Machine Clustered and Labeled Decision Tracks Derived from AI-enabled Intent Recognition
Navy SBIR 2020.1 - Topic N201-077
ONR - Ms. Lore-Anne Ponirakis - firstname.lastname@example.org
Opens: January 14, 2020 - Closes: February 26, 2020 (8:00 PM ET)
AREA(S): Human Systems, Information Systems
PROGRAM: Minerva INP; MTC2 (PMW150); TSOA (MC3)
technology within this topic is restricted under the International Traffic in
Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and
import of defense-related material and services, including export of sensitive
technical data, or the Export Administration Regulation (EAR), 15 CFR Parts
730-774, which controls dual use items. Offerors must disclose any proposed use
of foreign nationals (FNs), their country(ies) of origin, the type of visa or
work permit possessed, and the statement of work (SOW) tasks intended for
accomplishment by the FN(s) in accordance with section 3.5 of the Announcement.
Offerors are advised foreign nationals proposed to perform on this topic may be
restricted due to the technical data under US Export Control Laws.
Develop a watchfloor decision aid service, enabled by recent advances in gaming
artificial intelligence (AI),that, to be operationally relevant, must regulate
the frequency of recommendations and improve their explainability; and that
will identify clusters of sub-decision tracks within a decision track for an
AI-enabled game plan in which a similar objective or state was met.
The goal of this SBIR topic is to understand the mechanisms of AI-enabled game
play in order to produce optimal strategies for multiple objectives and game
states. As the Navy moves towards leveraging AI for decision support, maturing
intelligent algorithms for execution plans and explainable AI is imperative. AI
algorithms have been shown to produce not only optimal or close to optimal
solutions, but also a larger set of eclectic strategies otherwise not derived
by humans. An understanding of decision tracks leading to differing
solutions/strategies will enable the Navy to be strategic given different
mission states. The Navy seeks AI that recommends plans that consist of a set
of clustered micro-tasks that optimally lead to the achievement of a specific
Demonstrate the feasibility of developing operationally relevant techniques to
cluster and label decision tracks as plans in an AI-enabled game. Conduct a
detailed analysis of literature, commercial capabilities, and state-of-the-art
AI/ML techniques relevant to this topic. Identify and begin to mitigate key
technical risks to a Phase II prototype. Demonstrate progress. Develop Phase II
plans with a technology roadmap, development milestones, and projected Phase II
Move development of prototype techniques from a commercial game to a military
simulator such as JSAF, OneSAF, or NGTS. Agent interfaces using JSON messaging
can be leveraged. Develop and test against an increasingly complex mission plan
that spans all warfighting domains. Develop metrics for decision track
clustering and similarity measures. Attempt to identify or develop decision
track rankings within clusters. Demonstrate an end-to-end AI-enabled capability
at the plan level for at least 3 mission contexts (e.g., sea control or
amphibious assault). Work with programs of record and training sites to
transition the Phase II prototype.
DUAL USE APPLICATIONS: Produce a final prototype capable of deployment to training
centers, operational command and control centers, and as a virtual application.
Adapt the system to transition as a component to a larger system or as a
standalone commercial product. Provide a means for performance evaluation with
metrics for analysis (e.g., accuracy of assessments) and a method for operator
assessment of product interactions (e.g., display visualizations). The Phase
III system should have an intuitive human computer interface. The software and
hardware should be modified and documented in accordance with guidelines
provided by the engaged Programs of Record and any commercial partners.
Technology development should be applicable to any domain that requires the
training of end to end AI for a complex game or mission simulation.
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Alexander (Sasha), Mnih, Volodymyr, Agapiou, John, Osindero, Simon, Graves,
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Zeming, Gehring, Jonas, Khalidov, Vasil and Synnaeve, Gabriel. “STARDATA: A
StarCraft AI Research Dataset.” https://arxiv.org/abs/1708.02139
Sean, Dodge, Jonathan, Hilderbrand, Claudia, Anderson, Andrew, Simpson, Logan
and Burnett, Margaret. “Toward Foraging for Understanding of StarCraft Agents:
An Empirical Study.” https://arxiv.org/abs/1711.08019
Artificial Intelligence; StarCraft; Decision Support; Deep Reinforcement
Learning; Machine Learning; Plans; AI; ML