From Greedy to Smart: Optimizing the Block Blast Solver's Scoring Engine

Source: Dev.to

I wrote about why I built a solver for Block Blast – a simple, greedy algorithm that taught me surprising lessons about space and strategy. It was a great start, but it had one big flaw: it was short‑sighted.

The greedy solver only cared about the next move. It scored a placement based on immediate cleared lines and a crude “open space” count, picked the highest‑scoring option, and moved on. It worked… kind of. Often it would clear two lines now, only to create an impossible board state two blocks later. It was winning the battle but losing the war.

So I stopped just solving and started thinking: what does a strategic move look like? How can I teach the algorithm to see the board not as static cells, but as a system of future possibilities?

The Limitations of a Simple Greedy Bot

Here was the core of my initial scoring function. It was naive, but it was a start:

// The Old, Greedy Way
function simpleScore(newBoard) {
    let score = 0;
    score += countClearedLines(newBoard) * 100;   // Clear lines = good!
    score -= countIsolatedCells(newBoard) * 20;   // Holes = bad!
    return score;
}

This logic failed in subtle ways. It loved clearing a line immediately, even if that meant stacking blocks high in one corner. It hated single‑cell holes, but was blind to creating future un‑placeable zones for large 3×3 blocks. It played not to lose the next move, but had no plan for the next ten.

Building a Strategic Heuristic

The goal wasn’t brute‑force look‑ahead (that’s for a future minimax experiment). The goal was a smarter scoring heuristic—a function that could, in one evaluation, estimate the long‑term health of a board.

I broke down “board health” into specific, quantifiable components:

The new scoring function became a weighted sum of these strategic factors:

// The New, Strategic Heuristic
function strategicBoardScore(newBoard, nextBlockShapes) {
    let score = 0;

    // Core Strategy Weights
    score += calculateFlexibilityScore(newBoard, nextBlockShapes) * 0.4; // 40 % weight on future options
    score += calculateComboPotential(newBoard) * 0.3;                    // 30 % on setting up big plays
    score -= calculateBoardDanger(newBoard) * 0.2;                     // -20 % for creating risk
    score -= calculateInaccessibleAreas(newBoard) * 0.1;                 // -10 % for trapping spaces

    // Bonus for immediate clears (but less important)
    score += countClearedLines(newBoard) * 50;

    return score;
}

Tuning these weights (0.4, 0.3, etc.) took an entire afternoon of trial‑and‑error, watching the solver play thousands of games. It felt less like coding and more like training a very simple AI.

The “Aha!” Moment in Code

The real test wasn’t the final score, but a specific board state. The old greedy bot faced a choice:

• Option A: Place an L‑shape to clear 1 line immediately.
• Option B: Place the same L‑shape elsewhere, clearing 0 lines now.

The old logic, obsessed with immediate clears, always chose Option A. However, Option A would create a tall stack on the right.

The new heuristic saw something else. Option B, while not clearing anything, kept the board perfectly flat and left a beautiful 4×2 rectangle open. calculateFlexibilityScore() gave it a huge boost, while calculateBoardDanger() flagged Option A as risky.

For the first time, the solver sacrificed an immediate reward for long‑term health. It made a patient, human‑like decision. That was the “aha!” moment—the algorithm had learned a core tenet of the game.

Results and the Live Playground

The difference was stark. The strategic solver consistently survived 30‑40 % longer than its greedy predecessor. Its average score shot up, and its moves started to look intentional, as if it were working toward a goal rather than merely reacting.

If you want to see these principles in action or dissect the board states yourself, try the live version on my site: Block Blast Solver. Pick a tricky board and guess the solver’s “reasoning” based on the heuristic above. It’s a fascinating way to reverse‑engineer your own intuition.

What’s Next? The Minimax Horizon

The strategic heuristic is a massive leap, but it’s still an approximation. The true frontier is implementing a minimax algorithm with pruning—allowing the solver to actually simulate 2, 3, or 4 moves ahead before deciding.

That’s the next chapter. It’s a heavier computational challenge, but after seeing the power of a good heuristic, I’m convinced it’s the key to unlocking truly optimal play. The journey from greedy to smart is just the beginning.

Tags: javascript #algorithms #gamedev #optimization #puzzle