Styleable NPC behavior – A Case Study of Pool Game

碩士 === 國立臺灣科技大學 === 資訊工程系 === 105 === When the game complexity is getting higher, the strategy game faces many challenges, and these challenges have already become issues in the field of AI research. Due to the large game state, action spaces, existing search algorithms have difficulty to make a str...

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Main Authors: Hsi-Ting Chou, 周熙庭
Other Authors: Wen-Kai Tai
Format: Others
Language:zh-TW
Published: 2017
Online Access:http://ndltd.ncl.edu.tw/handle/62761313073648314529
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spelling ndltd-TW-105NTUS53920892017-10-31T04:58:58Z http://ndltd.ncl.edu.tw/handle/62761313073648314529 Styleable NPC behavior – A Case Study of Pool Game 個性化NPC行為—以撞球遊戲為例 Hsi-Ting Chou 周熙庭 碩士 國立臺灣科技大學 資訊工程系 105 When the game complexity is getting higher, the strategy game faces many challenges, and these challenges have already become issues in the field of AI research. Due to the large game state, action spaces, existing search algorithms have difficulty to make a strong decision in such a large search space. It seriously affects the development of AI. In addition, there is an increasing demand for AI. Many game AIs are grasped their weakness and flaws, after the players repeatedly played on the game, resulting in the game experience decline. AI's diversity, variability, has become a bulk demand. This also means that the current AI design, can no longer use hard-code-scripting way to develop. This study presents a method of styleable NPC behavior. We extract the characteristics of the game state, as the status evaluation of the information. By using the game state evaluation system, rating possible solutions. We use rapid estimate NPC behavior system to predict the game state information. Based on the concept of Hierarchical Portfolio Search (HPS), the NPC behavior’s ability and playing style are classified into different Partial Player, and the NPC action is used to produce the appropriate and smaller action set. The action evaluation system is used to score the action set of NPC. Finally, according to NPC strategy model, from a number of candidate behavior selected in a best solution. Make the complex game more simple. We use this NPC architecture applied in the pool game AI design. By analyzing the pool state strategic components to get the state, strike unit evaluation parameters, makes pool AI have the ability to reposition itself. From the experimental results that, in the different parameters of the weight set, so that AI show a very different style and ability. Wen-Kai Tai 戴文凱 2017 學位論文 ; thesis 66 zh-TW
collection NDLTD
language zh-TW
format Others
sources NDLTD
description 碩士 === 國立臺灣科技大學 === 資訊工程系 === 105 === When the game complexity is getting higher, the strategy game faces many challenges, and these challenges have already become issues in the field of AI research. Due to the large game state, action spaces, existing search algorithms have difficulty to make a strong decision in such a large search space. It seriously affects the development of AI. In addition, there is an increasing demand for AI. Many game AIs are grasped their weakness and flaws, after the players repeatedly played on the game, resulting in the game experience decline. AI's diversity, variability, has become a bulk demand. This also means that the current AI design, can no longer use hard-code-scripting way to develop. This study presents a method of styleable NPC behavior. We extract the characteristics of the game state, as the status evaluation of the information. By using the game state evaluation system, rating possible solutions. We use rapid estimate NPC behavior system to predict the game state information. Based on the concept of Hierarchical Portfolio Search (HPS), the NPC behavior’s ability and playing style are classified into different Partial Player, and the NPC action is used to produce the appropriate and smaller action set. The action evaluation system is used to score the action set of NPC. Finally, according to NPC strategy model, from a number of candidate behavior selected in a best solution. Make the complex game more simple. We use this NPC architecture applied in the pool game AI design. By analyzing the pool state strategic components to get the state, strike unit evaluation parameters, makes pool AI have the ability to reposition itself. From the experimental results that, in the different parameters of the weight set, so that AI show a very different style and ability.
author2 Wen-Kai Tai
author_facet Wen-Kai Tai
Hsi-Ting Chou
周熙庭
author Hsi-Ting Chou
周熙庭
spellingShingle Hsi-Ting Chou
周熙庭
Styleable NPC behavior – A Case Study of Pool Game
author_sort Hsi-Ting Chou
title Styleable NPC behavior – A Case Study of Pool Game
title_short Styleable NPC behavior – A Case Study of Pool Game
title_full Styleable NPC behavior – A Case Study of Pool Game
title_fullStr Styleable NPC behavior – A Case Study of Pool Game
title_full_unstemmed Styleable NPC behavior – A Case Study of Pool Game
title_sort styleable npc behavior – a case study of pool game
publishDate 2017
url http://ndltd.ncl.edu.tw/handle/62761313073648314529
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