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AI For Games A*

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Development span: February 2024 (~2.5 weeks)

Project Type

  • Individual

My Roles

  • Programmer

Source Code

Made For

  • CAP4053 AI for Virtual Environments and Games (UF)
Example of the algorithm's final path on a medium-sized grid. Darker patches are terrain that is more 'difficult' traverse.
Example of the algorithm's final path on a large grid. Example of the algorithm's final path on a unique grid. Example of the algorithm's path on one of my unique test grids. First you draw a circle...

About the Project

Implementing the A* pathfinding algorithm was one of two major projects in UF's AI for Games course, a class centered around explaining the fundamentals of non-player agents in games and the various algorithms and systems that they're made of (decision trees, state machines, search algorithms, rational agents, vocabulary and terminology, etc.). I highly optimized my implementation, and prevented memory leaks.

As per the request of the professor to all students in this course, I have only uploaded the files which contain my own work for this specific project. I enjoyed and am proud of all my solution implementations for projects in the course, but making all of my solutions public may provide other students an avenue for cheating, so I've isolated my A* algorithm for display away from some of the supplemental files that make it functional.

If any AI for Games students have somehow found this page - I believe in you! It's a great course, and looking at my code will do yours a disservice.

Programming Accomplishments

  • Programmed an efficient version of the A* algorithm while effectively managing memory

Other Accomplishments

  • Other C++ projects for the class included implementations of the flocking algorithm, a Minimax player AI for the game Reversi, and programming an AI for Robocode to work in a team with other robots.

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© 2024 Andrew Kohler