This PDF file contains the front matter associated with SPIE Proceedings Volume 6564, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and the Conference Committee listing.

The Irma synthetic signature prediction code is being developed by the Munitions Directorate of the Air Force Research Laboratory (AFRL/MN) to facilitate the research and development of multi-sensor systems. There are over 130 users within the Department of Defense, NASA, Department of Transportation, academia, and industry. Irma began as a high-resolution, physics-based Infrared (IR) target and background signature model for tactical weapon applications and has grown to include: a laser (or active) channel (1990), improved scene generator to support correlated frame-to-frame imagery (1992), and passive IR/millimeter wave (MMW) channel for a co-registered active/passive IR/MMW model (1994). Irma version 5.0 was released in 2000 and encompassed several upgrades to both the physical models and software; host support was expanded to Windows, Linux, Solaris, and SGI Irix platforms. In 2005, version 5.1 was released after an extensive verification and validation of an upgraded and reengineered active channel. Since 2005, the reengineering effort has focused on the Irma passive channel. Field measurements for the validation effort include the unpolarized data collection. Irma 5.2 is scheduled for release in the summer of 2007. This paper will report the validation test results of the Irma passive models and discuss the new features in Irma 5.2.

This paper presents the details of an advanced satellite radar scatterometer end-to-end simulation, known as the Conically Scanning Active/Passive Sensor Simulation (CAPSS) used for hardware development trade studies. This simulation is a collection of customized software tools that permits design engineers to vary the instrument parameters and configurations to assess the resulting instrument performance under realistic on-orbit scenarios. Atmospheric and oceanic environmental parameters and satellite to earth geometry are used to simulate active and passive microwave measurements. Afterwards, these microwave observables are used in a geophysical retrieval algorithm to infer physical parameters such as ocean wind speed and direction. This simulation software provides an efficient method to perform engineering trade studies to optimize the sensor instrument design for a desired level of geophysical measurement performance. An example of a simulation of NASA's SeaWinds scatterometer on QuikSCAT is presented and compared to actual SeaWinds measurements.

High resolution Digital Surface Models (DSMs) may contain voids (missing data) due to the data collection process used to obtain the DSM, inclement weather conditions, low returns, system errors/malfunctions for various collection platforms, and other factors. DSM voids are also created during bare earth processing where culture and vegetation features have been extracted. The Harris LiteSite^TM Toolkit handles these void regions in DSMs via two novel techniques. We use both partial differential equations (PDEs) and exemplar based inpainting techniques to accurately fill voids. The PDE technique has its origin in fluid dynamics and heat equations (a particular subset of partial differential equations). The exemplar technique has its origin in texture analysis and image processing. Each technique is optimally suited for different input conditions. The PDE technique works better where the area to be void filled does not have disproportionately high frequency data in the neighborhood of the boundary of the void. Conversely, the exemplar based technique is better suited for high frequency areas. Both are autonomous with respect to detecting and repairing void regions. We describe a cohesive autonomous solution that dynamically selects the best technique as each void is being repaired.

In many PC-based simulations, message passing is commonly used for transfer of dynamic data between software models. Parameter definition and/or initialization are typically handled differently and may suffer because that data is not transferred to all the required models at any time during a simulation run. Instead multiple and often dissimilar initializations of the same parameters occur in various software modules, much to the chagrin of the configuration control personnel and test engineers. Results from using this approach can be particularly damaging for navigation sensor simulations such as a GPS-aided inertial mode and for flight program software evaluation and validation, where extreme accuracy of results is required. The paper proposes solutions to prevent parameter data mismatch and/or compromise. These solutions are based upon doing a thorough job of critical parameter definition and initialization, given the simulation computer architecture. The solutions are discussed and explained. Included are descriptions of actual cases - examples of parameter mismatch in medium to large scale avionic simulations where accuracy was critical to performance evaluation. Parameter categories critical to accurate evaluations of avionic simulation performance are identified and discussed.

