An open learner model for trainee pilots

This paper investigates the potential for simple open learner models for highly motivated, independent learners, using the example of trainee pilots. In particular we consider whether such users access their learner model to help them identify their current knowledge level, areas of difficulty and specific misconceptions, to help them plan their immediate learning activities; and whether they find a longer‐term planning aid useful. The Flight Club open learner model was deployed in a UK flight school over four weeks. Results suggest that motivated users such as trainee pilots will use a system with a simple open learner model, and are interested in consulting their learner model information both to facilitate planning over time, and to understand their current knowledge state. We discuss the extent to which our findings may be relevant to learners in other contexts

Increasing the Numeric Expressiveness of the Planning Domain Definition Language

The technology of artificial intelligence (AI) planning is being adopted across many different disciplines. This has resulted in the wider use of the Planning Domain Definition Language (PDDL), where it is being used to model planning problems of different natures. One such area where AI planning is particularly attractive is engineering, where the optimisation problems are mathematically rich. The example used throughout this paper is the optimisation (minimisation) of machine tool measurement uncertainty. This planning problem highlights the limits of PDDL's numerical expressiveness in the absence of the square root function. A workaround method using the Babylonian algorithm is then evaluated before the extension of PDDL to include more mathematics functions is discussed

GARDSim - A GPS Receiver Simulation Environment for Integrated Navigation System Development and Analysis

Airservices Australia has recently proposed the use of a Ground-based Regional Augmentation System (GRAS) to improve the safety of using the NAVSTAR Global Positioning System (GPS) in aviation. The GRAS Airborne Receiver Development project (GARD) is being conducted by QUT in conjunction with Airservices Australia and GPSat Systems. The aim of the project is to further enhance the safety and reliability of GPS and GRAS by incorporating smart sensor technology including advanced GPS signal processing and Micro-Electro-Mechanical-Sensor (MEMS) based inertial components. GARDSim is a GPS and GRAS receiver simulation environment which has been developed for algorithm development and analysis in the GARD project. GARDSim is capable of simulating any flight path using a given aeroplane flight model, simulating various GPS, GRAS and inertial system measurements and performing high integrity navigation solutions for the flight. This paper discusses the architecture and capabilities of GARDSim. Simulation results will be presented to demonstrate the usefulness of GARDSim as a simulation environment for algorithm development and evaluation