Verifying adaptation of neuro-controlled game opponent by cross validation under supervised and unsupervised player modeling

As a new trend of game development, Artificial Intelligence (AI) used to be implemented by Finite State Machine (FSM) is being replaced by domain-knowledge-free automatic game design approaches. These training methods could achieve excellent performance as FSM and contribute to other improvements: N...

Full description

Saved in:
Bibliographic Details
Main Authors: Wan Huang, Suoju He, Xiao Liu, Jiajian Yang, Zhiyuan Shi, Xinyu Li, Ya'nan Hao
Format: Conference Proceeding
Language:English
Subjects:
adaptation of game AI
Artificial intelligence
Artificial neural networks
Automata
Automatic testing
Game theory
Helium
Logic
neuro-controller
pac-man
Software engineering
Software testing
strategy-based player modeling
supervised and unsupervised player modeling
Training data
UCT
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:As a new trend of game development, Artificial Intelligence (AI) used to be implemented by Finite State Machine (FSM) is being replaced by domain-knowledge-free automatic game design approaches. These training methods could achieve excellent performance as FSM and contribute to other improvements: Non Player Character's (NPC) behaviors appear wide differences based on player action, and for multiple opponents in a single game, they can learn to cooperate and form anti-strategy, which could greatly augment entertainment of game. In this paper, by using Pac-man of prey and predator game genre as test-bed and Upper Confidence bound for Trees (ÜCT) algorithm to generate training data for Artificial Neural Network (ANN), we demonstrate adaptation is confirmable with cross validation result under supervised and unsupervised player modeling. As a conclusion, we will clarify how theory developed in this paper could contribute to practical application in online video game and exert excellent game performance.