In the Smart Environment article, we explored how agents can avoid crowding by reserving paths. One NPC declares its intended route, and the environment marks it so others naturally pick alternate paths. This simple mechanism creates emergent flanking and tactical spread without requiring direct communication.

Now let’s extend that idea further. Instead of only coordinating on where to go, what if agents also coordinated on what actions to take? In a squad, some roles are urgent (like breaching a door) while others are supportive, like providing cover fire or watching a flank. If every agent tried to do the same thing, coordination would collapse.

Some games have attempted to solve this with a centralized AI system, but that approach is often complicated to implement, hard to debug, and brittle to maintain. A single bug in the “commander” can break every unit’s behavior. A more robust, scalable approach is to let agents share knowledge through a Blackboard Architecture.

What is a Blackboard?

A Blackboard is a shared data structure designed to carry many types of information: integers, floats, booleans, vectors, or even complex objects. Think of it as a key-value store, where each value is indexed by name (like TargetLocation, CoverPoint, or IsBreacherAssigned).

The critical rule is that each key’s data type must be known and respected. An integer cannot be interpreted as a vector, and vice versa. This type safety ensures that when agents or systems query the blackboard, they know exactly how to use the information.

Modern engines like Unreal Engine 4/5 use blackboards primarily to pass important data between branches of a Behavior Tree. Designers often rely on them to share context like “enemy actor” or “move-to position” within an NPC’s own logic.

But the original blackboard concept predates Unreal. It was first used in multi-agent coordination systems, where the blackboard acted as a shared memory for an entire squad or team, not just a single agent.

From Behavior Trees to Tactical Coordination

Imagine a squad of NPCs approaching a fortified room. Instead of each one independently deciding what to do, they coordinate through the blackboard:

• Agent A posts: “Breaching Door – Claimed.”

• Agent B sees that breaching is covered → chooses “Provide Cover Fire.”

• Agent C sees both taken → posts “Flank Right.”

Each NPC checks the blackboard before committing. If a role is already claimed, they select a different one. This transforms what could be chaotic overlap into emergent squad-level coordination.

The beauty of this approach is that no central commander dictates roles. Coordination arises naturally as agents negotiate responsibilities through the shared data.

Extending the Concept: Tasks and Coordination

This builds directly on the terrain analysis idea from influence maps. There, agents reserved paths so others wouldn’t crowd the same route. With blackboards, the same principle applies to tasks, but instead of raising a cost, actions are explicitly blocked once claimed.

• Tasks are posted to the blackboard (breach, cover, flank, secure).

• Each agent scores tasks based on urgency or utility.

• When an agent claims a task, it marks that action as filled on the blackboard.

• Other agents see the task is taken and automatically move to unclaimed roles.

If no alternatives exist, agents may still overlap, but only when necessary. This ensures that required roles are always covered, while avoiding wasted duplication (like three NPCs all trying to breach the same door).

Utility-Based Task Allocation

Coordination doesn’t need to be purely urgency-driven. With Utility Systems, each agent can choose tasks based on how well-suited they are.

Imagine a squad with four characters:

• A Sniper, who has high utility for long-range cover.

• A Medic, who naturally prioritizes healing.

• An Assault, who excels at breaching.

• A Support, who can flex into multiple roles.

Each agent evaluates all possible tasks and picks the one with the highest utility. But once a task is claimed, it is posted to the blackboard. Other agents immediately see it as blocked and move on to other options.

This prevents overlap while still letting agents play to their strengths. The utility system provides a clear way to score priorities, while the blackboard ensures those priorities stay coordinated across the group.

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Benefits of Blackboard Coordination

Blackboard systems offer several advantages for tactical AI:

• Simplicity: Avoids the complexity of centralized commander AI.

• Flexibility: Any type of data (location, role, state) can be stored and retrieved.

• Scalability: Works for small squads or large groups of agents.

• Emergence: Coordination arises naturally, not from rigid scripting.

• Integration: Works seamlessly with Behavior Trees, Utility Systems, Smart Objects, and Smart Environment.

Conclusion

Blackboard Architecture transforms a simple shared container into a powerful coordination tool. From reserving paths to dividing tactical roles, it scales AI behavior without bloating individual agents or relying on fragile centralized controllers.

