Decentralized Artificial Intelligence through Controlled Emergence (DICE)

Decentralized Artificial Intelligence through Controlled Emergence (DICE) 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 that can autonomously

execute sustained, long-time-horizon missions in contested environments while remaining under

control. The goal is to move beyond brittle, centralized orchestration through workflows and ad

hoc agent compositions of AI agents by harnessing self-organization while constraining emergent

behavior to remain predictable, doctrinally consistent, mission-aligned in adherence with user

intent, and resilient to benign failures and adversarial compromise.

This fundamental shift to decentralized intelligence envisioned by DICE is motivated by the need

to transform the existing approach to solving complex problems that require multiskilled teams

operating in dynamic environments, such as responding to natural disasters, advancing scientific

discovery, or engineering complex systems. Future conflicts will unfold at machine speed, and the

current centralized planning and scripted orchestration approach is too slow, too rigid, and too

predictable for the hyper-dynamic, contested environments of the future. Surprise, adaptability,

and resilience will depend on the ability to compose and recompose AI capabilities on demand.

DICE is built for such autonomy-powered, high-tempo battlespace. Imagine a scalable, adaptable,

and resilient force of autonomous systems that can execute complex, long-term missions in

contested environments without a human explicitly directing every action while remaining under

human control and in adherence with user’s intent. These autonomous forces will be able to sense

a change in the battlespace and adapt accordingly. It is a future where success is measured by the

relentless, creative ability of an autonomous collective to execute missions in the face of chaos

and uncertainty. This is a profound and necessary departure from the past.

To enable this vision, DICE seeks to build a highly scalable decentralized coordination architecture

that decomposes complex goals and fuses distributed situational context across specialized agents

over long-time-horizons. The architecture will support multistep interaction among agents,

iterative information gathering under partial observability, adaptive strategy refinement as the

environment evolves, and resilience to broken links, conflicting information, agents going rogue

by developing instrumental goals misaligned with mission, and the loss, failure, or adversarial

compromise of individual agents. Through peer-to-peer coordination and resilient distributed

consensus, agents will be able to self-organize into teams, allocate roles, and fuse conflicting,

incomplete, or adversarial information without waiting for a central human or machine planner. In

DICE, AI capabilities will emerge from the interactions among heterogeneous agents, extending

beyond the intelligence of individual agents.