Knowledge acquisition/discovery, ontology management, knowledge representation and knowledge sharing are key issues in ontology research. This paper focuses on the issue of applying ontology management and analysis to facilitate the reusability of simulation components based on ontology comparisons. Novel ontology comparison methods for component-based simulation composition are described, such as the four independent approaches for ontology similarity analysis: terminology-based, feature-based, semantic, and topological. A data -fusion-based approach is used to integrate the information from these three techniques into a single similarity score. The assignment of such similarity indices and the use of data fusion to obtain an ontology comparison similarity score are demonstrated using a simple example.

There is a key for anticipatory tools and techniques to assist command staff in Intelligently Preparing the Battlespace by predicting and assessing adversary and neutral courses-of-action in a manner that enable the rapid diffusion of undesirable military or socio-political situations. This paper discusses the development of an Adversary Prediction Environment (APE) that will provide this capability by leveraging soft computing techniques and grid computing resources to provide an environment that allows for rapid exploration and analysis of enemy COAs for a given set of scenarios. The APE accomplishes these capabilities by utilizing prediction capabilities present in our DSAP (Dynamic Situation Awareness and Predictive (DSAP) environment to apply operationally focused simulation through Joint SemiAutomated Forces (JSAF) to evaluate plan effectiveness. The paper discusses our efforts to identify prospective scenarios and define a library of basic adversary and neutral force plans, actions, and adversary objectives that can be used to model adversary behavior for the identified scenario. The paper also covers our efforts to modify DSAP in support of an APE proof-of-concept that can be used to simulate and rank adversary plans.

For a number of decades, among the most prevalent training media in the military has been Tactical Engagement Simulation (TES) training. TES has allowed troops to train for practical missions in highly realistic combat environments without the associated risks involved with live weaponry and munitions. This has been possible because current TES has relied largely upon the Multiple Integrated Laser Engagement System (MILES) and similar systems for a number of years for direct-fire weapons, using a laser to pair the shooter to the potential target(s). Emerging systems, on the other hand, will use a pairing method called geometric pairing (geo-pairing), which uses a set of data about both the shooter and target, such as locations, weapon orientations, velocities, and weapon projectile velocities, nearby terrain to resolve an engagement. A previous paper [1] introduces various potential sources of error for a geo-pairing solution. This paper goes into greater depth regarding the impact of errors that originate within initial velocity errors, beginning with a short introduction into the TES system (TESS). The next section will explain the modeling characteristics of the projectile motion followed by a mathematical analysis illustrating the impacts of errors related to those characteristics. A summary and conclusion containing recommendations will close this paper.

RAM Laboratories is developing a more advanced real-time update capability for both the predictive and stateestimation features of its Dynamic Situational Awareness and Predictive Framework and its underlying Multiple Replication Framework in support of the Air Force Research Laboratory's Joint Synthetic Environment for Research and Development. The overall goal of the DSAP Infrastructure is to allow Commanders and their staff at Air Operations Centers the ability to perform "what-if" analysis of plans and alternatives "on-the-fly" while continually augmenting the real-time picture sensor inputs with simulated state-estimated assessments. This paper discusses design and implementation efforts to provide a Dynamic Situational Awareness capability utilizing embedded simulations calibrated by real-time C4I inputs to estimate the state of unobservable elements of an operational picture. Specifically this paper will discuss enhancements via a Calibrated Real-time Simulation component, a real-time simulation component along with the process for providing real-time updates to running simulations.

The most recent client-server version of Pythia modeling software is presented. Pythia is a software implementation of a Bayesian Net framework and is used for course of action development, evaluation, and selection in the context of effects-based planning. A new version, Pythia 1.5, is a part of a larger suite of tools for behavioral influence analysis, brought into the state-of-the-art client-server computing environment. This server application for multi-user and multiprocess computing relies on the Citrix Presentation Server for integration, security and maintenance. While Pythia's process is run on a server, the input/output services are controlled and displayed through a client PC. Example use of Pythia is illustrated through its application to a suppression of IED activity in an Iraqi province. This case study demonstrates how analysts can create executable (probabilistic) models that link potential actions to effects, based on knowledge about the cultural and social environment. Both the tool and the process for creating and analyzing the model are described as well as the benefits of using the new server based version of the tool.

