Request for Proposal (RFP) for Transformational Model – Battle Management Generate Battle Courses of Actions (GBC) Decision Advantage Sprint for Human-Machine-Teaming (DASH) FA238425SRHW3
Summary
AI-generated · Aug 24, 2025Develop a Transformational Model for Battle Management: Generate Battle Courses of Action (GBC) within the Human-Machine-Teaming (HMT) DASH effort, to streamline how decisions are made by machines and humans. The model will take the output of a Match Effectors decision, determine supporting effects, and construct a network of actions (a web of events) needed for the principal effect to be achieved, potentially presenting COAs and their execution paths via a hypergraph. The goal is to generate more COAs than manual analysis and speed up, improve accuracy, and increase operator confidence in the HMT solution.
Proposals will be evaluated on software attributes such as decision speed, correctness, completeness, and user experience. Experiment details will be provided with the RFP. The effort focuses on how many opportunities are recognized or missed, how quickly the HMT can decide, and how confidently the human operator can rely on the solution.
While perhaps streamlining and expediting battlespace information transport and display, current and emerging C2 systems still foist battle management decision-making on the humans. One of these decision functions is Generate Battle Courses of Action (GBC). GBC considers which units, agencies, formations, platforms, or weapon systems individually or as pre-arranged force packages potentially can and may achieve a particular effect, and rank-orders those potential matches. GBC takes a matched effect-effector set output from a Match Effectors decision (i.e., the principal effect, deliverable(s), & effector(s)), determines what other effect(s) support the principal effect, and builds a web of events representing the actions necessary for the principal effector(s) to achieve the principal effect, and for all participants to arrive in some post-execution disposition. The GBC DASH aims to answer the following core questions: How many decision opportunities are recognized/missed? How fast can the HMT make its decision? How accurate or error free are the HMT s decisions? How confident is the human operator in the HMT s solution? What are some technical software attributes/requirements that must be considered along with the functional requirements? Since GBC is expected to generate more courses of actions than manual data collection and analysis processes can derive insights, a hypergraph may be the best presentation of COAs and associated paths. Software attributes will be assessed based on HMT decision speed, correctness, completeness, and user experience. Experiment details will be distributed along with this RFP.
From Solicitation posted on Jul 11, 2025Notice history
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USA