SBIR/STTR Research Award

A Comprehensive Framework to Develop, Refine, and Validate Learning Agents for Tactical Autonomy

Award identifier 2518ee61814dd3957dca811f1dca3f2095a3b363b3b3a64b3a14edf7ee0a0ba2Awarding agency Department of Defense · Air Force

Award amount

$149,633

Program and phase

STTR · Phase I

Award year

2015

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Award details

Awarding agency
Department of Defense
Branch
Air Force
Recipient company
ORBIT LOGIC INCORPORATED
Proposal award date
2015-07-29
Contract end date
2016-04-29
UEI
EHX1E95M1144
Solicitation number
2015.1
Topic code
AF15-AT14

Research abstract

ABSTRACT: To maintain superiority in the battle domains of space, ground, air and sea, it is natural that the U.S. move toward incorporating autonomous capabilities to enhance/replace the traditional role of humans. Continued advances in processing capability and techniques to employ decision logic and adaptive learning strategies will soon make this vision reality. To deploy any such capability operationally, however, requires a significant level of trust. Building this trust can only be accomplished by employing a comprehensive tool environment enabling high-fidelity battlespace simulations consisting of blue and red actors. Actors will be a mix of humans and agents employing human-like decision logic. Agents participating in these scenarios must be capable of learning from their experiences, and the framework must support the learning process by challenging the actors in ways that maximize learning potential. Orbit Logic and the University of Colorado propose a framework, leveraging a high-heritage distributed simulation environment to host agents. Agent behaviors, in combination with other simulation data sources, will be collected by an agent-learning engine and utilized to improve the actions of the agents both in terms of the breadth of situations that can be recognized and in the effectiveness of its responses to those situations. ; BENEFIT: Completing and commercializing the proposed framework would have nearly immeasurable benefit to all battle domains in the U.S. military purview. Once a basic infrastructure is created to mature and deploy agent-based capabilities, Moores law will apply to the capabilities that can be expected from the agents. Humans are unlikely to ever be completely removed from the decision process, but there is an optimum blend of human and machine strengths that results in the most effective system. This framework will assist in finding that balance. The capabilities of the proposed framework have benefits that apply to virtually any domain that requires human decision making or oversight. While we will be targeting military use cases, the fundamental approaches will be extensible to commercial UAS operations, process control, satellite operations, traffic management, and many more applications in the civil and commercial realm.

Technology keywords

Not reported

Other awards by this recipient

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Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

24 SBIR/STTR records share this UEI (2010–2022). Reported award amounts total $10,067,393. Reported amounts are not federal obligations.

Phases: Phase I, Phase II, Phase II – Direct to Phase II

Agencies: Department of Defense, National Aeronautics and Space Administration

Agency intelligence

Department of Defense

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