I recently built a tool to solve board layouts for the popular mobile puzzle game Block Blast. The app runs completely on the client side and helps players figure out the optimal placement for their blocks to maximize their score or clear lines.
The core engine relies on a backtracking search algorithm. In Block Blast, you get three pieces at a time and must place all three to receive the next batch. This means for any given turn, the solver needs to calculate the permutations of placing these three pieces onto an 8x8 grid.
To model this, the board is represented as a 2D array of boolean values. The pieces are defined by their relative coordinates. The algorithm checks every permutation of the three pieces ($3! = 6$ possible orders) and recursively attempts to place them in all valid coordinates on the grid.
Once a valid placement sequence is found, it calculates a heuristic score for the resulting board state. The heuristic evaluates:
- Number of cleared lines (full rows or columns)
- Number of empty cells remaining
- Grid fragmentation (isolated empty spaces that are hard to fill)
To make the user experience seamless, I implemented an HTML5 Canvas interface that allows users to either manually tap the grid to match their game board or upload a screenshot.
You can check out the live tool at Block Blast Solver to see the solver in action. I would love to hear your thoughts on optimization strategies for the heuristic search function!
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