Ann Arbor, MI, United States

Soar Technology, Inc.

www.soartech.com
Ann Arbor, MI, United States

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Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 491.77K | Year: 2015

SC2RAM (Simulated Cognitive Cyber Red team Attacker Model) is a cyber red team-in-a-box. It combines the cognitive power of human attackers with the efficiency and re-producibility of a computer program. It helps organizations defend against cyber attacks through at least three transition pathways: operator training, testing and certification of defensive automation, and analysis of cyber operations. Its methods can be extended to develop cognitive models of cyber defenders and users, enabling a multi-purpose environment for test and evaluation of alternative tactics, procedures and policies for network defense.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 495.38K | Year: 2015

SoarTech, along with our partners Adaptive Cognitive Systems and Aptima, are applying our vast expertise in the design, development and integration of artificial intelligence technologies, bringing it to bear to help develop more realistic entity-level scenarios for USMC simulated training. Most crucial to our work will be the design and development of a full infrastructure, called SERUM (Simulated training Exercises with Robust Unmanned Models) to support advanced AI for the USMCs simulation technology portfolio. This infrastructure will allow for robust, unmanned entities to exist in virtual simulation environments (e.g., VBS2), and will also include the necessary hooks and mechanisms to allow entities to both perceive the world and make actions within it. Using SERUM, developers and knowledge engineers can develop and integrate behaviors that drive SUDM characters within VBS2, thereby reducing the role player footprint required to support training. We anticipate that this resultant capability will provide significant flexibility for executing virtual and augmented-reality SUDM training by significantly reducing the constant requirement for manned role player support. Because the automated role players are driven by robust AI models and not scripts, training scenarios that use them can be much more dynamic and representative of the trainees operational responsibilities.


Grant
Agency: Department of Defense | Branch: Special Operations Command | Program: STTR | Phase: Phase II | Award Amount: 497.24K | Year: 2016

With the advanced capabilities planned by the TALOS program comes the risk of the operator becoming overwhelmed by the information available and the operation of the suit itself. To effectively employ the suit in the field, TALOS requires an effective situational awareness display and an intuitive, low-impact way of interacting with the suits physical/sensor systems. SoarTech proposes to continue design and development of the User Experience (UE) aspect of TALOS. In this proposed effort, we will focus on two central aspects of a TALOS User Experience: 1) management of mission-critical information to the operator through the helmet HUD; 2) effective user interaction with the information available to the operator and with the suit and its subsystems.


Grant
Agency: Department of Defense | Branch: Army | Program: SBIR | Phase: Phase II | Award Amount: 999.69K | Year: 2015

The Avatar-Administered Neuropsychological Testing (AVANT) system is an automated language-based assessment system that is primarily self-administering with clearly presented directions to a patient using both visual illustrations and avatar-based verbal


Grant
Agency: Department of Defense | Branch: Army | Program: SBIR | Phase: Phase II | Award Amount: 997.03K | Year: 2015

A fundamental challenge facing the Army is that soldiers carry too much weight, dramatically reducing their effectiveness and safety. To alleviate this excessive burden, the DoD has begun investing in robotic mules that move with squads and carry their


Grant
Agency: Department of Defense | Branch: Defense Advanced Research Projects Agency | Program: SBIR | Phase: Phase I | Award Amount: 149.93K | Year: 2015

PREACT (Predictable Robustness for Effective, Adaptive Coordinating Teams) is a planning and coordination service for battle management systems such as DARPAs Distributed Battle Management (DBM) program. PREACT will improve the effectiveness of planning


Grant
Agency: Department of Transportation | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 149.97K | Year: 2015

With the introduction of Level 2 (L2) vehicle automation technologies, a core challenge facing drivers is to remain engaged in the driving task and aware of the vehicle environment despite not playing an immediate role in the low level driving control loop. To overcome this challenge, SoarTech and its partners, UMTRI and Delphi Automotive, propose to develop DAISY, an innovative vehicle sub-system that maintains internal and external situation awareness and is able to the engage the driver in order to maintain safe L2 driving. Rather than removing the driver from the vehicle control loop, DAISY elevates the driver’s involvement to a high-level, supervisory control loop. DAISY leverages two core innovations: computational situation awareness (CSA), which estimates the driver’s engagement and complements the driver’s awareness of the vehicle state, and smart interaction (SI), which provides a natural, multi-modal mechanism for interacting with the user to share situation awareness and engage the user in the driving task. These innovations—CSA and SI—provide a powerful means of maintaining driver awareness without additional workload in order to afford safe, enjoyable L2 automation.


