The DICE program seeks to develop the theory and algorithms for decentralized coordination and local inference control to enable a scalable, adaptive, and resilient collective of heterogeneous AI agents that can autonomously execute sustained long-time-horizon missions in contested environments while remaining under human control. In contrast to small-scale, rigid, and fragile centralized orchestration or the high-risk unpredictable nature of ad hoc compositions of AI agents, DICE aims to harness the scalability and adaptability of self-organizing systems while minimizing risks and ensuring that the collective behavior remains predictable and aligned with intended outcomes. This approach mirrors the principles of decentralized self-organization that underpin the internet's own scalability and resilience, where robust global behavior emerges from simple, local rules. Funding Opportunity Number: HR001126S0010. Assistance Listing: 12.910. Funding Instrument: CA,O,PC. Category: ST.

DICE program (Decentralized Coordination and Local Inference Control for Heterogeneous AI Agents) is sponsored by Department of Defense (DOD) (presumably DARPA given focus). The DICE program aims to develop the theory and algorithms for decentralized coordination and local inference control to enable a scalable, adaptive, and resilient collective of heterogeneous AI agents.

Decentralized Coordination and Local Inference Control (DICE) is sponsored by DARPA (Defense Advanced Research Projects Agency). The DICE program seeks to develop theory and algorithms for decentralized coordination and local inference control to enable scalable, adaptive, and resilient collectives of heterogeneous AI agents for sustained long-time-horizon missions in contested environments.

Ice Control for cold Environments (ICE) program is sponsored by Defense Advanced Research Projects Agency (DARPA). DARPA's ICE program aims to develop new materials that control the physical properties of ice crystals to facilitate operations in extreme cold weather environments, which can pose a variety of risks to both personnel health and critical equipment.

Improving Battle Planning through AI is sponsored by Defense Advanced Research Projects Agency (DARPA). This SBIR topic seeks proposals for developing novel technologies that decouple course of action adjudication from course of action planning through the use of reduced-order models. The goal is to construct customized reduced-order models that generate principal components from simulated and real data sources for estimating the modes and eigenvalues of the composition operator defining the evolution of the common operating picture in response to an executed course of action. This will enable the development of surrogate models for simulation and war-gaming environments, significantly accelerating the adjudication process.