AI based serious game design - Kleptomancy

AI based serious game design - Kleptomancy

In this paper, we explored the use of Artificial Intelligence to create an adversary that demonstrates reasonable intelligence through the extensive use of Machine Learning techniques, Deep Reinforcement Learning and Imitation Learning techniques. In particular, we used Proximal Policy Optimizati...

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Bibliographic Details
Main Author: Wee, Chang Han
Other Authors: Smitha Kavallur Pisharath Gopi
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Computer and Information Science
Engineering
Artificial intelligence
Reinforcement learning
Machine learning
Generative adversarial imitation learning
Self-play
Serious game
Online Access:https://hdl.handle.net/10356/175216
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Institution: Nanyang Technological University
Language: English
Description
Summary:In this paper, we explored the use of Artificial Intelligence to create an adversary that demonstrates reasonable intelligence through the extensive use of Machine Learning techniques, Deep Reinforcement Learning and Imitation Learning techniques. In particular, we used Proximal Policy Optimization (PPO) algorithm, a branch of Model-Free RL Policy Optimization model, as well as Generative Adversarial Imitation Learning (GAIL) to train our intelligent agent. This project aims to evaluate and demonstrate the Intelligent Agent’s adaptive responses and strategies when faced with player-generated challenges in an edutainment game that was developed as part of this project, ‘Kleptomancy’.

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