HEROES: Scalable AI for Autonomous Mission Management

Developed for intelligent multi-agent tasking in dynamic, complex environments

Overview

Autonomy that adapts, collaborates and gets the job done.

Aeris-UK developed a scalable AI framework that enables distributed autonomous systems to manage shifting priorities in fast-changing environments – handling complex missions with minimal oversight and maximum resilience.

The Challenge

Making autonomy work when the plan can’t stay fixed – or connected

Many autonomous systems can follow a path – far fewer can choose one. The challenge was to build intelligent agents that could not only carry out tasks, but also decide which ones mattered most, moment by moment.

Operating with limited or intermittent communication – or where staying silent is preferable – each agent needed to make local decisions while supporting a shared goal. The system had to be robust to agent failure and flexible enough to reassign tasks as the situation evolved. It needed to scale and adapt across domains – air, land, maritime or digital – without relying on centralised control.

Our Approach

Layered intelligence – distributed agents, united goals

We developed a three-tier Hierarchical AI framework to manage autonomous tasking across multiple agents, powered by decentralised multi-agent reinforcement learning:

High-level agents prioritise tasks based on mission goals and progress
Mid-level agents turn those priorities into tactical assignments like search or tracking
Low-level agents execute movement, observation and real-time actions

This structure supports scalable, adaptive autonomy, even with degraded or limited communications. Agents learn to coordinate and act independently without relying on constant contact or central control.

Training took place in a high-fidelity simulation, built for realism and speed – including uncertainty, cluttered terrain and ambiguous signals. The result: agents that choose their own actions, respond to evolving conditions and work together – without needing to ask or be asked.

The Outcome

From fixed plans to adaptive mission management

The HEROES system delivered flexible, intelligent coordination across distributed autonomous agents. Each part of the system could act independently but contributed to a common goal.

Agents learned to select the right tasks at the right time, adapt as new information emerged and avoid duplication or conflict. The architecture is effective not just in one domain but as a generalisable model for complex autonomous tasking – whether tracking movement across a landscape, coordinating mobile sensors or managing responses in a dynamic network.