I’m working on a board game in Unity that has GO-inspired mechanics. Players take turns claiming grid cells by changing their colors to red or blue. When cells get completely surrounded by one color, they get removed and points are awarded to that player. There’s also a special burst mechanic where double-tapping creates protective residue.
I want to add an AI opponent that can make smart decisions. From what I’ve read, minimax seems like a good choice for turn-based games like this. However I’m not sure how to implement it properly in Unity.
Has anyone built something similar? What would be the best way to evaluate board positions and generate moves for this type of game? Any alternatives to minimax that might work better?
unity’s built-in ai tools are decent. start simple by counting controlled territory and piece advantage before diving deep into minimax. that burst mechanic you got is gonna be tricky to evaluate tho. maybe consider giving a bonus when it protects key spots?
Minimax works well here, but watch out for complexity. GO-style games have huge branching factors - too many possible moves can kill your performance.
I built something similar for a territorial game. Keeping search depth at 4-6 levels gave solid results without lag. For evaluation, focus on territorial stuff: surrounded opponent pieces, potential enclosures, and keeping your own pieces safe.
The burst mechanic’s pretty cool - weight positions where you can drop protective residue around key territories. Try iterative deepening so you can dial up/down computation time based on difficulty.
You’ll definitely need alpha-beta pruning. Also do move ordering - check captures and threats first since they trigger more pruning. Monte Carlo Tree Search is another option, but minimax with decent evaluation should handle your scope just fine.
Had this exact problem building a territory control game a few years ago. Skip Unity’s AI tools - roll your own minimax. You need that control.
Here’s what I learned the hard way: evaluation function beats search depth every time. For GO-style mechanics, weight these:
Cells you can capture next turn (top priority)
Potential enclosures - count “almost surrounded” groups
Heavily penalize isolated pieces
Bonus for pieces supporting multiple captures
Treat burst as a separate move type in generation. After a burst, add value based on how many pieces it protects from future captures.
Performance saver: precompute neighbor relationships and “capture zones” at board init. Just lookup during minimax instead of calculating adjacency constantly.
Cache your evaluations too. Same positions repeat in the search tree - recalculating is wasteful.
Start at depth 3, tune from there. Add time limits later if the AI’s too slow on complex positions.