152 lines
13 KiB
Markdown
152 lines
13 KiB
Markdown
# Findings
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> Last Updated: 2026-04-09T00:00:00.000Z
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## Summary
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Initializer refresh for SCOPONE-0009. The cached findings were stale relative to the live source tree, so the observations below reflect the current Phaser, worker, and AI implementation.
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## Codebase Observations
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- Primary gameplay code currently lives in 10 TypeScript source files under `src/`; the Android wrapper adds 3 Java files.
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- The project is structurally split between framework-free gameplay modules in `src/game/` and Phaser scene code in `src/scenes/`.
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- `src/scenes/GameScene.ts` and `src/game/ai.ts` remain the two largest concentrations of application logic.
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- The AI transport layer is now a stable three-file path: `ai-worker-protocol.ts`, `ai-worker-client.ts`, and `ai.worker.ts`.
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- The AI exposes three difficulty levels: `beginner`, `advanced`, and `master`.
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- `advanced` and `master` both use `CardTracker` to reason about unseen cards without directly reading hidden hands.
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- The current `master` search profile is `timeBudgetMs: 4600`, `sampleCount: 10`, `maxDepth: 6`, `batchSize: 2`.
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- `GameScene` consumes AI progress callbacks to update an on-screen think bar while a worker request is running.
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- `AIWorkerClient` fails over pending work to in-thread `chooseMove()` if worker creation, posting, or deserialization fails.
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- The Android wrapper targets SDK 36 with `minSdkVersion` 24 and applies immersive mode from the native activity.
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- Audio remains procedural via Web Audio; no dedicated audio asset pipeline is present in the source tree.
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- No ESLint or Prettier configuration is present.
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- The only repository-wide verification command supplied is `npx tsc --noEmit`.
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## Potential Improvement Areas
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- `GameScene.ts` still centralizes layout, turn flow, HUD updates, effects, and audio in one scene class, which raises maintenance cost.
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- `ai.ts` still combines heuristic tiers, inference helpers, determinization, and alpha-beta evaluation in one module.
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- Worker transport is isolated cleanly, but progress rendering remains coupled to scene-level UI concerns.
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- A 4600 ms master search budget may still be noticeable on slower mobile devices even with batch yielding.
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- There is no dedicated automated rules or AI test suite beyond type-checking.
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- Formatting and style are enforced socially rather than by automated linting or formatting tools.
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## Current Rule / Implementation Notes
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### Capture behavior in `engine.ts`
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- Direct-match capture has priority over subset-sum capture.
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- When multiple direct matches exist, `findCaptures()` returns one single-card option per matching card.
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- Subset-sum captures are considered only when no direct match exists.
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- `applyMove()` defaults to the first legal capture if no explicit capture choice is supplied.
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- Scope is awarded only when a capture clears the table before the final play of the round.
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### AI implementation snapshot
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- `beginner` uses a simpler heuristic with noise to remain beatable.
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- `advanced` adds race awareness, anti-scopa logic, partner setup, anchor play, and tracker-based probability estimates.
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- `master` orders legal moves with a quick evaluator, samples hidden hands, and scores them with alpha-beta search under the active deadline.
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- Progress is reported through `AIDecisionProgress` so the scene can keep the think bar responsive.
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### Worker execution snapshot
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- `GameScene` creates `AIWorkerClient` during `create()` and disposes it on both `shutdown` and `destroy`.
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- `AIWorkerClient` serializes `CardTracker` state through `toSnapshot()` instead of attempting to transfer the class instance.
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- `ai.worker.ts` rebuilds tracker state with `CardTracker.fromSnapshot()` before calling `chooseMove()`.
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- Progress, result, and serialized error payloads all travel through `ai-worker-protocol.ts`.
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- If worker execution becomes unavailable, pending requests are rerun with the in-thread AI path rather than being dropped.
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### Scene / UI implementation snapshot
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- `BootScene` loads atlas assets and presents a simple loading bar.
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- `MenuScene` exposes difficulty selection before match start.
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- `GameScene` tracks played and captured cards in `CardTracker` as the round evolves.
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- The scene owns score HUD rendering, player labels, status text, think-bar rendering, and procedural particle effects.
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- Round-end and match-end flows remain managed inside the scene instead of separate overlay components.
