Public showcase · Simulation & Evaluation
Cuttle MCTS Strategy Discovery.
A strategy-discovery workflow that uses Cuttle simulations and counterfactual analysis to improve MCTS and heuristic play.
Metric · 01
MCTS root parallelization and 1000+ game analysis referenced in commits
Metric · 02
Contextual glasses decision rule discovered from 1500-iteration ISMCTS analysis
01 / Showcase Signal
What this showcase helps explain.
Instead of arguing about the best Cuttle openings, this workflow generated simulated games, compared counterfactual decisions, and turned strategy hunches into tested heuristics.
- MCTS root parallelization and 1000+ game analysis referenced in commits
- Contextual glasses decision rule discovered from 1500-iteration ISMCTS analysis
02 / Architecture
How the system is put together.
Cuttle MCTS Strategy Discovery starts with a concrete game ai strategy problem, routes it through MCTS, Simulation, Evals capabilities, and produces an artifact a builder can inspect.
The useful pattern is the boundary between automation and proof: inputs are captured, the AI-assisted step is constrained, and the result is checked through screenshots, source links, tests, reports, or public product surfaces.
03 / Walkthrough
The reproducible pattern.
01
Start from the source artifact
Collect the public URL, local output, report, or dataset that proves the game ai strategy workflow exists and can be inspected.
Input artifact: https://github.com/elawless/cuttle-simulation
Privacy check: Public-safe after checking generated files for secrets.02
Apply the MCTS layer
Use mcts as the main transformation layer, then keep the intermediate result visible enough for review.
03
Publish the proof surface
Ship a screenshot, diagram, report, dashboard, or link that makes the outcome understandable without asking the visitor to trust the description.
04 / Stack
What it leans on technically.
| Tool | Version | Role | Why this tool |
|---|---|---|---|
| MCTS | Current | MCTS | Searches decision trees through simulation so strategy can be measured rather than guessed. |
| Simulation | Current | Simulation | Creates repeatable trials and counterfactuals for comparing decisions safely. |
| Evals | Current | Evals | Keeps generated or automated work accountable through tests, comparisons, and review artifacts. |
| Data Pipelines | Current | Data Pipelines | Moves raw inputs through normalization, enrichment, persistence, and validation. |
05 / Prompts
Copyable prompt surfaces.
Inspection prompt
SystemYou explain AI workflow evidence clearly without inventing private implementation details.
User templateProject: Cuttle MCTS Strategy Discovery Evidence: <artifact> Explain the input, AI step, validation, and public output in plain English.
06 / Evidence
Artifacts you can inspect.
github pr Minimax and heuristic v2 PRhttps://github.com/elawless/cuttle-simulation/pull/1 Available
07 / Privacy & Evidence Boundary
What the public material does and does not expose.
Privacy. Public-safe after checking generated files for secrets.
08 / Next Step