The Influence of Cockpit Weather Automation on Pilot Perception and Decision-Making in Severe Weather Conditions

This research examines situations in which a pilot either chooses to use, or refrains from using weather-related automation systems, and how the presence of such systems influences a pilot’s decision-making, performance and ability to perceive danger in severe weather conditions. Results indicate that the influence of automation on a pilot’s perception and decision-making process is dependent upon the pilot’s ability to perform manual flight tasks, independent of the automation. Pilots are more likely to continue flight into severe weather conditions and less likely to identify hazardous weather changes when an imbalance exists between a pilot’s flight experience, confidence in ability to manually operate the aircraft, and reliance on automation systems

Visualizations for an Explainable Planning Agent

In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making. Imposing transparency and explainability requirements on such agents is especially important in order to establish trust and common ground with the end-to-end automated planning system. Visualizing the agent's internal decision-making processes is a crucial step towards achieving this. This may include externalizing the "brain" of the agent -- starting from its sensory inputs, to progressively higher order decisions made by it in order to drive its planning components. We also show how the planner can bootstrap on the latest techniques in explainable planning to cast plan visualization as a plan explanation problem, and thus provide concise model-based visualization of its plans. We demonstrate these functionalities in the context of the automated planning components of a smart assistant in an instrumented meeting space.Comment: PREVIOUSLY Mr. Jones -- Towards a Proactive Smart Room Orchestrator (appeared in AAAI 2017 Fall Symposium on Human-Agent Groups

An analysis of the application of AI to the development of intelligent aids for flight crew tasks

This report presents the results of a study aimed at developing a basis for applying artificial intelligence to the flight deck environment of commercial transport aircraft. In particular, the study was comprised of four tasks: (1) analysis of flight crew tasks, (2) survey of the state-of-the-art of relevant artificial intelligence areas, (3) identification of human factors issues relevant to intelligent cockpit aids, and (4) identification of artificial intelligence areas requiring further research

Flight crew aiding for recovery from subsystem failures

Some of the conceptual issues associated with pilot aiding systems are discussed and an implementation of one component of such an aiding system is described. It is essential that the format and content of the information the aiding system presents to the crew be compatible with the crew's mental models of the task. It is proposed that in order to cooperate effectively, both the aiding system and the flight crew should have consistent information processing models, especially at the point of interface. A general information processing strategy, developed by Rasmussen, was selected to serve as the bridge between the human and aiding system's information processes. The development and implementation of a model-based situation assessment and response generation system for commercial transport aircraft are described. The current implementation is a prototype which concentrates on engine and control surface failure situations and consequent flight emergencies. The aiding system, termed Recovery Recommendation System (RECORS), uses a causal model of the relevant subset of the flight domain to simulate the effects of these failures and to generate appropriate responses, given the current aircraft state and the constraints of the current flight phase. Since detailed information about the aircraft state may not always be available, the model represents the domain at varying levels of abstraction and uses the less detailed abstraction levels to make inferences when exact information is not available. The structure of this model is described in detail

An evaluation of NASA's program in human factors research: Aircrew-vehicle system interaction

Research in human factors in the aircraft cockpit and a proposed program augmentation were reviewed. The dramatic growth of microprocessor technology makes it entirely feasible to automate increasingly more functions in the aircraft cockpit; the promise of improved vehicle performance, efficiency, and safety through automation makes highly automated flight inevitable. An organized data base and validated methodology for predicting the effects of automation on human performance and thus on safety are lacking and without such a data base and validated methodology for analyzing human performance, increased automation may introduce new risks. Efforts should be concentrated on developing methods and techniques for analyzing man machine interactions, including human workload and prediction of performance

Experts and Decision Making: First Steps Towards a Unifying Theory of Decision Making in Novices, Intermediates and Experts

Expertise research shows quite ambiguous results on the abilities of experts in judgment and decision making (JDM) classic models cannot account for. This problem becomes even more accentuated if different levels of expertise are considered. We argue that parallel constraint satisfaction models (PCS) might be a useful base to understand the processes underlying expert JDM and the hitherto existing, differentiated results from expertise research. It is outlined how expertise might influence model parameters and mental representations according to PCS. It is discussed how this differential impact of expertise on model parameters relates to empirical results showing quite different courses in the development of expertise; allowing, for example, to predict under which conditions intermediates might outperform experts. Methodological requirements for testing the proposed unifying theory under complex real-world conditions are discussed.Judgment and Decision Making, Expertise, Intermediate Effects, Parallel Constraint Satisfaction, Mental Representation

Expanding the prevention armamentarium portfolio: A framework for promoting HIV-Conversant Communities within a complex, adaptive epidemiological landscape

The article describes a design journey that culminated in an HIV-Conversant Community Framework that is now being piloted in the Limpopo Province of South Africa. The objective of the initiative is to reduce the aggregate community viral load by building capacity at multiple scales that strengthens peoples’ HIV-related navigational skill sets—while simultaneously opening a ‘chronic situation’ schema. The framework design is based upon a transdisciplinary methodological combination that synthesises ideas and constructs from complexity science and the management sciences as a vehicle through which to re-conceptualise HIV prevention. This resulted in a prototype that included the following constructs: managing HIV-prevention in a complex, adaptive epidemiological landscape; problematising and increasing the scope of the HIV knowledge armamentarium through education that focuses on the viral load and Langerhans cells; disruptive innovation and safe-fail probes followed by the facilitation of path creations and pattern management implementation techniques. These constructs are underpinned by a ‘middle-ground’ prevention approach which is designed to bridge the prevention ‘fault line’, enabling a multi-ontology conceptualisation of the challenge to be developed. The article concludes that stepping outside of the ‘ordered’ epistemological parameters of the existing prevention ‘messaging’ mind-set towards a more systemic approach that emphasises agency, structure and social practices as a contribution to ‘ending AIDS by 2030’ is worthy of further attention if communities are to engage more adaptively with the dynamic HIV landscape in South Africa.Keywords: chronic schemas, complex adaptive epidemiological landscape, disruptive innovation, pattern management, safe-fail probe

An Aviation Weather Preflight Decision Support Tool to Improve Ga Pilots Preflight and Inflight Performance

Low hour, inexperienced General Aviation (GA) pilots account for the majority of weather-related incidents, which often result in fatalities. Previous research identifies poor preflight planning practices and a lack of aviation weather knowledge as key contributing factors to the high novice private pilot accident and fatality rate. Research invested into resolving these issues often attempt to introduce new inflight weather technology to assist pilots with weather avoidance. However, these interventions usually result in pilots using the information to strategically navigate closer to degraded weather conditions (Beringer & Ball, 2004; Burgess & Thomas, 2004). Therefore, the purpose of this study was to investigate the effect of a performance support tool for weather preflight (PWDST) on pilots’ preflight performance and inflight performance. Seventy-eight private pilots (Mage = 20.15, SD = 2.56) without instrument ratings were recruited from a Southeastern US university. Forty-one visual flight rule (VFR) private pilots were randomly assigned to the control group (no preflight decision tool) and 37 VFR private pilots were assigned to the experimental group (preflight decision tool). Participants performed a weather preflight and a simulated flight for one VFR into instrument meteorological conditions scenario (i.e., VFR to IMC). Results indicated that participants in the PWDST condition examined significantly more weather products and reported higher weather awareness following the preflight activities than did participants in the control group. Furthermore, results also indicated that participants in the PWDST condition spent significantly less time in IMC than participants in the control condition. Additionally, results revealed that preflight decision-making was predicted by preflight performance and inflight decision-making was predicted by pilots’ awareness of weather inflight