本文選題：計算機博弈 + 五子棋�。� 參考：《安徽大學(xué)》2017年碩士論文

【摘要】：計算機博弈是人工智能領(lǐng)域最具挑戰(zhàn)的研究分支之一。它是研究人腦思維的載體,是計算機技術(shù)與博弈論相結(jié)合的產(chǎn)物,是人工智能領(lǐng)域的"試驗田",被譽為人工智能的"果蠅"。因此,有關(guān)計算機博弈的理論與實踐研究,將可以促進人工智能的發(fā)展。在計算機博弈中,棋類博弈是其研究熱點之一,因為人們相信存在于棋類博弈中的智能信息或許可以應(yīng)用到人類智能活動中。五子棋博弈是棋類博弈中至關(guān)重要的組成部分,其普及程度僅次于國際象棋。它具有聚集博弈典型意義、容易深入研究、博弈結(jié)果直觀反應(yīng)機器智能程度等優(yōu)點。因此可以把五子棋博弈作為計算機博弈的典型代表之一,對其進行深入研究,從而促使計算機博弈理論和實踐研究的發(fā)展,進而推動人工智能事業(yè)不斷地前進。本文以五子棋為載體對計算機博弈相關(guān)理論與技術(shù)進行了分析與研究。針對傳統(tǒng)Alpha-Beta剪枝算法搜索效率較低以及博弈水平不高的問題,提出了一種基于連續(xù)沖四搜索的Alpha-Beta剪枝算法以及基于搜索限定的Alpha-Beta剪枝算法;針對傳統(tǒng)基于棋型估值函數(shù)的參數(shù)主要由經(jīng)驗獲得并通過手工進行調(diào)整,存在人為不確定性的問題,提出了一種新的自適應(yīng)慣性權(quán)重混沌粒子群算法(A New Chaos Particle Swarm Optimization Based Adaptive Inertia Weight,CPSO-NAIW),并把它首次應(yīng)用到五子棋估值函數(shù)參數(shù)優(yōu)化問題中。實驗結(jié)果表明,本文提出的改進Alpha-Beta剪枝算法有效地提高了搜索效率和博弈水平;采用本文提出的CPSO-NAIW算法優(yōu)化后參數(shù)的五子棋博弈系統(tǒng)的博弈水平得到了很大提升。本文首先介紹了計算機博弈相關(guān)概念與技術(shù),然后分析了五子棋博弈組成要素并利用事件對策論對其進行數(shù)學(xué)建模,研究了五子棋博弈中的搜索算法以及估值函數(shù),最后對系統(tǒng)進行了設(shè)計與實現(xiàn)。本文核心技術(shù)與創(chuàng)新點如下:(1)提出了一種基于連續(xù)沖四搜索的Alpha-Beta剪枝算法。根據(jù)五子棋博弈的特點,在Alpha-Beta剪枝算法中引入連續(xù)沖四搜索這種強有力的進攻手段,并采用搜索范圍限定以及對連續(xù)沖四成功進行保存,當(dāng)下次遇到相同局面時,優(yōu)先對存儲的連續(xù)沖四著法進行搜索的連續(xù)沖四啟發(fā)方法,以減少無用和重復(fù)搜索。該算法提高了搜索效率和博弈水平。(2)提出了一種基于搜索限定的Alpha-Beta剪枝算法。根據(jù)五子棋落子比較集中和脫離戰(zhàn)場思想,對棋盤搜索區(qū)域進行劃分,并根據(jù)不同搜索區(qū)域落子對局面的影響程度采用不同的搜索深度,以減少無用搜索。該算法在不影響博弈水平的情況下,提高了搜索效率。(3)提出了一種新的自適應(yīng)慣性權(quán)重混沌粒子群算法(CPSO-NAIW)。該算法從慣性權(quán)重的調(diào)整以及如何擺脫局部極值兩個方面入手來改善粒子群算法(Particle Swarm Optimization,PSO)的性能。首先采用粒子相對于群體極值位置的距離對權(quán)重進行動態(tài)調(diào)整,把權(quán)重的變化與粒子的位置狀態(tài)信息關(guān)聯(lián)起來的方法,減少了算法陷入局部極值的概率,然后在算法陷入局部極值時,對群體極值位置進行混沌優(yōu)化,以使粒子搜索局部極值外的新鄰域和新路徑,增強了算法跳出局部極值的可能,最后把CPSO-NAIW算法首次應(yīng)用到五子棋估值函數(shù)的參數(shù)優(yōu)化問題中,以解決傳統(tǒng)估值參數(shù)僅通過手工調(diào)整,存在人為不確定的問題。采用該算法優(yōu)化后參數(shù)的五子棋博弈系統(tǒng)的博弈水平有顯著提升。本文以五子棋為載體對計算機博弈中至關(guān)重要的搜索算法以及估值函數(shù)進行了相關(guān)研究與改進。在搜索算法方面,提出了一種基于連續(xù)沖四搜索的Alpha-Beta剪枝算法以及基于搜索限定的Alpha-Beta剪枝算法。在估值函數(shù)方面,提出了一種CPSO-NAIW算法,并把它首次應(yīng)用到估值函數(shù)的參數(shù)優(yōu)化問題中。實驗結(jié)果表明,兩種改進的Alpha-Beta剪枝算法有效地提高了搜索效率和博弈水平,應(yīng)用CPSO-NAIW算法優(yōu)化后參數(shù)的五子棋博弈系統(tǒng)的博弈水平具有明顯優(yōu)勢。
[Abstract]:Computer game is one of the most challenging research branches in the field of artificial intelligence. It is the carrier of human brain thinking, the product of the combination of computer technology and game theory. It is the "experiment field" in the field of artificial intelligence and is known as the "fruit fly" of artificial intelligence. Therefore, the theoretical and practical research on the computer game will be able to promote artificial intelligence. The chess game is one of the hotspots in the computer game, because it is believed that the intelligence information that exists in the chess game may be applied to the human intelligence activity. The chess game is the most important part of the chess game, and its popularity is second only to the chess. It has a typical gathering game. Therefore, the game can be regarded as one of the typical representative of the computer game, so we can make a thorough study of the game, so as to promote the development of computer game theory and practice research, and then push the cause of artificial intelligence to advance continuously. This paper analyzes and studies the related theory and technology of computer game. Aiming at the problem of low search efficiency and low game level of traditional Alpha-Beta pruning algorithm, a Alpha-Beta pruning algorithm based on continuous punching four search and a Alpha-Beta pruning algorithm based on search limit are proposed. The parameters of the estimation function are mainly obtained by experience and adjusted by hand. A new adaptive inertia weight chaotic particle swarm optimization (A New Chaos