A Python Engine for Teaching Artificial Intelligence in Games
Project Overview
The document outlines the innovative use of the Game AI Game Engine (GAIGE), a Python-based platform developed to teach artificial intelligence (AI) concepts within the framework of computer game design. Through a structured series of seven programming assignments, students are guided to create a fully operational Multi-User Online Battle Arena (MOBA) game, which serves as a practical application of various AI techniques, including pathfinding and decision-making. By modularizing AI components, GAIGE simplifies the integration of complex algorithms into game development, enhancing students' understanding of both design and functionality. This hands-on approach not only fosters engagement with AI but also cultivates critical problem-solving skills as students navigate the challenges of game design. The findings suggest that such experiential learning methods significantly motivate students and improve their grasp of AI principles, ultimately demonstrating the effectiveness of generative AI tools in enhancing educational outcomes in technology and computer science.
Key Applications
Game AI Game Engine (GAIGE)
Context: Educational context involves students learning AI concepts through game development, specifically targeting computer science undergraduates.
Implementation: Students complete a series of seven programming assignments using GAIGE to build a MOBA game, implementing various AI algorithms.
Outcomes: Students gain practical experience with AI concepts, develop programming skills, and receive immediate feedback through autograders. The structure facilitates a sense of progression and accomplishment.
Challenges: Some assignments, like navigation mesh generation, present difficulties due to complexity and edge cases, which can lead to lower grades. Additionally, students may rely on instructor-provided solutions, which could affect their learning experience.
Implementation Barriers
Complexity Barrier
Assignments can be complex and require understanding of nuanced AI concepts, which may overwhelm some students.
Proposed Solutions: Creating simpler preliminary assignments (like Assignment 1.5) to familiarize students with necessary concepts before tackling more complex tasks.
Resource Barrier
Students may rely on instructor-provided solutions for complex assignments, which could hinder their independent problem-solving skills.
Proposed Solutions: Encouraging individual implementations and providing avenues for creative solutions alongside instructor support.
Project Team
Mark O. Riedl
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Mark O. Riedl
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai