Jared Freeman

Training objective packages: enhancing the effectiveness of experiential training

ABSTRACT Training objectives define the purpose of instructional events; attaining them is the measure of successful training. Yet, it is challenging to apply training objectives in large, complex, multiparty military exercises. In such events it can be difficult for trainers to determine which students were able to address their objectives in a given scenario-based exercise, or, in some cases, whether they were able to address any training objectives at all. A scalable, formal mechanism is required to document and manage training objectives, their relationships to scenario conditions, and the performance measures by which attainment of objectives is evaluated. In this article, we describe Training Objective Packages and two subordinate formalisms: behaviour envelopes, which specify the bounds on student behaviour given conditions, and a formal expression of performance measurements that includes an approach called measurement envelopes. Each of these has value in the three phases of training: planning, execution, and assessment. We define these formalisms and describe several applications and opportunities for research.

Capturing and Building Expertise in Virtual Worlds

Model-driven simulation can make the design and delivery of instruction more efficient and effective. We describe two computational models that support both the design and delivery of instruction. BEST (the Benchmarked Experiential System for Training) can guide experts through the space of domain problems during the knowledge engineering phase of instructional design; it can guide trainees through the space of training objectives during instruction. PRESTO (Pedagogically Relevant Engineering of Scenarios for Training Objectives) builds scenarios on the fly to elicit the knowledge of experts during instructional design, and to satisfy the instructional objectives of trainees.

16. Design of a Multi-Vehicle Control System: System Design and User Interaction

As they are currently conducted, missions by single ROVs consist of several sub-tasks. After a vehicle has been launched, a human operator or a small team is responsible for controlling the flight, navigation, status monitoring, flight and mission alteration, problem diagnosis, communication and coordination with other operators, and often data analysis and interpretation. These tasks are similar in terms of their locus of control (e.g., keyboard and mouse input, joystick, trackball, visual display).

Exploratory joint and separate tracking of geographically related time series

Target tracking techniques have usually been applied to physical systems via radar, sonar or imaging modalities. But the same techniques - filtering, association, classification, track management - can be applied to nontraditional data such as one might find in other fields such as economics, business and national defense. In this paper we explore a particular data set. The measurements are time series collected at various sites; but other than that little is known about it. We shall refer to as the data as representing the Megawatt hour (MWH) output of various power plants located in Afghanistan. We pose such questions as: 1. Which power plants seem to have a common model? 2. Do any power plants change their models with time? 3. Can power plant behavior be predicted, and if so, how far to the future? 4. Are some of the power plants stochastically linked? That is, do we observed a lack of power demand at one power plant as implying a surfeit of demand elsewhere? The observations seem well modeled as hidden Markov. This HMM modeling is compared to other approaches; and tests are continued to other (albeit self-generated) data sets with similar characteristics. Keywords: Time-series analysis, hidden Markov models, statistical similarity, clustering weighted

A Benchmarked Experiential System for Training (BEST) and Dynamic Systems Theory

We tested a Benchmarked Experiential System for Training (BEST) on Dynamic Distributed Decision Making (DDD)/AWACS simulations. BEST leveraged mathematical optimization, human expertise, and observational learning to give trainees feedback about mathematically and behaviorally defined near-optimal/expert strategies. We measured team defensive scores for college students on baseline, and three assessment trials with planning, missions, and debriefings. During debriefings, BEST and Control teams had checklists and equal time, but BEST teams also observed a near-optimal mission. On baseline, BEST and Control teams were similar; on assessments 1–3, MOST teams significantly outperformed Control teams (partial eta squared effect sizes of .10, .21, and .13). The findings are a foundation for scientists to address how expert, novice, BEST, and Control teams differ on themes of cognitive systems engineering and dynamic systems theory. BEST interventions may substantially reduce team training time and facilitate teams in flexible and dynamic work conditions.

Intelligent Training Support Tools: Technology for the Future

Naval aviation has historically led the military training community in the field of modeling and simulation. Most research and development in this area has focused on hardware and software solutions to address issues such as visual fidelity and physics-based modeling. However, there is a clear need to integrate principles of learning with hardware and software solutions for tools to enhance training devices. Modeling and simulation techniques, specifically Object-Oriented (OO) Intelligent Agents (IA), provide technological advances well suited for assisting instructors in accomplishing training goals. I. Introduction HE naval aviation community faces evolving training challenges. Legacy simulation systems were not developed to support a Distributed Mission Training (DMT) environment, nor are they suited to support growing DMT requirements. In particular, legacy systems typically address the operations of a single platform, and they optimize physical fidelity rather than instructional effectiveness 1 . The Air Warfare Training Development (AWTD) program offers promising solutions to begin to address these issues. AWTD is an advanced R&D program underway in the U.S. Navy that investigates, demonstrates, and integrates strategies and technologies for distributed and deployable simulation-based aviation training. This effort focuses on rapidly transitioning mature technologies to the Fleet by developing prototype products for specific simulation and training applications. In addition, this effort investigates elements that have been called out by acquisition efforts as areas in need of R&D for future transitions as well as strategies and technologies that can be transitioned into existing platforms in the short-term. Two areas of investigation within AWTD include the Common Distributed Mission Training Station (C-DMTS) and Intelligent Training Support Tools (ITST). The first thread, C-DMTS, aims to improve simulator control stations used by instructor/operators by developing a common framework for a multitude of platforms 1 . The second thread, ITST, supports the development of a C-DMTS by pursuing tools and strategies related to distributed performance measurement and debrief preparation. The remainder of this paper will describe the C-DMTS and ITST work, and discuss how Object-Oriented (OO) techniques facilitate this effort.

Optimising instructional strategies: a benchmarked experiential system for training

The problem of developing and delivering feedback concerning teamwork in ill-defined domains is addressed. Three strategies are combined to train using feedback: (1) mathematical optimisation techniques are leveraged to rapidly devise solutions to the complex problems of asset selection and scheduling in military mission planning and execution and those optimised solutions are used as feedback; (2) trainee attention is focused on specific principles that humans can learn from these optimised solutions; (3) the profound human capability to go beyond what is told and to learn from observation is leveraged. An experiment that assessed the impact of these strategies on human learning in a team command and control task is reported and implications for simulation-based training are stated.

Multi-attributed network discovery: learning suspicious patterns in social network data (Withdrawal Notice)

This paper was presented at the SPIE conference indicated above and has been withdrawn from publication at the request of the authors.