of empirical observations. The two are found to be different in recognizing the prepare-to-abandon-platform alarm (PAPA ), and similar to each other in recognition of an emergency using other cues. Finally, the decision-support module is proposed as a union of spatial-learning module, situation awareness module, and NDM based decision-logic. The NDM-based decision-logic is inspired by Klein’s (1998) recognition primed decision-making (RPDM) model. The agent’s attitudes related to decision-making as per the RPDM are represented in the form of belief, desire, and intention (BDI). The decision-logic involves recognition of situations based on experience (as proposed in situation-recognition module), and recognition of situations based on classification, where ontological classification is used to guide the agent in cases where the agent’s experience about confronting a situation is inadequate. At the planning stage, the decision-logic exploits the agent’s spatial knowledge (as proposed in spatial-learning module) about the layout of the environment to make adjustments in the course of actions relevant to a decision that has already been made as a by-product of situation recognition. The proposed agent model has potential to be used to improve virtual training environment’s fidelity by adding agents that exhibit human-like intelligence in performing tasks related to emergency evacuation. Notwithstanding, the potential to exploit the basis provided here, in the form of an agent representing human fallibility, should not be ignored for fields like human reliability analysis