Grant
Agency: Department of Defense | Branch: Air Force | Program: STTR | Phase: Phase I | Award Amount: 150.00K | Year: 2015

ABSTRACT: The rapid continued development of unmanned air systems (UAS) is enabling new mission types, in-creased mission effects, and increased airman safety. However, these advances also present numerous challenges to airman-machine interaction, tactics development, and defense. The rapid development pace has produced a situation where new technologies are outpacing the knowledge of how best to use them. To maximize the effectiveness of automated and semi-automated systems in future conflicts we will develop a testbed that includes predictive models, which airmen can use to train, experiment with, and assess these new capabilities. The Configurable Adversary Response Prediction (CARP) system will provide predictive analytical human decision-making models that are accurate, navigable to systemati-cally explore spaces of predictions, adaptable to match realistic outcomes from data, and easy to inte-grate with existing distributed mission simulation environments. CARPs foundation rests on a sub-stantial legacy of high-fidelity tactical models developed by SoarTech. Our innovative approach will adapt model-building techniques for high-fidelity, data-driven behavior models to enable the systematic navigation of accurate and adaptable predictive behaviors spaces.; BENEFIT: Anticipated DOD Benefits: The research, development, and implementation of CARP will offer the DOD an unprecedented predic-tive what-if analysis capability for complex mission types (such as Anti-Access Area Denial, A2AD). CARPs incorporation of accurate and configurable decision-making and behavior models will support a usable and useful analytical capability that provides the following benefits: 1. Models that generate accurate predictions through a systematic exploration/navigation process. 2. Decision-making models that incorporate modern theories of human reasoning, as well as mod-ern techniques and representations for engineering human decision-making processes 3. The capability to analyze dynamically changing work, mission, and infrastructure configura-tions 4. Easy reconfigurability of red and blue forces, as well as systematic exploration of configuration settings to generate spaces of accurate predictions. 5. Adaptability of the models to increase predictive accuracy with experience and information from real-world and other data, using state-of-the-art machine learning techniques 6. Sharable and fully interoperable models and simulation environments, including existing LVC environments. Potential Commercial Applications: Accurate modeling of decision making is significant win them in corporate environments. The ability to accurately analyze and predict outcomes from decision-maker interactions is useful in training, strategy evaluation, negotiation, and numerous other business activities.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 999.23K | Year: 2014

The revolution in simulation technologies for training is enabling learners to practice and learn in realistic environments. Researchers are developing algorithms that can tailor learner practice to the estimated abilities and needs of individuals, offeri


Grant
Agency: Department of Defense | Branch: Office of the Secretary of Defense | Program: SBIR | Phase: Phase II | Award Amount: 981.32K | Year: 2015

It is understood that an autonomous unmanned air, ground, or sea vehicles can incur a near infinite decision space that is difficult to capture completely in extensive simulation. The response of these vehicles to untrained environments can potentially have unintended consequences to adversely affect safety. This is of particular concern when these vehicles are considered for collaborative manned/unmanned teaming missions. For such systems, a run time verification engine may be developed to ensure the safety of human life by constraining the output of the autonomous algorithm to guarantee actions are correct, interpretable, and recoverable. The autonomous algorithm combined with the failsafe mechanism is intended to improve the robustness of autonomous systems to unknown environments and unexpected events. However, if such a failsafe/recovery mechanism existed, what evaluation systems are available to test their viability and robustness The intent of this solicitation is to develop a verification method to examine the robustness of a run time safety algorithm. The first objective is to examine the techniques presented in [1][2] and apply them to an autonomous unmanned vehicle model that includes a learning trajectory generation algorithm. The techniques in [1] present a method to protect the behavior of an adaptive / learning function. The techniques in [2][3] present methods to analyze the robustness of the implemented safety algorithm. Due to the dependence of autonomous systems on historical state data, current simulation environments require the need for extensive run times to reach a potential unintended operating region. Additionally, as a greater quantity of information is fused and utilized by the autonomous algorithm to make decisions, gradual, unintended data streams may induce state conditions that may cause an unsafe or unpredictable response. A key capability must be to rapidly re-stimulate the system to an untrained, unintended, or erroneous operating state in order to assess the robustness of the run time safety algorithm. The verification algorithm must implement: A method to introduce specific logical or run time operating states that induce an algorithm failure. A mechanism for recording and initializing systems to specific states. An interface control description that emulates real world sensor outputs to be provided to the system under test. The generation of a robustness measure around an operating region [2][3].

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