基于ID3算法的智能代理決策系統(tǒng)設(shè)計(jì)與研究
發(fā)布時(shí)間：2019-05-27 18:06
【摘要】：游戲系統(tǒng)中智能代理?yè)?dān)負(fù)著與玩家直接進(jìn)行情感交互的重任,它的決策系統(tǒng)作為游戲可玩性的技術(shù)核心體現(xiàn)著“人工智能”的技術(shù)成果,直接關(guān)系著游戲設(shè)計(jì)的成敗。但是當(dāng)前主流的被市場(chǎng)廣泛認(rèn)可的智能代理決策系統(tǒng)都是基于預(yù)制邏輯進(jìn)行搭建的,它們自身的動(dòng)態(tài)調(diào)整能力有限,比較容易被玩家找出破綻從而降低決策智能,進(jìn)而影響用戶體驗(yàn)。而采用傳統(tǒng)人工智能技術(shù)的智能代理決策系統(tǒng),雖然能提供自主的決策智能,但是由于比較占用系統(tǒng)計(jì)算能力并且使用限制較多等原因,并不能被市場(chǎng)廣泛認(rèn)可。因此一種能繼承傳統(tǒng)決策系統(tǒng)的傳統(tǒng)優(yōu)勢(shì),同時(shí)又能結(jié)合新型人工智能技術(shù),根據(jù)游戲運(yùn)行情況進(jìn)行實(shí)時(shí)、自主地動(dòng)態(tài)調(diào)整的決策系統(tǒng)的研發(fā)變得尤為重要。本文對(duì)當(dāng)前主流和新型的智能代理決策系統(tǒng):模糊邏輯、人工神經(jīng)網(wǎng)絡(luò)、遺傳算法、游戲腳本、有限狀態(tài)機(jī)、行為樹進(jìn)行了介紹和分析。針對(duì)傳統(tǒng)智能代理決策系統(tǒng)存在的不能很好的在實(shí)際使用環(huán)境中保持決策合理性,無(wú)法有效適配玩家多樣化操作的問(wèn)題,本文提出了一種基于機(jī)器學(xué)習(xí)ID3算法與行為樹的ID3行為樹作為智能代理的決策系統(tǒng)。它通過(guò)收集玩家與NPC交互時(shí)產(chǎn)生的后臺(tái)數(shù)據(jù)借由ID3算法的分析指導(dǎo)行為樹進(jìn)行決策,實(shí)時(shí)調(diào)節(jié)決策系統(tǒng),保持決策的合理性。為驗(yàn)證新決策系統(tǒng)的有效性,基于Unity3d平臺(tái)設(shè)計(jì)了一款采用此方案的NPC智能代理,最后搭建了游戲系統(tǒng)并對(duì)其進(jìn)行了實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果驗(yàn)證了此方案的有效性。
[Abstract]:The intelligent agent in the game system undertakes the important task of directly interacting with the players. As the technical core of the playability of the game, its decision-making system embodies the technical achievements of "artificial intelligence", which is directly related to the success or failure of the game design. However, the current mainstream intelligent agent decision-making systems, which are widely recognized by the market, are built based on prefabricated logic, and their own dynamic adjustment ability is limited, so it is easier for players to find out the flaws and reduce the decision intelligence. And then affect the user experience. Although the intelligent agent decision system based on traditional artificial intelligence technology can provide independent decision intelligence, it can not be widely recognized by the market because it occupies the computing power of the system and has more restrictions. Therefore, it is particularly important to develop a decision system which can inherit the traditional advantages of the traditional decision system and combine the new artificial intelligence technology to carry out real time and independently dynamic adjustment according to the operation of the game. In this paper, the mainstream and new intelligent agent decision systems, such as fuzzy logic, artificial neural network, genetic algorithm, game script, finite state machine and behavior tree, are introduced and analyzed. In view of the problem that the traditional intelligent agent decision system can not maintain the rationality of decision in the actual use environment and can not effectively adapt to the diversified operation of players, In this paper, a ID3 behavior tree based on machine learning ID3 algorithm and behavior tree is proposed as an intelligent agent decision system. By collecting the background data generated by the interaction between players and NPC, it guides the behavior tree to make decisions through the analysis of ID3 algorithm, adjusts the decision system in real time, and maintains the rationality of the decision. In order to verify the effectiveness of the new decision system, a NPC intelligent agent based on Unity3d platform is designed. Finally, the game system is built and its experiments are carried out. The experimental results verify the effectiveness of the scheme.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18
本文編號(hào)：2486367
[Abstract]:The intelligent agent in the game system undertakes the important task of directly interacting with the players. As the technical core of the playability of the game, its decision-making system embodies the technical achievements of "artificial intelligence", which is directly related to the success or failure of the game design. However, the current mainstream intelligent agent decision-making systems, which are widely recognized by the market, are built based on prefabricated logic, and their own dynamic adjustment ability is limited, so it is easier for players to find out the flaws and reduce the decision intelligence. And then affect the user experience. Although the intelligent agent decision system based on traditional artificial intelligence technology can provide independent decision intelligence, it can not be widely recognized by the market because it occupies the computing power of the system and has more restrictions. Therefore, it is particularly important to develop a decision system which can inherit the traditional advantages of the traditional decision system and combine the new artificial intelligence technology to carry out real time and independently dynamic adjustment according to the operation of the game. In this paper, the mainstream and new intelligent agent decision systems, such as fuzzy logic, artificial neural network, genetic algorithm, game script, finite state machine and behavior tree, are introduced and analyzed. In view of the problem that the traditional intelligent agent decision system can not maintain the rationality of decision in the actual use environment and can not effectively adapt to the diversified operation of players, In this paper, a ID3 behavior tree based on machine learning ID3 algorithm and behavior tree is proposed as an intelligent agent decision system. By collecting the background data generated by the interaction between players and NPC, it guides the behavior tree to make decisions through the analysis of ID3 algorithm, adjusts the decision system in real time, and maintains the rationality of the decision. In order to verify the effectiveness of the new decision system, a NPC intelligent agent based on Unity3d platform is designed. Finally, the game system is built and its experiments are carried out. The experimental results verify the effectiveness of the scheme.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18
【參考文獻(xiàn)】
相關(guān)期刊論文 前5條
1 劉曉偉;高春鳴;;結(jié)合行為樹與Q-learning優(yōu)化UT2004中agent行為決策[J];計(jì)算機(jī)工程與應(yīng)用;2016年03期
2 黃宇達(dá);王迤冉;;基于樸素貝葉斯與ID3算法的決策樹分類[J];計(jì)算機(jī)工程;2012年14期
3 王永梅;胡學(xué)鋼;;決策樹中ID3算法的研究[J];安徽大學(xué)學(xué)報(bào)(自然科學(xué)版);2011年03期
4 但小容;陳軒恕;劉飛;柳德偉;;數(shù)據(jù)挖掘中決策樹分類算法的研究與改進(jìn)[J];軟件導(dǎo)刊;2009年02期
5 李建珍;基于遺傳算法的人工神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)算法[J];西北師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2002年02期
相關(guān)碩士學(xué)位論文 前2條
1 王振宇;計(jì)算機(jī)游戲中智能角色行為的研究與實(shí)現(xiàn)[D];湖南師范大學(xué);2010年
2 王黎明;決策樹學(xué)習(xí)及其剪枝算法研究[D];武漢理工大學(xué);2007年
，本文編號(hào)：2486367
本文鏈接：http://sikaile.net/kejilunwen/zidonghuakongzhilunwen/2486367.html
最近更新
教材專著