In live force-on-force direct fire training, simulated munitions are used instead of live munitions. Simulated munitions are typically modeled using laser systems such as the Multiple Integrated Laser Engagement System (MILES). Replacing the laser with an electronic message (also known as an electronic bullet or e-bullet) sent over a network is becoming feasible due to advances in sensors, communications, and computing. The e-bullet engagement methodology uses weapon location, orientation, and adjudication algorithms. Technical challenges in implementation include having accurate weapon and target location and orientation, network bandwidth, and terrain database resolution. This paper discusses issues and challenges using an e-bullet and laser/e-bullet hybrids for delivery accuracy and damage assessment. We will also present an engagement methodology robust enough to evolve with advances in technology.

An architecture for an integrated air combat and sensor network simulation is presented. The architecture integrates two components: a parallel real-time sensor fusion and target tracking simulation, and an air combat simulation. By integrating these two simulations, it becomes possible to experiment with scenarios in which one or both sides in a battle have very large numbers of primitive passive sensors, and to assess the likely effects of those sensors on the outcome of the battle. Modern Air Power is a real-time theater-level air combat simulation that is currently being used as a part of the USAF Air and Space Basic Course (ASBC). The simulation includes a variety of scenarios from the Vietnam war to the present day, and also includes several hypothetical future scenarios. Modern Air Power includes a scenario editor, an order of battle editor, and full AI customization features that make it possible to quickly construct scenarios for any conflict of interest. The scenario editor makes it possible to place a wide variety of sensors including both high fidelity sensors such as radars, and primitive passive sensors that provide only very limited information. The parallel real-time sensor network simulation is capable of handling very large numbers of sensors on a computing cluster of modest size. It can fuse information provided by disparate sensors to detect and track targets, and produce target tracks.

Modeling, Simulation and Training (MS&T) technologies have provided significant capabilities for Military training and mission rehearsal. However, most of the state-of-the-art MS&T systems used today are high fidelity, stand alone systems, routinely staffed by a team of support and instructional personnel. As the military becomes more reliant on these technologies to support ever changing concepts of operations, they are asking for numerous technological advancements including 1) automated instructional features to reduce the number of personnel required for exercises, 2) increased capability for adaptation of human computer interfaces to support individual differences and embedded performance support in operational settings, and 3) a continuum of low to high fidelity system components to provide embedded, deployable and transportable solutions. A multi-disciplinary team of researchers at the University of Central Florida's (UCF) Institute for Simulation and Training (IST) Applied Cognition and Training in Immersive Virtual Environments Lab (ACTIVE), lead by Dr. Denise Nicholson, is performing research and development to address these emerging requirements as part of on-going projects for Navy, Marine Corps and Army customers. In this paper we will discuss some of the challenges that confront researchers in this area and how the ACTIVE lab hopes to respond to these challenges.

Improvements in remote sensing technology for the collection of high resolution aerial LIDAR and hyperspectral data of urban landscapes have led to increasing interest in rapid 3d scene reconstruction and environment inferencing. In recent years algorithmic strategies fusing aerial LIDAR with hyperspectral data have been proposed to increase the overall confidence in data segmentation by taking advantage of the unique qualities of the data for each sensor type. No technique exists today, however, that fully automates the end-to-end process - from the initial collection of the uncorrected data to the production of a finished, accurate and realistic urban scene. Notwithstanding, key milestones that minimize human intervention have been made, and notable high quality suites of semi-automated tools are available today. In this paper, an alternative strategy towards fully automated building extraction under a variety of terrain relief conditions is presented. Advantages are discussed of multiple sensor feedback loops that update the scene at each segmentation step starting with an initial hypothesis of each feature's classification. The merit of such a strategy from the point of view of implementing it within a fully automated system is presented.

