My first post was about why I built a solver for Block Blast—a simple, greedy algorithm that showed me surprising lessons about space and strategy. It was a great start, but it had one big flaw: it was short-sighted.
That greedy solver only cared about the next move. Score a placement based on immediate cleared lines and maybe a crude "open space" count, pick the highest number, and move on. It worked, kind of. But it would often make a move that cleared 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 do 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:
javascript
// 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 unplaceable zones for large 3x3 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 lookahead (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:
Future Flexibility (Weight: High): Empty space is a resource, but not all space is equal. A clean 3x3 area is gold. I created a countPotentialPlacements() function that would check how many of the next possible block shapes could theoretically fit on the board. A move that preserved more options scored highly.
Combo Potential (Weight: Medium): Clearing one line is fine; setting up two or three for the next block is brilliant. I added a comboSetupScore() that analyzed if a move left the board with multiple lines that were one block away from completion.
Danger Detection (Weight: Critical): The single-cell hole is the classic killer. But a more subtle killer is the "tall column." I added a penalty for height variance across columns (calculateColumnHeightVariance()). A flat board is a flexible board. A spiky board is a death trap.
Accessibility (Weight: Low): This was a minor touch. I penalized moves that completely surrounded an empty cell, making it accessible only by a perfect, unlikely block shape.
The new scoring function became a weighted sum of these strategic factors:
`javascript
// 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.) was 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 in a different spot, clearing zero lines now.
The old logic, obsessed with immediate clears, always chose Option A. Option A, however, 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 open a beautiful 4x2 rectangle. calculateFlexibilityScore() gave it a huge boost. calculateBoardDanger() showed Option A was 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 new strategic solver consistently survived 30-40% longer than its greedy predecessor. Its average score shot up. More importantly, its moves started to look intentional, like it was working towards a goal, not just reacting.
This iterative process—from a simple bot to a thinking one—is what fascinates me. The solver is no longer just a tool for answers; it's a playground for testing strategy.
If you want to see these principles in action or dissect the board states yourself, you can experiment with the live version on my site: Block Blast Solver. Try a tricky board and see if you can 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.
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