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Ann Arbor, MI, United States

Boston A.,Soar Technology, Inc.
Energy Policy | Year: 2013

The energy system can only be considered sustainable in the long term if it is low carbon, affordable and secure. These three create a complex trilemma for all stakeholders in the energy business who have to strike a careful balance without neglecting any one aspect. This discussion paper examines the issues surrounding security of supply of the power system which has received less attention than the other aspects. It looks at how threats and mitigation measures can be classified in terms of where they act on the supply chain and the timescale over which they act. Only by considering the full range of timescales from seconds to decades can the full picture emerge of the effects of new technologies on security of supply. An examination of blackouts over the past 40 years sheds light on the causes of failure to supply and the most vulnerable aspects of the supply chain. © 2012 Elsevier Ltd.


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].


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: 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

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