Introduction
Ever wanted to design your own maze? That’s the core idea of our latest board game. It’s all about placing hexagonal tiles to create paths that connect back to a central hub. This hub has six paths leading out, and every other tile needs to link back to it. Getting this to work smoothly was a challenge, so we turned to simulation to help us out.
Objectives
Our primary goal was to ensure all the hex tiles in the game would connect back to the central hub tile, forming a single, interconnected network. But we also needed to figure out which tiles to include in the game’s total pool. Since players will randomly select tiles from this pool to build the game board, it was crucial to ensure that any combination of tiles would create a fun, playable board. To achieve this, we ran thousands of simulations to see how different tile configurations would play out.
Development Process
Defining Tile Types and Patterns
The game includes various hexagonal tiles, each with unique path configurations. Some tiles have straight paths, others have curves, and a few have intersections that offer multiple directions. The way these tiles are arranged can completely change the game board, so we needed to understand how each type interacts with the others to ensure a balanced and engaging game.
Simulating Tile Placement
To test how these tiles would work together, we developed a script in Python to simulate their placement on a grid. The script places tiles one by one, checking to make sure they all connect back to the central hub. This allowed us to explore countless tile configurations without manually placing each tile, helping us identify which tiles should make it into the final pool.
Running Thousands of Simulations
We didn’t just run a few simulations—we ran thousands upon thousands. Each simulation produced different histograms and distributions, but a general pattern emerged: a bell curve. This pattern reinforced my intuition about the types of tiles to include in the pool. Seeing this consistent distribution across so many simulations gave me confidence that we were on the right track. Of course, there’s always room for improvement, and I could probably create a more complex simulation or mathematical model to better balance maintaining a valid board state and creating complex paths through the game. But I’m not an expert in that area, and in the interest of moving forward, I think we’ve reached a solid milestone.
Balancing Tile Frequencies
One key decision was how often each tile type should appear. To create a balanced game, we assigned different weights to each tile type, making some tiles more likely to appear than others. For example, tiles with 3 paths might show up more frequently than complex intersections. This balance was important, and the bell curve that emerged from our simulations helped confirm that we were likely on the right path.
Challenges We Faced
As with any project, we encountered a few challenges along the way. One major issue was ensuring the script saved the tile layouts correctly without losing any crucial data. This required some serious tweaking and debugging (and completely scrapping all the code at least once), especially when dealing with large sets of tiles and complex layouts. Another challenge was making sure our connectivity checks worked for all kinds of layouts, including those on the edges of the grid, where connections could get tricky. We had to fine-tune our algorithms to handle these edge cases, ensuring that every tile remained linked to the central hub no matter where it was placed or oriented. But with some adjustments and a lot of patience, we managed to get everything working smoothly, paving the way for a solid foundation in our game development.
What We Learned
The simulation process gave us invaluable insights into which tiles should be included in the game’s total pool. By running so many simulations, we were able to identify patterns that guided our decisions, helping us create a balanced and engaging game. The bell curve distribution that emerged confirmed that our tile selection was in the ballpark, giving us confidence in our approach. However, I’m fully aware that there’s always room for further refinement, especially as we move into the playtesting phase. We can continue to tweak things based on real-world feedback, ensuring that the final game not only works as intended but also offers a challenging and fun experience for players. This iterative process is key to delivering a game that’s as enjoyable as it is well-designed.
What’s Next?
Now that we’ve reached this milestone, we’re moving on to refining the tile designs and running more tests to make sure everything is just right. We’ll continue playtesting to see how players interact with the game with this set of available tiles. This process will help us fine-tune the game even further and could lead to new tile types or adjustments in the tile pool based on feedback.
Wrapping Up
Simulating tile placement was a crucial step in developing our game. Running thousands of simulations helped us determine a better selection of tiles to include in the game’s total pool and indicated that our approach works. While there’s always room for improvement, I’m confident we’ve reached a good stopping point for now. We’ll keep refining the game as we gather more feedback, but we’re excited to see how players will enjoy building the game board.
