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November 27, 2025

Archived READ TIME: 2 min read

MISSION BRIEF

BlockBlast Slayer

A JavaScript-based solver for Block Blast–style puzzles.

STATUS

Archived

PUBLISHED

November 27, 2025

READ TIME

2 min read

TAGS

HTMLJavaScriptCSSPWA
SECTION INDEX tap to expand

Overview

A JavaScript-based solver for Block Blast–style puzzles.

The solver analyses the current 8×8 board and the three available pieces, then performs a depth-first search (DFS) to determine the best placement order that maximises score and board space.


Stack

Frontend: HTML, CSS
Solver: JavaScript


How It Works

1. Piece Parsing

Each piece is converted from a grid format into relative coordinates.
This normalises the shape and allows it to be placed at any valid position on the board.

2. Move Generation

For each piece, the solver scans the entire board to determine all valid placements.

A placement is valid if:

  • The piece stays within the board boundaries
  • None of its cells overlap existing blocks

The solver explores all possible placement orders of the available pieces using depth-first search (DFS).

For each move:

  • The piece is placed on a cloned board
  • Completed rows and columns are cleared
  • The resulting board is passed recursively to evaluate the next piece

4. Scoring

Moves are evaluated based on:

  • Lines cleared (high reward)
  • Remaining empty space (encourages open boards)

The solver selects the sequence of moves with the highest total score.

5. Final Output

The algorithm returns:

  • The optimal placement sequence
  • The predicted score of the resulting board state

Example Decision Factors

The solver prefers moves that:

  • Clear multiple lines
  • Maintain board openness
  • Avoid creating isolated blocked regions

Limitations

The solver performs a full search of possible placements for the given pieces.

While this works efficiently for an 8×8 board with three pieces, the approach scales poorly with larger boards or more pieces due to exponential growth in possible states.


Future Improvements

Potential improvements include:

  • Alpha-beta style pruning
  • Beam search
  • Monte Carlo Tree Search (MCTS)
  • Improved heuristic evaluation

Why Archived

The project has achieved its intended functionality and is considered complete.
Further development is not planned.

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