The Smart Target Model Generator (STMG) is an AFRL/MNAL sponsored tool for generating 3D building models for use in various weapon effectiveness tools. These tools include tri-service approved tools such as Modular Effectiveness/Vulnerability Assessment (MEVA), Building Analysis Module in Joint Weaponeering System (JWS), PENCRV3D, and WinBlast. It also supports internal dispersion modeling of chemical contaminants. STMG also has capabilities to generate infrared or other sensor images. Unlike most CAD-models, STMG provides physics-based component properties such as strength, density, reinforcement, and material type. Interior components such as electrical and mechanical equipment, rooms, and ducts are also modeled. Buildings can be manually created with a graphical editor or automatically generated using rule-bases which size and place the structural components using rules based on structural engineering principles. In addition to its primary purposes of supporting conventional kinetic munitions, it can also be used to support sensor modeling and automatic target recognition.

Chassis loads and vehicle handling are primarily impacted by the road surface over which a vehicle is traversing. By accurately measuring the geometries of road surfaces, one can generate computer models of these surfaces that will allow more accurate predictions of the loads introduced to various vehicle components. However, the logistics and computational power necessary to handle such large data files makes this problem a difficult one to resolve, especially when vehicle design deadlines are impending. This work aims to improve this process by developing Markov Chain models by which all relevant characteristics of road surface geometries will be represented in the model. This will reduce the logistical difficulties that are presented when attempting to collect data and run a simulation using large data sets of individual roads. Models will be generated primarily from measured road profiles of highways in the United States. Any synthetic road realized from a particular model is representative of all profiles in the set from which the model was derived. Realizations of any length can then be generated allowing efficient simulation and timely information about chassis loads that can be used to make better informed design decisions, more quickly.

The principal excitation to a vehicle's chassis system is the road profile. Simulating a vehicle traversing long roads is impractical and a method to produce short roads with given characteristics must be developed. There are many methods currently available to characterize roads when they are assumed to be homogeneous. This work develops a method of characterizing non-stationary road profile data using ARIMA (Autoregressive Integrated Moving Average) modeling techniques. The first step is to consider the road to be a realization of an underlying stochastic process. Previous work has demonstrated that an ARIMA model can be fit to non-stationary road profile data and the remaining residual process is uncorrelated. This work continues the examination of the residual process of such an ARIMA model. Statistical techniques are developed and used to examine the distribution of the residual process and the preliminary results are demonstrated. The use of the ARIMA model parameters and residual distributions in classifying road profiles is also discussed. By classifying various road profiles according to given model parameters, any synthetic road realized from a given class of model parameters will represent all roads in that set, resulting in a timely and efficient simulation of a vehicle traversing any given type of road.

During the vehicle design process, excitation loads are needed to correctly model the system response. The main source of excitation to this dynamic system comes from the terrain. Characteristic models of terrain topology, therefore, would allow for more accurate models and simulations of the system response. Terrain topology can be characterized as a realization of an underlying stochastic process. It has been demonstrated that ARIMA modeling can be used to characterize non-stationary road profiles. In this work it is suggested that ARIMA models of terrain topology can be further developed by characterizing the previously deterministic autoregressive coefficients as random variables. In this way uncertainty is introduced into the system parameters and propagated through the process to yield a distribution of terrain topology. This distribution is then dependent on the distribution of the residuals as well as the distribution of the ARIMA coefficients. The use of random variables to classify road types is discussed as possible future work.

