commit 7df3ddd793a69cba93ded966d634045f4810a5fc
parent 2644610c24ac12d9ef707571aec1e31a934389a8
Author: Brian Graham <brian@buildingbetterteams.de>
Date: Fri, 10 Apr 2026 21:06:34 +0200
Correct attribution: Pierre Dellacherie's 4-heuristic Tetris AI
The algorithm is from Pierre Dellacherie (2003), not LeeYiyuan.
Weights are from Colin Fahey's GA optimization. LeeYiyuan/tetrisai
is the reference implementation we adapted code from.
Updated methodology page, SPEC.md, player.ts, bot.ts, CLAUDE.md.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Diffstat:
5 files changed, 17 insertions(+), 13 deletions(-)
diff --git a/CLAUDE.md b/CLAUDE.md
@@ -85,7 +85,7 @@ All evaluation is deterministic code. No LLM grading.
2. `quality.sh` - ESLint, typecheck, bundle size
3. `code-analysis.py` - 14 code quality metrics
4. `transcript-analysis.py` - agent behavior from conversation log
-5. `gameplay-bot/` - two-phase: mechanics test then play-to-win (adapted from MIT-licensed LeeYiyuan/tetrisai and mikhail-vlasenko/Tetris-AI)
+5. `gameplay-bot/` - two-phase: mechanics test then play-to-win. Uses Pierre Dellacherie's 4-heuristic Tetris AI (2003) with Colin Fahey's GA-optimized weights. Reference implementations: LeeYiyuan/tetrisai and mikhail-vlasenko/Tetris-AI (both MIT)
6. `sonarqube-scan.py` - automated code quality scan (requires SonarQube at localhost:9000)
## Dashboard
diff --git a/dashboard/src/pages/methodology.astro b/dashboard/src/pages/methodology.astro
@@ -254,12 +254,14 @@ import Base from "../layouts/Base.astro";
<h3>AI play</h3>
</div>
<p>
- A 4-heuristic algorithm evaluates all possible placements for each piece:
+ Pierre Dellacherie's 4-heuristic algorithm (2003) evaluates all possible placements for each piece:
</p>
<pre><code>score = -0.51 * height + 0.76 * lines - 0.36 * holes - 0.18 * bumpiness</code></pre>
<p class="muted">
- Algorithm from <a href="https://github.com/LeeYiyuan/tetrisai" target="_blank" rel="noopener">LeeYiyuan/tetrisai</a> (MIT license).
- The goal is not to play well -- it is to exercise all game mechanics.
+ Weights from genetic algorithm optimization by Colin Fahey. Reference implementation:
+ <a href="https://github.com/LeeYiyuan/tetrisai" target="_blank" rel="noopener">LeeYiyuan/tetrisai</a> (MIT license).
+ The bot is a strong Tetris player -- the original algorithm can clear thousands of lines without losing.
+ We use it to exercise game mechanics and trigger events like multi-line clears for bug detection.
</p>
</div>
diff --git a/tasks/tetris/eval/gameplay-bot-v2/bot.ts b/tasks/tetris/eval/gameplay-bot-v2/bot.ts
@@ -17,7 +17,8 @@ import type {
} from "./types";
// ---------------------------------------------------------------------------
-// AI Player Logic (from LeeYiyuan/tetrisai, MIT License)
+// Pierre Dellacherie's 4-heuristic Tetris AI (2003) with Colin Fahey's
+// GA-optimized weights. Reference implementation: LeeYiyuan/tetrisai (MIT)
// ---------------------------------------------------------------------------
const W_HEIGHT = -0.510066;
diff --git a/tasks/tetris/eval/gameplay-bot/SPEC.md b/tasks/tetris/eval/gameplay-bot/SPEC.md
@@ -9,7 +9,7 @@ SVG, WebGL). The bot must work with all of them.
The bot does NOT use Claude or any LLM for grading. All evaluation is deterministic
code. The bot plays the game using a known-good AI algorithm (4-heuristic genetic
-optimization from LeeYiyuan/tetrisai, MIT License) and records what happens.
+optimization, reference implementation: LeeYiyuan/tetrisai, MIT License) and records what happens.
## Architecture
@@ -328,7 +328,7 @@ not game state). Validate aspect ratio (height ~= 2 * width).
## AI Player
-4-heuristic evaluation from LeeYiyuan/tetrisai (MIT License):
+Pierre Dellacherie's 4-heuristic evaluation (2003) with Colin Fahey's GA-optimized weights, reference implementation: LeeYiyuan/tetrisai (MIT License):
- Aggregate height: sum of column heights (weight: -0.510066)
- Lines cleared: number of complete rows (weight: 0.760666)
- Holes: empty cells below filled cells (weight: -0.35663)
diff --git a/tasks/tetris/eval/gameplay-bot/player.ts b/tasks/tetris/eval/gameplay-bot/player.ts
@@ -1,12 +1,13 @@
-// Heuristic evaluation adapted from LeeYiyuan/tetrisai (MIT License)
-// Weights are from genetic algorithm optimization in that project.
-// Piece definitions and simulation logic also adapted from that codebase.
+// Pierre Dellacherie's 4-heuristic Tetris AI (2003).
+// Weights from Colin Fahey's genetic algorithm optimization.
+// Reference implementation: LeeYiyuan/tetrisai (MIT License) -- piece
+// definitions and simulation logic adapted from that codebase.
import type { Page } from "@playwright/test";
import type { Grid, CalibrationResult, PieceType } from "./types";
import { readGrid, detectActivePieceCells, identifyPieceType, gridsAreDifferent } from "./grid-reader";
-// Genetically optimized weights from LeeYiyuan/tetrisai
+// Genetically-optimized weights (Fahey)
const W_HEIGHT = -0.510066;
const W_LINES = 0.760666;
const W_HOLES = -0.35663;
@@ -19,7 +20,7 @@ const GRID_COLS = 10;
* Standard Tetris piece definitions.
* Each piece has 4 rotation states.
* Each rotation state is a list of [row, col] offsets from the piece origin.
- * Adapted from LeeYiyuan/tetrisai piece.js
+ * Adapted from LeeYiyuan/tetrisai piece.js (reference implementation)
*/
const PIECES: Record<string, [number, number][][]> = {
I: [
@@ -390,7 +391,7 @@ export async function stackToGameOver(
}
// --- Heuristic evaluation functions ---
-// Adapted from LeeYiyuan/tetrisai (MIT License)
+// Pierre Dellacherie 4-heuristic, reference: LeeYiyuan/tetrisai (MIT License)
/**
* Find the best column and rotation for a given piece type using the
