Genetic Programming and Coevolution to Play the Bomberman™ Video Game

We present a GUI-driven and efficient Genetic Programming (GP) and AI Planning framework designed for agent-based learning research. Our framework, ABL-Unity3D, is built in Unity3D, a game development environment. ABL-Unity3D addresses challenges entailed in co-opting Unity3D: making the simulator serve agent learning rather than humans playing a game, lowering fitness evaluation time to make learning computationally feasible, and interfacing GP with an AI Planner to support hybrid algorithms. We achieve this by developing a Graphical User Interface (GUI) using the Unity3D editor’s programmable interface and performance optimizations. These optimizations result in at least a 3x speedup. In addition, we describe ABL-Unity3D by explaining how to use it for an example experiment using GP and AI Planning. We benchmark ABL-Unity3D by measuring the performance and speed of the AI Planner alone, GP alone, and the AI Planner with GP. 

GUI-Based, Efficient Genetic Programming and AI Planning for Unity3D

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GUI-Based, Efficient Genetic Programming and AI Planning for Unity3D