Combined with Utility Systems, Smart Objects/Smart Environment, and Influence Maps, blackboards complete the picture of a lean, extensible toolkit for smarter AI, systems that look coordinated and tactical while remaining simple under the hood.

If your team wants to explore how blackboards or other coordination techniques can empower your NPCs, check out our Game AI service. We help studios design scalable AI that balances performance, simplicity, and immersion.

FAQ: Blackboard Architecture and Tactical Coordination

What is a Blackboard in game AI?
A Blackboard is a shared key-value store used by AI systems to exchange information. Each key has a name (e.g., TargetLocation) and a type (integer, float, vector, boolean), so agents or systems know how to use the value correctly.

How is a Blackboard used in Unreal Engine?
In Unreal Engine 4/5, blackboards are used mainly to pass important data between branches of a Behavior Tree for example, storing the current enemy target or move-to location. This is a local, per-agent use of the concept.

How does Blackboard coordination differ from Behavior Trees?
Behavior Trees handle one agent’s decision-making. A tactical blackboard extends the concept to multiple agents, letting them post and check tasks. This way, NPCs coordinate their roles (breach, cover, flank) without a central commander AI.

How does this prevent NPCs from overlapping on the same task?
When one agent claims a task, it posts it to the blackboard as blocked. Other agents see that it’s already taken and choose different actions. This avoids wasted duplication, like multiple NPCs trying to breach the same door.

Can Utility Systems be used with a Blackboard?
Yes. Each agent can evaluate tasks with a utility score (based on skills, persona, or urgency). Once a task is claimed, the blackboard invalidates it for the rest. This keeps squads coordinated while still letting each agent play to its strengths.

How does this relate to terrain analysis?
In influence Maps, terrain analysis raised the cost of crowded paths to spread NPCs out. With blackboards, the same idea applies to tasks: once an action is claimed, it’s blocked for others. Both approaches reduce duplication and encourage emergent coordination.

Why not just use a central commander AI?
Centralized AI can work, but it’s often complicated to build, difficult to debug, and brittle to maintain. Blackboard coordination is decentralized: agents share responsibilities by reading/writing to a common memory, creating scalable and flexible behavior.

Does this require a custom engine?
Not at all. Blackboards can be implemented in most engines. Unreal Engine already supports them locally through Behavior Trees, and extending them to multi-agent coordination is mostly a matter of design.

How does a blackboard support multi-agent AI systems?
Blackboard architecture allows multiple agents to read and write to a shared memory structure. This enables multi-agent coordination without a central controller, reducing complexity and improving scalability.

Can blackboards be used for squad-level AI systems?
Yes. In squad AI systems, agents use the blackboard to claim roles like breacher, sniper, or support. This prevents overlap and creates emergent tactical coordination across the entire squad.

Is blackboard architecture a decentralized AI approach?
Exactly. Instead of relying on a single commander AI, blackboard systems create decentralized game AI where each agent makes decisions based on shared knowledge rather than hierarchical commands.

How does a blackboard compare to shared memory systems?
A blackboard is a specialized shared memory system with structure, type safety, and named keys. It allows agents to exchange information cleanly, whereas raw shared memory lacks organization and intent.

What is the role of utility systems in blackboard coordination?
Utility systems help agents rank tasks based on suitability or urgency. The blackboard ensures that once a task is chosen, it is marked as taken, preventing duplication and improving task allocation across the team.

How does blackboard architecture create emergent coordination?
Because agents read the same shared data, they naturally divide roles and avoid collisions without explicit scripting. This leads to emergent coordination that feels intentional but is driven by decentralized logic.

When should I use Unreal Engine’s blackboard instead of a tactical blackboard?
Use UE’s blackboards for single-agent behavior tree logic (like storing an enemy target). Use a tactical blackboard when coordinating multiple agents or roles across a squad. Both systems can coexist in the same AI.

Is blackboard AI good for role-based game AI?
Yes. Blackboard architecture shines when characters have specialized roles (healer, sniper, breacher). It keeps role assignments organized and prevents multiple agents from competing for the same job.