52 research outputs found

    Applying Gaming Technology to Tomahawk Mission Planning and Training

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    Fall 2005 Simulation Interoperability Workshop, Paper Number 4 & Presentation.Simulation Interoperability Standards Organization (SISO) SIW Conference PaperOver the past decade the computer gaming industry has not only generated its own multi-billion dollar section of the entertainment industry, but it has also made significant inroads into the military market, especially in training and simulation, starting with Marine Doom and continuing up to today ’s Full Spectrum Command and America ’s Army. This paper describes a Navy-funded research project that uses gaming technology for not only training, but also as an operational decision aid for the Tactical Tomahawk Weapon Control System (TTWCS). The research is aimed at adapting game engine technology to predict and simulate the motion of ground target vehicles (e.g. SCUD Launchers) through their local terrain over a given period of time, then use the associated rendering capabilities to provide realistic 3D views. The paper presents an overview of the TTWCS mission and how it will benefit from specific advances in gaming technology, especially in the areas of artificial intelligence, path finding, and physics. It discusses the current state of the project using existing commercial gaming technology and the future plans for adapting and expanding the open source game engine technology of the Delta3D project underway at the MOVES Institute at the Naval Postgraduate School

    Using a Cognitive Architecture for Opponent Target Prediction

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    One of the most important aspects of a compelling game AI is that it anticipates the player’s actions and responds to them in a convincing manner. The first step towards doing this is to understand what the player is doing and predict their possible future actions. In this paper we show an approach where the AI system focusses on testing hypotheses made about the player’s actions using an implementation of a cognitive architecture inspired by the simulation theory of mind. The application used in this paper is to predict the target that the player is heading towards, in an RTS-style game. We improve the prediction accuracy and reduce the number of hypotheses needed by using path planning and path clustering

    New Generation of Instrumented Ranges: Enabling Automated Performance Analysis

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    Military training conducted on physical ranges that match a unit’s future operational environment provides an invaluable experience. Today, to conduct a training exercise while ensuring a unit’s performance is closely observed, evaluated, and reported on in an After Action Review, the unit requires a number of instructors to accompany the different elements. Training organized on ranges for urban warfighting brings an additional level of complexity—the high level of occlusion typical for these environments multiplies the number of evaluators needed. While the units have great need for such training opportunities, they may not have the necessary human resources to conduct them successfully. In this paper we report on our US Navy/ONR-sponsored project aimed at a new generation of instrumented ranges, and the early results we have achieved. We suggest a radically different concept: instead of recording multiple video streams that need to be reviewed and evaluated by a number of instructors, our system will focus on capturing dynamic individual warfighter pose data and performing automated performance evaluation. We will use an in situ network of automatically-controlled pan-tilt-zoom video cameras and personal position and orientation sensing devices. Our system will record video, reconstruct dynamic 3D individual poses, analyze, recognize events, evaluate performances, generate reports, provide real-time free exploration of recorded data, and even allow the user to generate ‘what-if’ scenarios that were never recorded. The most direct benefit for an individual unit will be the ability to conduct training with fewer human resources, while having a more quantitative account of their performance (dispersion across the terrain, ‘weapon flagging’ incidents, number of patrols conducted). The instructors will have immediate feedback on some elements of the unit’s performance. Having data sets for multiple units will enable historical trend analysis, thus providing new insights and benefits for the entire service.Office of Naval Researc

    A Platform Independent Game Technology Model for Model Driven Serious Games Development

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    Game‑based learning (GBL) combines pedagogy and interactive entertainment to create a virtual learning environment in an effort to motivate and regain the interest of a new generation of ‘digital native’ learners. However, this approach is impeded by the limited availability of suitable ‘serious’ games and high‑level design tools to enable domain experts to develop or customise serious games. Model Driven Engineering (MDE) goes some way to provide the techniques required to generate a wide variety of interoperable serious games software solutions whilst encapsulating and shielding the technicality of the full software development process. In this paper, we present our Game Technology Model (GTM) which models serious game software in a manner independent of any hardware or operating platform specifications for use in our Model Driven Serious Game Development Framework

    Prototype development of low-cost, augmented reality trainer for crew service weapons

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    A significant emerging threat to coalition forces in littoral regions is from small craft such as jet skis, fast patrol boats, and speedboats. These craft, when armed, are categorized as Fast Inshore Attack Craft (FIAC), and their arsenal can contain an array of weapons to include suicide bombs, crew-served weapons, anti-tank or ship missiles, and torpedoes. While these craft often have crude weapon technologies, they use an asymmetric tactic of large numbers of small, cheap, poorly armed and armored units to overwhelm coalition defenses. Training on crew-served weapons on coalition ships has not advanced to meet this new threat. The current training methods do not satisfactorily train the following skills: Rules of engagement (ROE), marksmanship against highly maneuverable targets, threat prioritization, target designation, field of fire coordination, coordinated arms effects, or watch station to CIC communications. The creation of a prototype Augmented Reality Virtual At Sea Trainer (AR-VAST) shows that emerging augmented reality technologies can overcome limitations of traditional training methods. A fully developed AR-VAST system would be a deployable technology solution that uses in-place weapon systems as trainers in real-world environments with simulated enemy targets. While the AR-VAST architecture can be expanded to allow for training and coordination with multiple weapon operators, phone talkers, and bridge teams for maximum training effectiveness, the current prototype addresses the primary issue of identification and marksmanship.http://archive.org/details/prototypedevelop109453933US Navy (USN) author.Approved for public release; distribution is unlimited

    Partial observability during predictions of the opponent's movements in an RTS game

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    Abstract — In RTS-style games it is important to be able to predict the movements of the opponent’s forces to have the best chance of performing appropriate counter-moves. Resorting to using perfect global state information is generally considered to be ‘cheating ’ by the player, so to perform such predictions scouts (or observers) must be used to gather information. This means being in the right place at the right time to observe the opponent. In this paper we show the effect of imposing partial observability onto an RTS game with regard to making predictions, and we compare two different mechanisms that decide where best to direct the attention of the observers to maximise the benefit of predictions. I

    Enabling immersive simulation.

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