SBIR/STTR Award attributes
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.