This study focuses on developing an assessment tool for the performance prediction of lightweight autonomous vehicles with varying locomotion platforms on coastal terrain involves three segments. A table based on the House of Quality shows the relationships - high, low, or adverse - between mission profile requirements and general performance measures and geometries of vehicles under consideration for use. This table, when combined with known values for vehicle metrics, provides information for an index formula used to quantitatively compare the mobility of a user-chosen set of vehicles, regardless of their methods of locomotion. To study novel forms of locomotion, and to compare their mobility and performance with more traditional wheeled and tracked vehicles, several new autonomous vehicles - bipedal, self-excited dynamic tripedal, active spoke-wheel - are currently under development. While the terramechanics properties of wheeled and tracked vehicles, such as the contact patch pressure distribution, have been understood and models have been developed for heavy vehicles, the feasibility of extrapolating them to the analysis of light vehicles is still under analysis. wheeled all-terrain vehicle and a lightweight autonomous tracked vehicle have been tested for effects of sand gradation, vehicle speed, and vehicle payload on measures of pressure and sinkage in the contact patch, and preliminary analysis is presented on the sinkage of the wheeled all-terrain vehicle. These three segments - development of the comparison matrix and indexing function, modeling and development of novel forms of locomotion, and physical experimentation of lightweight tracked and wheeled vehicles on varying terrain types for terramechanic model validation - combine to give an overall picture of mobility that spans across different forms of locomotion.

In recent times, warfighting has been taking place not in far-removed areas but within urban environments. As a consequence, the modern warfighter must adapt. Currently, an effort is underway to develop shoulder-mounted rocket launcher rounds suitable with reduced acoustic signatures for use in such environments. Of prime importance is to ensure that these acoustic levels, generated by propellant burning, reflections from enclosures, etc., are at tolerable levels without requiring excessive hearing protection. Presented below is a proof-of-concept approach aimed at developing a computational tool to aid in the design process. Unsteady, perfectly-expanded-jet simulations at two different Mach numbers and one at an elevated temperature ratio were conducted using an existing computational aeroacoustics code. From the solutions, sound pressure levels and frequency spectra were then obtained. The results were compared to sound pressure levels collected from a live-fire test of the weapon. Lastly, an outline of work that is to continue and be completed in the near future will be presented.

The Air Force's 45th Space Wing is in charge of operating the Range Safety System (RSS) for all launches that take place on the Eastern Range. If initiated, the RSS currently implemented on the Space Transportation System after launch would provide for the partial destruction of the solid rocket boosters (SRBs) to terminate thrust. The majority of the risk from the large explosive debris created comes from the aft ends of the SRBs, which fall largely intact along with the remaining propellant. Historically, no impact data on such a scenario has been available and in support of the Space Shuttle Return-to-Flight schedule, aerodynamic and trajectory analyses were performed to characterize any pitch angle biases associated with the aft end's descent after initiating the linear shaped charges (LSCs) on the SRBs. Results show the aft end has a bias towards impacting at ±5, 70, or 175 degrees and takes an average of 10 seconds to stabilize into any one of these orientations after being separated from the SRB forward body.

A multibody dynamics model of a Vertical Take-off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) is presented in this study. The scope of the project was to investigate a lightweight landing gear which has a stable and robust landing performance. Two original designs of the landing gear for the module of interest have been modeled and analyzed in this study. Two new designs have also been developed, modeled, and analyzed. A limited analysis of the forces that occur in the legs/struts has also been performed, to account for possible failure of the members due to buckling. The model incorporates a sloped surface of deformable terrain for stability analysis of the landing scenarios, and unilateral constraints to model the ground reaction forces upon contact. The lift forces on the UAV are modeled as mathematical relations dependent on the speed of the ducted fan to enable the variation of the impact velocities and the different landing scenarios. The simulations conducted illustrate that initial conditions at landing have a big impact on the stability of the module. The two new designs account for the worst possible scenario, and, for the material properties given, end with a larger weight than the one of the original design with three legs and a ring. Simulation data from several landing scenarios are presented in this paper, with analysis of the difference in performance among the different designs.

