Randy Jensen

Demonstration-Based Training: A Review of Instructional Features:

Objective: This article reviews instructional features used in demonstration-based training (DBT). Background: The need for fast and effective training and performance support that can be accessed from anywhere is a growing need for organizations. DBT programs are one method to address these needs, but a better understanding of how to maximize the effectiveness of DBT activities is needed. Specifically, beyond the content of the demonstration (i.e., the dynamic example of task performance), what instructional features (i.e., information and activities in addition to the demonstration) can be used to improve the effectiveness of DBT interventions? Method:The authors conducted a systematic review of the applied and basic science literatures relevant to DBT. Results: Instructional features in DBT can be categorized according to the degree to which they encourage active learner involvement (i.e., active vs. passive), when they occur relative to viewing the demonstration (i.e., pre-, during-, and postdemonstration conditions), and the observational learning process they are intended to augment. Five categories of instructional features are described: passive guidance or support, preparatory activities, concurrent activities, retrospective activities, and prospective activities. Conclusion: There is a wide variety of instructional features used in DBT, but more systematic research is needed to understand the conditions under which each is most effective as well as to outline a method for sequencing of demonstration with other delivery methods, such as practice opportunities. Application: The framework presented in this article can help guide the systematic development of training systems incorporating DBT as well as provide a direction for future research.

Enhancing Unmanned Aerial System Training: A Taxonomy of Knowledge, Skills, Attitudes, and Methods

The burgeoning use of Unmanned Aerial Systems (UASs) has, regrettably, not been met with an appropriate integration of training science into UAS training efforts. At best, current UAS training efforts are fragmented; at worst, they are ineffective (Stulberg, 2007). However, this need not be the case. Though nascent, the UAS literature has identified many necessary knowledge, skill, and attitude (KSA) components of UAS operation. This article works to aid the UAS training community in combining practice with science. In order to accomplish this goal, the emerging UAS knowledge base is collected herein as a taxonomy of KSAs. These KSAs are joined by a listing of training methodologies that can be used to impart them to UAS operator teams. Finally, these KSAs and methods are used to resolve example training deficiencies drawn from the literature.

Evaluating Game Technologies for Training

In recent years, videogame technologies have become more popular for military and government training purposes. There now exists a multitude of technology choices for training developers. Unfortunately, there is no standard set of criteria by which a given technology can be evaluated. In this paper we report on initial steps taken towards the evaluation of technology with respect to training needs. We describe the training process, characterize the space of technology solutions, review a representative sample of platforms, and introduce evaluation criteria.

Bottleneck Avoidance Techniques for Automated Satellite Communication Scheduling

This paper describes Bottleneck Avoidance (BA), a one-pass scheduling technique, and its application to the domain of automated satellite communication scheduling. Optimal scheduling is in a class of computationally complex problems that require creative solutions to be solved in a reasonable amount of time. Satellite communication scheduling is particularly interesting because of a relatively high degree of resource contention and strong emphasis on scheduling all jobs without conflict. Generating a solution in this domain can be separated into two stages: first attempting to honor all constraints and requirements of the incoming requests, then negotiating with users to relax constraints and resolve unavoidable conflicts. The techniques and heuristics described here are intended to aid in the first phase. By carefully selecting the order in which tasks are assigned and then assigning them in a way that reduces resource contention, we can quickly generate a solution that attempts to minimize the number of conflicts and increase overall flexibility.

Creating an AI modeling application for designers and developers

Simulation developers often realize an entitys AI by writing a program that exhibits the intended behavior. These behaviors are often the product of design documents written by designers. These individuals, while possessing a vast knowledge of the subject matter, might not have any programming knowledge whatsoever. To address this disconnect between design and subsequent development, we have created an AI application whereby a designer or developer sketches an entitys AI using a graphical “drag and drop” interface to quickly articulate behavior using a UML-like representation of state charts. Aside from the design-level benefits, the application also features a runtime engine that takes the applications data as input along with a simulation or game interface, and makes the AI operational. We discuss our experience in creating such an application for both designer and developer.

Automated Probabilistic Analysis of Air Traffic Control Communications

Abstract : Initiatives to integrate autonomous Unmanned Aerial Vehicles (UAVs) with regular airport operations require automated onboard situational awareness to maintain safety at all times. More specifically, this requires the capability to sense, interpret, and predict what other aircraft are doing, based on the same incoming data that are available to a human pilot. This includes not only baseline knowledge of the airport layout, operational practices and landmarks, but also an ability to interpret radio communications with Air Traffic Control (ATC) and correlate them with observable movements and positions of other aircraft. This analysis informs an autonomous UAVs control mechanisms which ultimately regulate its kinetic behavior at the airport. As with any operational domain governed by human actions and control, there are many inherent challenges in interpreting ATC communications -- a noisy data stream not only in terms of signal quality, but more significantly in the range of human deviations from the strictest procedures. This makes the analysis a natural application for Artificial Intelligence techniques, where the goal is to support automated reasoning that mimics a human pilots decision processes. This paper provides a detailed discussion of a probabilistic reasoning approach using Bayesian Networks to classify ATC communications and synthesize them with baseline knowledge of an airport and produce real-time hypotheses about the states and trajectories of other aircraft. This provides a key component for automated situational awareness, which also requires correlation with sensor data, and ultimately a functional set of behaviors to act accordingly, although these latter capabilities are beyond the scope of this paper. The probabilistic communications analysis methodology is described, along with testing results using a real-world sample data set annotated for ground truth, to evaluate performance.

