Crafting a Pogo Stick in Minecraft with Heuristic Search (Extended Abstract)

Abstract

Minecraft is a widely popular video game renowned for its intricate environment. The game's open-ended design allows the creation of unique tasks and challenges for the agents, providing a broad spectrum for researchers to experiment with different AI techniques and applications. Indeed, various Minecraft tasks have been posed as an AI challenge. Most AI research on Minecraft focused on either applying Reinforcement Learning (RL) to solve the problem, learning an action model for planning, or modeling the problem for a domain-independent planner. In this work, we focus on the combinatorial search aspect of solving the Craft Wooden Pogo task within the Polycraft World AI Lab (PAL) Minecraft environment. PAL is an interface to Minecraft that provides an API for AI agents to interact with Minecraft's environment and send commands to the main character. PAL supports symbolic observations of the current state, making it ideal for planning algorithms, which require a symbolic model of the environment for problem-solving. Other Minecraft research frameworks such as MineRL, provide a visual, pixel-based representation of the game.