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## Research Performed
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### Web Research: Scopone Scientifico Rules (2026-03-31)
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**Sources**: Wikipedia (*Scopa* article, Scopone section), Pagat.com (*Scopone* page by John McLeod)
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#### Core Rules (Scopone Scientifico variant)
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- 4 players, 2 fixed teams of 2 (sit opposite): Team A = players 0+2, Team B = players 1+3.
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- 40-card Napoletane deck: 4 suits (`bastoni`, `coppe`, `denara`, `spade`), values 1-10.
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- All 40 cards are dealt at the start of the round; the table begins empty.
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- Turns advance in the implementation as `0 -> 1 -> 2 -> 3`.
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#### Capture Rules
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1. If the played card matches one or more table cards by value, a direct match must be taken.
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2. If multiple direct matches exist, one matching table card is chosen.
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3. If no direct match exists, a subset of table cards may be captured when their values sum to the played value.
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4. A direct match has priority over any possible sum capture.
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5. Scopa awards a point only when the table is cleared before the final play of the round.
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#### Scoring
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| Category | Rule |
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|----------|------|
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| Carte | Majority of captured cards |
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| Denari | Majority of `denara` suit cards |
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| Settebello | Team that captures the 7 of `denara` |
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| Primiera | Highest best-of-each-suit prime value |
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| Scope | One point per scopa |
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#### Primiera values used in code
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| Card value | Primiera value |
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|------------|----------------|
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| 7 | 21 |
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| 6 | 18 |
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| 1 | 16 |
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| 5 | 15 |
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| 4 | 14 |
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| 3 | 13 |
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| 2 | 12 |
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| 8, 9, 10 | 10 |
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### SCOPONE-0008: AI progress rendering notes (2026-04-02)
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- The current implementation does not use Phaser `TimerEvent` progress helpers.
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- Instead, `chooseMove()` emits its own normalized progress payload through `AIDecisionProgress`.
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- `GameScene.updateThinkBar()` renders remaining time from that callback.
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- The yielding behavior in the master search path is necessary so the browser can repaint while search batches continue.
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### SCOPONE-0009: Phaser scene lifecycle notes (2026-04-08)
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- Source: Context7 `/websites/phaser_io_api-documentation`, query `Phaser 3.87 Scene lifecycle create restart shutdown destroy event listeners scene restart preserving external state`.
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- Phaser dispatches `shutdown` when a scene stops being active but may be re-used later; resource cleanup that should also cover final teardown can additionally listen to `destroy`.
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- The current `GameScene` pattern of registering one-shot shutdown and destroy handlers is aligned with Phaser guidance for worker disposal and UI cleanup.
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- Dealer rotation and next-round state changes can stay inside the existing in-scene orchestration without requiring a different Phaser lifecycle primitive.
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### SCOPONE-0009: Iteration 3 strength-planning notes (2026-04-08)
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- `src/game/ai.ts` currently generates master determinization samples by uniformly shuffling all unseen cards and slicing them into opponents' hidden hands; it does not yet bias assignments by dealer role, parity residue, or observed capture semantics.
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- The transposition-table key in `src/game/ai.ts` includes the exact sampled hidden hands, so reuse is effective within a determinized sample but does not merge equivalent uncertainty classes across different sample assignments.
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- No executable benchmark harness or AI quality test module exists under `src/`; the current timing evidence lives only in prompt artifacts such as `prompts/SCOPONE-0009/iteration_2/benchmark_summary.md`.
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- `tsconfig.json` includes only `src`, so any automated quality or self-play harness that should be typechecked by the default `npx tsc --noEmit` command needs to live under `src/` unless the project configuration changes.
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### SCOPONE-0009: Iteration 3 continuation notes (2026-04-09)
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- The accepted iteration 3 benchmark work is now present in source: `src/game/ai-benchmark.ts` and `src/game/ai-benchmark-fixtures.ts` exist under `src/`, `package.json` exposes `benchmark:ai-quality`, and the harness already measures fixed fixtures, self-play, and production-master timing.
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- The live production master budgets in `src/game/ai.ts` are already below the requested five-second ceiling in every shipped branch: base `4300`, `<= 20 cards` `4350`, `<= 12 cards` `4200`, `<= 8 cards` `3900`, `<= 6 cards` `3600`, and `<= 4 cards` `3200` milliseconds.
