SBIR-STTR Award

The Navy must train and deploy effective teams in a variety of complex domains, from intelligence analysis to field medicine. For example, intelligence analysts preparing the battlespace need a team of experts to interpret a variety of multi-INT sources and incorporate local socio-cultural context, and field medicine often requires a team of doctors skilled in a variety of disciplines. Training individual personnel in all the skills and knowledge required by these missions would be cost-prohibitive and inefficient. Instead, teams are constructed to optimize their collective expertise. To construct these teams, the Navy needs a team development system that manages task and learning assignments to dynamically construct teams with the expertise to achieve evolving mission objectives. To meet this need, we propose to design and demonstrate a system for Group Learning and Optimization of Collaborative Workflows (GLOW). GLOW constructs teams that have the collective expertise required to address complex mission tasks through a combination of dynamic task and personnel ecosystem models, task and learning assignment optimization, and tools for effective team communication and collaboration. Probabilistic predictive models evaluate potential contributors, and a simulated market algorithm distributes tasks and learning assignments to balance personal development and mission requirements.

Benefit:
We expect the full-scope GLOW system to immediately and tangibly benefit Navy talent management performed by the Navy Personnel Command (NPC). We also expect it will benefit commercial, academic, and educational institutions and programs. Commercially, the results of this effort are well positioned to significantly impact talent management and education technologies, a multi-billion dollar market in the United States alone.

Keywords:
Market-based optimization, Market-based optimization, collective expertise, Rapid training, team management/optimization, assessment and evaluation, probabilistic programming, Decision-making, ADAPTIVE LEARNING

The Navy must train and deploy effective teams in complex domains, from intelligence analysis to field medicine. Training individual personnel in all the skills and knowledge required by these tasks would be cost-prohibitive and inefficient. Instead, teams should be constructed to optimize their collective expertise. To construct these teams, the Navy needs a team development system that manages task and learning assignments to dynamically build the expertise required to achieve evolving task objectives. To meet this need, and based on our Phase I success, we propose to design and develop a full-scope system for Group Learning and Optimization of Collaborative Workflows (GLOW). GLOWs Collaborative Workspace will enable teams to decompose complex tasks into work chunks that can be performed by contributing team members, execute (or learn to execute) those tasks while efficiently applying the collective expertise of the team, and assess performance on those tasks through an integrated peer review process. We expect this system to immediately and tangibly benefit the Navy and DoD by enabling collaboration across a number of complex tasks, including collaborative intelligence analysis with DCGS-N

Benefit:
N/aWe expect the full-scope GLOW system to immediately and tangibly benefit the Navy and DoD by enabling collaboration across a number of complex tasks, including collaborative intelligence analysis with DCGS-N. We also anticipate immediate benefits for personnel management; detailers will be able to construct more effective teams with the collective expertise to succeed. Commercially, the results of this effort are positioned to significantly impact the do-it-yourself learning community, a $200 million market in the US and UK alone.

Keywords:
ADAPTIVE LEARNING, Market-based optimization, Argumentation, peer review, probabilistic programming, Crowdsourcing, collaborative analysis, collective expertise ----------- The DoD can maximize their strategic advantage by increasing the scale of collective problem solving to exceed current individual and team limits. Though promising results have been demonstrated for foundational technologies such as Artificial Social Intelligence (ASI), additional technology advances are required to develop more generalized frameworks and cross domain collective problem-solving support. We propose ASI Group Learning and Optimization of Collaborative Workflows (AGLOW). AGLOW will (1) integrate the Charles River ASI agent, from our DARPA ASIST work, into the GLOW framework; (2) extend GLOW with new domain-general processing components and reasoning algorithms; (3) adapt our ASIST ASI agent for a new domain; and (4) deploy the ASI agent via AGLOW to a real-world puzzle-hunt environment, a task that shares similarity to teamwork performed by Intelligence Fusion Cells. We expect AGLOW results to materially benefit the ASIST program, the human-machine-teaming AI research community, commercial products, and military technology transition roadmaps. AGLOW provides reusable data logging and intervention listeners used in real-world deployments, as well as an ontology for domain packages and abstractions for cross-domain and domain-specific activities, which can serve as reference implementations to help other ASI technologies generalize. AGLOW adds social-intelligence team support to commercial products.