I’m working on a territory capture game similar to Qix and Volfied where players draw lines to claim areas. I need help creating an algorithm for an automated player to identify the best spots to create enclosures.
In my grid system, I have owned territory (represented as filled areas), unclaimed space (empty regions), and potential path points that the AI should traverse to maximize territory gain.
The core challenge is: how can I programmatically determine the most efficient route that surrounds the largest possible empty area?
I’ve considered using boundary detection algorithms, but I’m not sure if that’s the right approach. The AI needs to evaluate concave regions in the existing territory and plan a path that creates the biggest enclosure with minimal movement.
Has anyone implemented similar pathfinding for area enclosure games? What algorithms work best for detecting these optimal capture opportunities?
flood fill is a solid choice! start at the borders, and check potential area gains as u expand. then A* can help with planning your path once u’ve identified the best spots. good luck!
Used Monte Carlo tree search for my Qix clone - probably overkill but it worked perfectly. Just simulate random enclosure attempts and track which starting positions perform best. Much easier than analytical solutions and handles weird edge cases without breaking.
Just hit this exact problem last year building an AI for our game jam. Treat it like graph traversal where you’re hunting for cycles that maximize enclosed area.
I used modified Dijkstra that checks partial enclosures at each step. Skip shortest paths - instead keep a priority queue of incomplete loops ranked by potential area gain divided by completion cost.
The real trick is preprocessing your grid for “capture seeds” - empty regions with natural barriers like walls or boundaries. Run connected components on empty spaces, then calculate how much perimeter you’d need to close each one off.
Don’t overthink the geometry. Simple polygon area calculation works once you’ve got candidate paths. Performance bottleneck is usually evaluating too many path combinations, not the area math.
One gotcha - my AI created technically optimal paths but left tiny unusable gaps. Added a minimum viable area threshold and that fixed most issues.
This sounds like a classic optimization problem - Voronoi diagrams are perfect for this. I tackled something similar a few years ago and learned that treating empty regions as capture zones beats pure pathfinding every time. Here’s what worked: First, find all the “vulnerable” empty areas that are already partially surrounded by your territory. Calculate the minimum perimeter needed to close each one, then rank them by area-to-perimeter ratio. For execution, use dynamic programming - sometimes multiple small captures beat one risky large one. Watch out for opponent interference though. I got burned by this - your algorithm needs to adapt when they try to block your route. Voronoi diagrams handle weird concave boundaries naturally and give you a solid math framework for comparing strategies.
I built something similar for a territory control bot. Skip boundary detection and go straight to a modified convex hull algorithm with cost-benefit analysis. Here’s what worked for me: find all the “anchor points” on your current territory edge, then calculate potential area gains for paths between different anchor combos. Use the shoelace formula to estimate enclosed area before making any moves. The trick is balancing path length vs area gained - sometimes a longer route grabs way more territory. Also add lookahead so you don’t create paths that block better captures later. Ray casting helps check if your potential enclosures would hit existing boundaries.