Analyzing thermodynamic patterns during product development can easily be characterized by various "Modeling and Simulation" software programs to observe Emissive Signatures. Baseline temperatures are referenced as an adjusted "Blackbody" value and used to compare differential temperature changes during dynamometer testing. An infrared spectrum distinguishes pattern profiles unique to the product for both thermodynamic performance and to accurately validate test materials. A collaborative CRADA1 effort has been established between the US Army RDECOMTARDEC and Drive Dynamics LLC of Dallas, TX on the development of an advanced Run Flat Insert System for military wheeled vehicles. Mapping of measured infrared thermometer values help in locating and determining whether or not material temperatures are within design limits. Prior testing by the US Army Physical Simulation Team has established a baseline Emissive Signature for HMMWV wheel assemblies at specific loads and speeds. As advanced Run Flat Insert Systems are developed for increased load capacities using structurally engineered profiles the Emissive Signature can be used to compare to baseline wheel assemblies and aid in establishing Run Flat performance and longevity.

As part of the development of any computer simulation of procedures, equipment, or airspace, an appropriate model of aircraft must be selected. Due to the complexity and aviation safety-critical nature of these development efforts, a detailed survey of the current state-of-the-art in aircraft flight dynamic models is desired. Options include basic 3-D performance envelopes of various aircraft (e.g., acceleration, deceleration, turn rate, and climb rate), high-fidelity models (e.g., proprietary aircraft manufacturer models), commercial-off-the-shelf models (e.g., Laminar Research's X-Plane and Microsoft's Flight Simulator), Government models (e.g., NASA or FAA), and originally developed six degree-of-freedom mathematical models. Here, the simple kinematics model (using basic kinematic relationships without considering the impact of aerodynamics), the small perturbation theory model (which uses only the known, non-dimensional aerodynamic properties of the aircraft), the total forces and moments method (which solves the complete set of nonlinear differential equations and requiring large tables describing aircraft parameters in different flight regimes), and blade element theory (which makes use of the aircraft's physical structure to calculate the aerodynamic forces and moments on thin strips of the aircraft) are reviewed.

A highly productive approach to small systems design and integration (SSDI), and modeling and simulation (M&S), based on rapid/interactive prototyping has been effectively used at Physical Optics Corporation (POC) to support small system development of "intellectual products" that cover a broad range of electromagnetic spectra (X-ray, optical, infrared, and microwave). In particular, the implementation of an accelerated interactive modeling (AIM) environment produces effective engineering solutions for tackling difficult and complex technical issues for combining 3D mechanical design and microwave engineering. In addition, using established small systems engineering principles and the effective use of collaborative input at the start of a development effort that incorporates a diverse range of areas like optics, mechanics, electronics, software, thermal modeling, electromagnetism, surface chemistry, and manufacturing plays an important role in the success of future military and homeland security applications.

This paper describes our recent work combining a high-fidelity battlefield software simulaton, a suite of autonomous sensor and navigation control algorithms for unmanned air vehicles (UAVs), and a hardware-in-the-loop control interface. The complete system supports multiple real and simulated UAVs that search for and track multiple real and simulated targets. Targets communicate their real-time locations to the simulator through a wireless GPS link. Data from real target(s) is used to create target(s) in the simulation testbed that may exist alongside additional simulated targets. The navigation and video sensors onboard the UAVs are tasked (via another wireless link) by our control algorithm suite to search for and track targets that exist in the simulation. Video data is streamed to an image plane video tracker (IPVT), which produces detections that can be fed to a global tracker within the control suite. Routing and gimbal control algorithms use information from the global tracker to task the UAVs, thus completing an information feedback control loop. Additional sensors (such as the ground moving target indicator (GMTI) radar) can exist within the simulation and generate simulated detections to augment the tracking information obtained from the IPVT. Our simulator is part of Toyon's Simulation of the Locations and Attack of Mobile Enemy Missiles (SLAMEM^(R)) tool. SLAMEM contains detailed models for ground targets, surveillance platforms, sensors, attack aircraft, UAVs, data exploitation, multi-source fusion, sensor retasking, and attack nomination. SLAMEM models road networks, foliage cover, populated regions, and terrain, using the terrain elevation data (DTED).

