A barrier to developing novel AI for complex reasoning is the lack of appropriate wargaming platforms for training and evaluating AIs in a multiplayer setting combining collaborative and adversarial reasoning under uncertainty with game theory and deception. An appropriate platform has several key requirements including flexible scenario design and exploration, extensibility across all five elements of Multi-Domain Operations (MDO), and capability for human-human and human-AI collaborative reasoning and data collection, to aid development of AI reasoning and the warrior-machinelike interface. Here, we describe the ARL Battlespace testbed which fulfills the above requirements for AI development, training and evaluation. ARL Battlespace is offered as an open source software platform (https://github.com/USArmyResearchLab/ARL_Battlespace). We present several example scenarios implemented in ARL Battlespace that illustrate different kinds of complex reasoning for AI development. We focus on ‘gap’ scenarios that simulate bridgehead and crossing tactics, and we highlight how they address key platform requirements including coordinated MDO actions, game theory and deception. We describe the process of reward shaping for these scenarios that will incentivize an agent to perform command and control (C2) tasks informed by human commanders’ courses of action, as well as the key challenges that arise. The intuition presented will enable AI researchers to develop agents that will provide optimal policies for complex scenarios.
The Inter-Domain Path Computation problem under Node-defined Domain Uniqueness constraint (IDPC-NDU) is a recently investigated topic for finding the effective routing paths on the multi-domain network topology as well as transportation. The objective of the IDPC-NDU is to find the shortest path in the multi-domain directed graph that traverses every domain at most once. Since the IDPC-NDU belongs to NP-Hard class, this paper proposes a novel two-level approach based on an Evolutionary Algorithm (EA) to solve it. The first level aims to determine the sequence of crossed domains using an improved Genetic Algorithm (GA), while the second one aims to locate the minimally costly path between two nodes among the entire domains. Furthermore, we devise an approach to represent a chromosome, which reduces the chromosome length to the number of domains. Experiments on numerous sets of instances were implemented to show the effectiveness and characteristics of the proposed algorithm.
Future Multi Domain Operation (MDO) wargaming will rely on Artificial Intelligence/Machine Learning (AI/ML) algorithms to aid and accelerate complex Command and Control decision-making. This requires an interdisciplinary effort to develop new algorithms that can operate in dynamic environments with changing rules, uncertainty, individual biases, changing cognitive states, as well as the capability to rapidly mitigate unexpected hostile capabilities and exploit friendly technological capabilities. Building on recent advancements in AI/ML algorithms, we believe that new algorithms for learning, reasoning under uncertainty, game theory with three or more players, and interpretable AI can be developed to aid in complex MDO decision-making. To achieve these goals, we developed a new flexible MDO warfighter machine interface game, Battlespace, to investigate and understand how human decision-making principles can be leveraged by and synergized with AI. We conducted several experiments with human vs. random players operating in a fixed environment with fixed rules, where the overall goal of the human players was to collaborate to either capture the opponents’ flags or eliminate all of their units. Then, we analyzed the evolution of the games and identified key features that characterized the human players’ strategies and their overall goal. We then followed a Bayesian approach to model the human strategies and developed heuristic strategies for a simple AI agent. Preliminary analysis revealed that following the human agents’ strategy in the capture the flag games produced the greatest winning percentage and may be useful for gauging the value of intermediate game states for developing the coordinated action planning of reinforcement learning algorithms.
The Army anticipates that future battles will be in more complex and dynamic environments, requiring the Army to push modernization priorities. In order for Soldiers to thrive within these challenging operational contexts, they must rapidly adapt to leverage and integrate technology in order to gain and maintain overmatch over near peer adversaries. Teaming will be especially critical for mission success. Soldier teams will need to be adaptive and fluid in their roles to respond to dynamic mission demands. Technology can be leveraged to enable and enhance teaming between human and humanagent teams. Augmented reality (AR) technology may provide an adaptive solution for information sharing across individuals and teams to promote a common operational picture within future operational environments. Here, we present a small teams study where dyads leveraged technological tools that helped facilitate teaming during a simulated mission planning and rehearsal scenario. Partners worked together to plan a path to extract a high value target while avoiding obstacles and hostile forces. Dyads completed missions using two technologies counterbalanced across the study. The first condition was reflective of current methods for mission planning in the Army; dyads used a Table Top to plan, rehearse, and execute the simulated mission. In the second condition, dyads used the Microsoft HoloLens to complete the mission in an augmented reality environment. This paper will present findings of how perceived teaming efficacy and performance relate to mission performance and workload in the two technologies.