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- `src/scenes/GameScene.ts` still executes AI turns immediately after `await aiClient.chooseMove(...)` resolves in `doAIMove()`; there is currently no scene-level minimum think-time floor.
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- `src/scenes/GameScene.ts` still uses a bare `setStatus(msg)` helper that only calls `this.statusText.setText(msg)`; there is no timed persistence policy, no cancellation of prior status timers, and no dedicated post-move outcome message path.
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- Phaser 3.87 scene timers can be cancelled with `TimerEvent.remove()` and their references cleaned with `TimerEvent.destroy()`; the current scene already listens to `shutdown` and `destroy`, so timed status cleanup belongs in the existing `handleSceneShutdown()` path.
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### SCOPONE-0009: Iteration 3 refresh notes (2026-04-09)
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- The current `src/game/ai.ts` heuristic does not reason about numeric even/odd card values; it already computes the unseen copy count for each rank and stores whether the remaining copies for that rank are in a singleton residue or a paired residue, but the internal names still use `oddResidue`, `evenResidue`, and `scoreParityTableState`, which can mislead future work.
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- The live tactical seam that needs refresh is therefore naming and policy framing, not a wholesale replacement of the underlying signal: the AI should explicitly treat `apparigliare` / `sparigliare` as preserving or breaking same-rank copy residues and connect that to table control, scopa prevention, and forced replies.
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- The accepted benchmark harness in `src/game/ai-benchmark.ts` still measures runtime with `performance.now()` and therefore depends on wall-clock search time. It does not yet use an injected or simulated search clock for fast validation runs.
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- `src/scenes/GameScene.ts` already contains the previously planned pacing and status work: `AI_MIN_THINK_MS = 1000`, `MOVE_OUTCOME_STATUS_MS = 2000`, a timer-backed `setStatus(...)`, and `handleSceneShutdown()` timer cleanup are all present in source and should be preserved rather than re-planned.
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- `src/game/ai-benchmark-fixtures.ts` still contains one fixture and tag using the stale label `dealer-parity-preserve-pair` / `critical-dealer-parity`; if benchmark files are reopened for simulated timing, that terminology should be refreshed to rank-residue wording at the same time.
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### SCOPONE-0009: Iteration 5 planning notes (2026-04-09)
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- The live AI quality harness in `src/game/ai-benchmark.ts` still hard-codes an `iteration: 4` quality gate with targets of `12` fixed fixtures, `4` critical concepts, and `48` self-play matches requiring `>= 30` wins and `<= 12` losses; the readable summary does not yet surface cross-seed aggregation such as the recurring dual-loss seeds from the latest rejected run.
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- `src/game/ai-benchmark-fixtures.ts` currently covers `settebello-capture`, `anti-scopa-defense`, `dealer-rank-residue-preservation`, and `exact-endgame-resolution` as critical concepts, but it does not yet encode an explicit critical fixture for partner invitation / partner scopa setup and does not yet make `fare scopa` itself a critical concept despite the user's new ordering.
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- Non-critical fixtures already exist for denari pressure, late denari shielding, and seven pressure, so the benchmark seam for iteration 5 is to rebalance critical-vs-fixed coverage and ordering expectations rather than to introduce a second harness.
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- Cross-tier heuristic priorities are concentrated in `src/game/ai.ts`: beginner logic in `scoreCaptureBeginner()` / `scoreDumpBeginner()`, advanced logic in `scoreCaptureAdv()` / `scoreDumpAdv()`, and master root/search logic in `quickEval()`, `orderSearchMoves()`, `generateSamples()`, and `evaluateFast()`.
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- Partner-aware logic already exists in all three tiers, but it is currently additive and distributed across multiple heuristics; there is no single explicit priority ladder that guarantees `partner setup` outranks seven denial, denari denial, and generic material capture across the whole file.
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- Anti-scopa prevention is already strong enough to pass the fixed tactical fixtures, but the rejected iteration 4 result (`18` wins, `30` losses over `48` seeded self-play matches) indicates that full-game strength is still limited by strategic continuity across seed-intrinsic lines rather than by isolated tactical blindness.
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