"Agent" on these pages means a game agent: the trained player. The AI agents that build and run the project are covered under autonomy.
The shape of it#
real game server ──► perception layer ──► observation ──► agent
▲ (what a player could │
│ know, when they'd know it) │
└──────────── agent interface ◄──── action ◄─────────┘Every agent, however it works, is seated on a bot character on the real server. It gets observations only from the perception layer and acts only through the agent interface. It never holds a game connection of its own, so there is no way around the limits.
What is fixed and what isn't#
| Fixed, so results compare over years | Replaceable as methods improve |
|---|---|
| The real game server | Agents and their models |
| The perception layer and its principles | Training methods |
| The agent interface | Simulators |
| The loadout and comp format | Compute |
| How strength is measured |
The plan for agents#
- A scripted baseline for each starting team: hand-written play that is decent and predictable. It is the first opponent and the fixed yardstick.
- Learning takes over wherever it proves stronger than the script, measured by rating on the real server.
- A gradient: earlier agent versions fill the ratings below the top, so a human of any skill finds a fair opponent.
None of this has started yet; the harness it needs is being built now.
Pages in this section#
| Page | What it covers |
|---|---|
| Human parity | What agents may perceive and how fast they may react: the parity principles, and the one layer that enforces them. |
| The harness | The machinery agents play through: bots created per match, one interface in and out of process, the match recorder and the virtual clock. |
| Measurement | How strength is judged: win rate on the real server, ratings with confidence intervals, fixed anchors, and how many matches it takes to tell two agents apart. |