sim_evaluate
Score a player's legal next moves by NEXT-MOVE ELIMINATION (the tic-tac-toe blog technique): compute the expected objective from the given marking with all moves available, then once per candidate with that move's rate zeroed — the move whose elimination loses the most is the best move. Needs the game schema (simulation.objective + simulation.players). Ungated nets use the continuous ODE relaxation; gated nets use exact seeded SSA rollouts, and the response says which — the same rule sim_scenario's "engine" choice follows (docs/engine-selection.md).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | model id | |
| player | Yes | player name from simulation.players | |
| horizon | No | model time to explore ahead (default 3) | |
| marking | No | JSON object, sparse marking override (the position to evaluate from); default = the initial marking | |
| realizations | No | SSA rollouts per elimination (default 40) |