Hierarchical Pathfinding and AI-Based Learning Approach in Strategy Game Design

Hierarchical Pathfinding and AI-Based Learning Approach in Strategy Game Design

Strategy game and simulation application are an exciting area with many opportunities for study and research. Currently most of the existing games and simulations apply hard coded rules so the intelligence of the computer generated forces is limited. After some time, player gets used to the simulati...

Full description

Bibliographic Details
Main Authors: Le Minh Duc, Amandeep Singh Sidhu, Narendra S. Chaudhari
Format: Article
Language:English
Published: Hindawi Limited 2008-01-01
Series:International Journal of Computer Games Technology
Online Access:http://dx.doi.org/10.1155/2008/873913
id doaj-8377a7cab15b40f79c78363d747d2bba
record_format Article
spelling doaj-8377a7cab15b40f79c78363d747d2bba2020-11-24T21:41:06ZengHindawi LimitedInternational Journal of Computer Games Technology1687-70471687-70552008-01-01200810.1155/2008/873913873913Hierarchical Pathfinding and AI-Based Learning Approach in Strategy Game DesignLe Minh Duc0Amandeep Singh Sidhu1Narendra S. Chaudhari2School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, SingaporeSchool of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, SingaporeSchool of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, SingaporeStrategy game and simulation application are an exciting area with many opportunities for study and research. Currently most of the existing games and simulations apply hard coded rules so the intelligence of the computer generated forces is limited. After some time, player gets used to the simulation making it less attractive and challenging. It is also costly and tedious to incorporate new rules for an existing game. The main motivation behind this research project is to improve the quality of artificial intelligence- (AI-) based on various techniques such as qualitative spatial reasoning (Forbus et al., 2002), near-optimal hierarchical pathfinding (HPA*) (Botea et al., 2004), and reinforcement learning (RL) (Sutton and Barto, 1998).http://dx.doi.org/10.1155/2008/873913
collection DOAJ
language English
format Article
sources DOAJ
author Le Minh Duc
Amandeep Singh Sidhu
Narendra S. Chaudhari
spellingShingle Le Minh Duc
Amandeep Singh Sidhu
Narendra S. Chaudhari
Hierarchical Pathfinding and AI-Based Learning Approach in Strategy Game Design
International Journal of Computer Games Technology
author_facet Le Minh Duc
Amandeep Singh Sidhu
Narendra S. Chaudhari
author_sort Le Minh Duc
title Hierarchical Pathfinding and AI-Based Learning Approach in Strategy Game Design
title_short Hierarchical Pathfinding and AI-Based Learning Approach in Strategy Game Design
title_full Hierarchical Pathfinding and AI-Based Learning Approach in Strategy Game Design
title_fullStr Hierarchical Pathfinding and AI-Based Learning Approach in Strategy Game Design
title_full_unstemmed Hierarchical Pathfinding and AI-Based Learning Approach in Strategy Game Design
title_sort hierarchical pathfinding and ai-based learning approach in strategy game design
publisher Hindawi Limited
series International Journal of Computer Games Technology
issn 1687-7047
1687-7055
publishDate 2008-01-01
description Strategy game and simulation application are an exciting area with many opportunities for study and research. Currently most of the existing games and simulations apply hard coded rules so the intelligence of the computer generated forces is limited. After some time, player gets used to the simulation making it less attractive and challenging. It is also costly and tedious to incorporate new rules for an existing game. The main motivation behind this research project is to improve the quality of artificial intelligence- (AI-) based on various techniques such as qualitative spatial reasoning (Forbus et al., 2002), near-optimal hierarchical pathfinding (HPA*) (Botea et al., 2004), and reinforcement learning (RL) (Sutton and Barto, 1998).
url http://dx.doi.org/10.1155/2008/873913
work_keys_str_mv AT leminhduc hierarchicalpathfindingandaibasedlearningapproachinstrategygamedesign
AT amandeepsinghsidhu hierarchicalpathfindingandaibasedlearningapproachinstrategygamedesign
AT narendraschaudhari hierarchicalpathfindingandaibasedlearningapproachinstrategygamedesign
_version_ 1725923172196286464

Similar Items