I’m working on a territory capture game like Qix or Paper.io. The main challenge is teaching the AI to spot good areas to grab.
Here’s what I’m dealing with:
- Blue spots are already owned
- Black spaces are up for grabs
- Green and white dots show where the AI should move
I’m scratching my head trying to figure out how to make the AI find the best path to circle the most empty space. Maybe some kind of outline tracing could work?
I’ve got a bunch of sample map chunks to test with. They show different setups the AI might face. Anyone have ideas on how to tackle this? I’m open to any suggestions!
Having worked on similar projects, I’d suggest implementing a grid-based pathfinding algorithm like A* or Dijkstra’s. These can be adapted to prioritize paths that enclose the largest areas.
To evaluate potential moves, you could use a scoring system that considers:
- Area enclosed
- Distance to existing territory
- Risk of interception by opponents
You might also want to incorporate some randomness to prevent predictable behavior. Consider using Monte Carlo simulations to evaluate multiple potential paths and select the most promising one.
Remember to optimize your algorithm for performance, especially if you’re dealing with large grids. Caching intermediate results and using efficient data structures can make a big difference in real-time gameplay.
hey there, i’ve messed with similar stuff before. have u tried using a greedy algorithm? basically, u could make the AI always go for the biggest empty chunk it can find nearby. it’s not perfect, but it’s pretty fast and usually works okay.
just make sure to add some randomness so it doesn’t get stuck in loops. good luck with ur game!
As someone who’s dabbled in similar territory-based games, I can share an approach that worked well for me. Instead of trying to trace outlines, I found success using a flood-fill algorithm combined with a heuristic scoring system.
Here’s the gist: For each potential move, simulate a flood-fill from that point to see how much area could theoretically be captured. Then, assign scores based on factors like total area, proximity to existing territory, and risk of enemy interception.
This method lets the AI evaluate multiple paths quickly and choose the most promising one. It’s not perfect, but it gives a good balance of greed and safety.
One caveat: make sure to limit the depth of the flood-fill to keep processing manageable. You might need to tweak the scoring weights based on your specific game mechanics, but this approach should give you a solid starting point for smarter territory grabbing.