ITADS: A Real-World Intelligent Tutor to Train Troubleshooting Skills

Real-world intelligent tutoring systems are important ambassadors for promoting wide adoption of the technology. Questions about affordability, quality control, operational readiness, training effectiveness, and user acceptance are significant in this context. This paper describes ITADS, an intelligent tutor developed to provide a problem-based, experiential learning tool to complement schoolhouse training. The goal was to train US Navy Information Systems Technology support staff in troubleshooting skills through the use of realistic simulations and automated assessment and feedback. This paper describes the tutoring system and a preliminary validation study of its training effectiveness. The results demonstrate that the system is effective in improving troubleshooting knowledge and skills. The ITADS system was successfully developed in twenty-six months from requirements to validation, following strict systems engineering procedures. The results of the training effectiveness study indicate that the ITS also leads to significantly improved performance among Navy IT recruits in troubleshooting tasks.

Predictive Assessment for Phased Array Antenna Scheduling

In contrast to traditional parabolic dish antennas which must be mechanically steered to point at satellites, phased array antennas operate by electronically activating subarrays in configurations that can be maneuvered across the surface of the antenna without any physical movement. This leads to many benefits including an increase in capacity from the fact that multiple active areas can be enabled simultaneously on the same phased array. Under this concept, a single antenna can support multiple simultaneous contacts, although there are still constraints specific to any implementation. The phased array is made up of subarrays with Transmit and Receive modules, which present their own specific limitations. In one implementation, an individual Transmit / Receive module can simultaneously support two Receive beams from two distinct satellites, but only one Transmit beam. As the active areas for separate beams move across the surface of the antenna, the conditions where they overlap may overload specific modules in the overlapping area. Thus when constructing automated logic for allocating supports to antennas, a predictive compatibility assessment mechanism is required to determine if a trial allocation will lead to conditions that violate the constraints of the antenna hardware. When two or more supports will be active on the same phased array, a predictive assessment must consider their active areas and their paths across the surface over time to identify conflicts and then determine if such conflicts can be remedied. A phased array antenna allows active areas to be shifted away from their optimal positions, as long as they are also increased in size to compensate. This paper describes a central piece of the assessment mechanism implemented in conjunction with an automated scheduling algorithm, which predicts whether beam conflicts will occur, and whether they can be deconflicted with changes in position and size. A series of small experiments was conducted to determine boundary conditions for the deconfliction algorithm, such as a threshold number of iterations for incrementally separating beams, and impacts from the positions of active areas such as proximity to an edge of the antenna. These experiments also explored how the deconfliction thresholds change with different combinations of larger and smaller active areas. This paper summarizes the results of these experiments, and also related elements of the algorithm such as the information preserved from the deconfliction process to inform the logic for trying alternative allocations when necessary. Finally, this paper also presents results from a larger experiment conducted to compare performance in two scenarios – a baseline scenario with only parabolic antennas, and an alternate scenario with phased array antennas substituted at several ground stations. Using a sample set of satellite support requests over a 24 hour period, we compare overall performance in the phased array scenario with the baseline, in terms of the ability to satisfy requests in each case via the automated scheduling algorithm.

Intelligent Pilot Intent Analysis System Using Artificial Intelligence Techniques.

Abstract : This paper presents the design, prototyping and testing results for a software system thatautomatically analyzes Air Traffic Control (ATC) communications that have been convertedto speech along with location and velocity data of other aircraft from onboard sensors todetermine pilot intent and predict their trajectories. The design includes novel knowledgerepresentation techniques for this domain such as procedure graphs, dialog graphs, andgrammar fragments. The design was prototyped and tested on actual ATC communicationsfrom San Jose International Airport (SJC). Results of this testing are also presented

Integrating Phased Array Path Planning with Intelligent Satellite Scheduling

As the demand for satellite-driven communication increases in both the commercial and military sectors, so do the numbers of active satellite constellations and the parallel requirements for ground-based support capacity. Phased array antennae have been identified as a cost-effective hardware solution for increasing communications capacity at ground stations, due to their ability to support multiple contacts simultaneously and their design compatibility with cost-effective commercial components. However, with increased communications capacity comes added complexity for the task of scheduling satellite supports in a network of satellites and ground stations with multi-beam phased array antennae. This task breaks down into two inter-related goals. First, the network-level challenge remains to allocate contacts to specific local sites. This is already complex in the case where ground stations exclusively use traditional mechanically steered reflector antennae, as schedulers seek to optimize resource usage within the confines of satellite visibilities and equipment availability at different sites. Second, with the introduction of phased array antennae, there is an additional local-level challenge to calculate active areas and paths on the surface of the phased array, to determine whether a candidate allocation with multiple contacts can actually be supported. These are inter-related because the local path planning analysis is predicated on an allocation developed at the network-level, whereas the network-level reasoning is most effective if it can be informed by knowledge of incompatibilities manifested at the local level. This paper describes an Artificial Intelligence based approach for handling these mutual dependencies efficiently while generating nearly optimal solutions.