The paper will present a simulation testbed in which a scenario can be setup, simulated and evaluated and where planning tools, electronic warfare (EW) components and command and control (C2) functionality can be integrated. The testbed is HLA (high level architecture) compliant, allows for a distributed simulation with dynamically configurable federates, and can also be used for testing actual equipment in a simulated scenario. One of the key components in the testbed is a set of planning tools that can be used to show ranges for sensors, jamming and communication systems. These tools can be used not only for planning the mission (e.g. best route) but can also be used during the mission to show the location of possible threats or the range of own equipment (sensor, jamming, communication) in different situations. During a mission these tools can be used to support the decisions of what actions to take in different situations. One goal with developing the planning tools in the testbed is to learn how to use planning tools in real life scenarios. Therefore, the planning tools are constantly developed and tested with respect to technical and tactical use. Also technical and tactical aspects of current and future EW and C2 equipment can be tested and developed in the testbed.

To foster shared battlespace awareness in Air Operations Centers supporting the Joint Forces Commander and Joint Force Air Component Commander, BAE Systems is developing a Commander's Model Integration and Simulation Toolkit (CMIST), an Integrated Development Environment (IDE) for model authoring, integration, validation, and debugging. CMIST is built on the versatile Eclipse framework, a widely used open development platform comprised of extensible frameworks that enable development of tools for building, deploying, and managing software. CMIST provides two distinct layers: 1) a Commander's IDE for supporting staff to author models spanning the Political, Military, Economic, Social, Infrastructure, Information (PMESII) taxonomy; integrate multiple native (third-party) models; validate model interfaces and outputs; and debug the integrated models via intuitive controls and time series visualization, and 2) a PMESII IDE for modeling and simulation developers to rapidly incorporate new native simulation tools and models to make them available for use in the Commander's IDE. The PMESII IDE provides shared ontologies and repositories for world state, modeling concepts, and native tool characterization. CMIST includes extensible libraries for 1) reusable data transforms for semantic alignment of native data with the shared ontology, and 2) interaction patterns to synchronize multiple native simulations with disparate modeling paradigms, such as continuous-time system dynamics, agent-based discrete event simulation, and aggregate solution methods such as Monte Carlo sampling over dynamic Bayesian networks. This paper describes the CMIST system architecture, our technical approach to addressing these semantic alignment and synchronization problems, and initial results from integrating Political-Military-Economic models of post-war Iraq spanning multiple modeling paradigms.

Nowadays, there is an increasing demand for the military to conduct operations that are beyond traditional warfare. In these operations, analyzing and understanding those who are involved in the situation, how they are going to behave, and why they behave in certain ways is critical for success. The challenge lies in that behavior does not simply follow universal/fixed doctrines; it is significantly influenced by soft factors (i.e. cultural factors, societal norms, etc.). In addition, there is rarely just one isolated enemy; the behaviors and responses of all groups in the region, and the dynamics of the interaction among them composes an important part of the whole picture. The Dynamic Adversarial Gaming Algorithm (DAGA) project aims to provide a wargaming environment for automation of simulating dynamics of geopolitical crisis and eventually be applied to military simulation and training domain, and/or commercial gaming arena. The focus of DAGA is on modeling communities of interest (COIs), where various individuals, groups, and organizations as well as their interactions are captured. The framework should provide a context for COIs to interact with each other and influence others' behaviors. These behaviors must incorporate soft factors by modeling cultural knowledge. We do so by representing cultural variables and their influence on behavior using probabilistic networks. In this paper, we describe our COI modeling, the development of cultural networks, the interaction architecture, and a prototype of DAGA.