KEYWORDS: Visualization, Visual analytics, 3D modeling, Virtual reality, Data processing, 3D displays, Data analysis, Scientific visualization, Displays, Human-machine interfaces
Advancement in the areas of high performance computing and computational sciences have facilitated the generation of an enormous amount of research data by computational scientists - the volume, velocity and variability of Big 'Research' Data has increased across all disciplines. An immersive and non-immersive analytics platform capable of handling extreme-scale scientific data will enable scientists to visualize unwieldy simulation data in an intuitive manner and guide the development of sophisticated and targeted analytics to obtain usable information. Our immersive and non-immersive visualization work is an attempt to provide computational scientists with the ability to analyze the extreme-scale data generated. The main purpose of this paper is to identify different characteristics of a scientific data analysis process to provide a general outline for the scientists to select the appropriate visualization systems to perform their data analytics. In addition, we will include some of the details on how to how the immersive and non-immersive visualization hardware and software are setup. We are confident that the findings in our paper will provide scientists with a streamlined and optimal visual analytics workflow.
Major advancements in computational and sensor hardware have enormously facilitated the generation and collection of research data by scientists - the volume, velocity and variety of Big ’Research’ Data has increased across all disciplines. A visual analytics platform capable of handling extreme-scale data will enable scientists to visualize unwieldy data in an intuitive manner and guide the development of sophisticated and targeted analytics to obtain useable information. Reconfigurable Visual Computing Architecture is an attempt to provide scientists with the ability to analyze the extreme-scale data collected. Reconfigurable Visual Computing Architecture requires the research and development of new interdisciplinary technological tools that integrate data, realtime predictive analytics, visualization, and acceleration on heterogeneous computing platforms. Reconfigurable Visual Computing Architecture will provide scientists with a streamlined visual analytics tool.
Fifteen years of experience in designing and implementing a VR integration library have produced a wealth of lessons upon which we can further build and improve our capability to write worthwhile virtual reality applications. The FreeVR virtual reality library is a mature library, yet continues to progress and benefit from the insights and requests encountered during application development. We compare FreeVR with the standard provisions of virtual reality integration libraries, and provide an in-depth look at FreeVR itself. We examine what design decisions worked, and which fell short. In particular, we look at how the features of FreeVR serve to restore applications of the past into working condition and aid in providing longevity to newly developed applications.
The Virtual Hydrology Observatory will provide students with the ability to observe the integrated hydrology simulation
with an instructional interface by using a desktop based or immersive virtual reality setup. It is the goal of the virtual
hydrology observatory application to facilitate the introduction of field experience and observational skills into
hydrology courses through innovative virtual techniques that mimic activities during actual field visits. The simulation
part of the application is developed from the integrated atmospheric forecast model: Weather Research and Forecasting
(WRF), and the hydrology model: Gridded Surface/Subsurface Hydrologic Analysis (GSSHA). Both the output from
WRF and GSSHA models are then used to generate the final visualization components of the Virtual Hydrology
Observatory. The various visualization data processing techniques provided by VTK are 2D Delaunay triangulation and
data optimization. Once all the visualization components are generated, they are integrated into the simulation data
using VRFlowVis and VR Juggler software toolkit. VR Juggler is used primarily to provide the Virtual Hydrology
Observatory application with fully immersive and real time 3D interaction experience; while VRFlowVis provides the
integration framework for the hydrologic simulation data, graphical objects and user interaction. A six-sided CAVETM like
system is used to run the Virtual Hydrology Observatory to provide the students with a fully immersive experience.
Over the last decades, Louisiana has lost a substantial part of its coastal region to the Gulf of Mexico. The goal of the
project depicted in this paper is to investigate the complex ecological and geophysical system not only to find solutions
to reverse this development but also to protect the southern landscape of Louisiana for disastrous impacts of natural
hazards like hurricanes. This paper sets a focus on the interactive data handling of the Chenier Plain which is only one
scenario of the overall project. The challenge addressed is the interactive exploration of large-scale time-depending 2D
simulation results and of terrain data with a high resolution that is available for this region.
Besides data preparation, efficient visualization approaches optimized for the usage in virtual environments are
presented. These are embedded in a complex framework for scientific visualization of time-dependent large-scale
datasets. To provide a straightforward interface for rapid application development, a software layer called VRFlowVis
has been developed. Several architectural aspects to encapsulate complex virtual reality aspects like multi-pipe vs.
cluster-based rendering are discussed. Moreover, the distributed post-processing architecture is investigated to prove its
efficiency for the geophysical domain. Runtime measurements conclude this paper.
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