I’m working on a game that’s kind of like those territory capture games. You know, where you go around and claim areas? I’m having trouble figuring out how to make the AI smart about picking good spots to grab.
Here’s what I’m dealing with:
- Blue stuff is already owned
- Black parts are up for grabs
- Green and white dots are where the AI should move
I’m trying to work out the best way for the AI to spot valuable areas to capture. It’s like I need to find the quickest path that grabs the most empty space.
I’ve been wondering if maybe some kind of outline tracing technique might work? But I’m not sure.
Has anyone tackled something like this before? Any ideas on how to approach it?
def search_optimal_path(matrix):
# Example placeholder code for finding the optimal path
optimal_route = []
for i in range(len(matrix)):
for j in range(len(matrix[0])):
if matrix[i][j] == 'vacant':
# Evaluate neighboring cells
continue
return optimal_route
I’d really appreciate any tips or insights. Thanks!
hey dancingbird, have u tried using a voronoi diagram? it’s pretty cool for this kinda stuff. basically, u divide the space based on closest points. could help spot valuable areas fast. combine it with some basic pathfinding n u might get decent results. just a thought!
I’ve actually tackled a similar problem in a project I worked on a while back. One approach that worked well for me was using a flood fill algorithm combined with a heuristic scoring system.
The idea is to start from each potential move point and ‘flood’ outwards, keeping track of the area covered and any strategic points encountered (like chokepoints or large open areas). You can assign different scores to different types of spaces - maybe empty spaces are worth 1 point, spaces that cut off opponent paths are worth 5, etc.
For the path finding part, I found A* to be pretty effective. You can use the heuristic scores as part of the cost function to guide the AI towards high-value areas.
One thing to watch out for is performance - these algorithms can get computationally expensive on large grids. You might need to limit the search depth or use some optimizations if you’re dealing with a big game world.
It took some tweaking, but once I got it dialed in, the AI became pretty good at identifying and capturing valuable territories. Hope this gives you some ideas to work with!
Having worked on similar territory-based games, I can offer some insights. A flood fill algorithm is indeed a solid approach, but you might want to consider combining it with a distance transform. This method can help identify the largest enclosed areas efficiently.
Start by applying the distance transform to your grid, which will assign each empty cell a value based on its distance from the nearest occupied cell. The cells with the highest values will be at the centers of large open areas.
Then, use these values to guide your AI’s decision-making process. Prioritize moves that capture cells with high distance transform values, as these are likely to be strategically valuable.
For pathfinding, consider using Jump Point Search (JPS) instead of A*. It’s particularly efficient for uniform cost grids and can significantly speed up your AI’s decision-making process.
Remember to balance exploration and exploitation in your AI strategy. Sometimes capturing a smaller area quickly can be more beneficial than aiming for a larger, more contested region.