Particle Swarm Optimization Based Adaptive Inertia Weight, CPSO-NAIW) is proposed, and it is used for the first time in the function parameter of the Gobang estimation function. The experimental results show that the improved Alpha-Beta pruning algorithm proposed in this paper can effectively improve the search efficiency and the game level. The game level of the five chess game system which is optimized after the optimization of the CPSO-NAIW algorithm proposed in this paper has been greatly improved. This paper first introduces the related concepts and techniques of computer game, Then it analyzes the elements of the chess game and uses the event game theory to model it, studies the search algorithm and the valuation function in the chess game, and finally designs and implements the system. The core technology and innovation of this paper are as follows: (1) a Alpha-Beta pruning algorithm based on continuous four search is proposed. According to the features of the chess game, the Alpha-Beta pruning algorithm is introduced in the Alpha-Beta pruning algorithm to search for this powerful attack method, and the search scope is limited and the continuous impulse four is saved successfully. When the next situation comes into the same situation, a continuous impulse four heuristic method for searching the stored continuous flushing method is given to reduce the continuous impulse. The algorithm improves the search efficiency and the game level. (2) a Alpha-Beta pruning algorithm based on the search limit is proposed. The search area is divided according to the focus of the checkerboard and the idea of disengagement from the battlefield, and different search is used to search the situation according to the different search area. Depth, in order to reduce the useless search. The algorithm improves the search efficiency without affecting the game level. (3) a new adaptive inertia weight chaotic particle swarm optimization (CPSO-NAIW) algorithm is proposed. The algorithm improves the particle swarm optimization (Particle Swarm Opt) from the adjustment of the inertia weight and how to get rid of the local extremum. The performance of imization, PSO). Firstly, the weight is dynamically adjusted by the distance between the particle relative to the group extremum position, and the method of correlation between the weight change and the position state information of the particle is used to reduce the probability of the algorithm falling into the local extremum. Then, when the algorithm falls into the local extremum, the chaotic optimization of the position of the population extremum is carried out. In order to make the particle search the new neighborhood and new path outside the local extremum, the possibility of the algorithm to jump out of the local extremum is enhanced. Finally, the CPSO-NAIW algorithm is first applied to the parameter optimization problem of the five chess estimation function, so as to solve the traditional estimation parameter only by manual adjustment, there is a human uncertainty problem. The algorithm is used to optimize the parameters of five. The game level of the sub chess game system has been significantly improved. In this paper, the search algorithm and the estimation function of the computer game are studied and improved by using the Gobang as the carrier. In the search algorithm, a Alpha-Beta pruning algorithm based on the continuous scour four search and the Alpha-Beta scissors based on the search limit are proposed. In the estimation function, a CPSO-NAIW algorithm is proposed and applied to the parameter optimization problem of the estimation function for the first time. The experimental results show that the two improved Alpha-Beta pruning algorithms can effectively improve the search efficiency and game level, and use the CPSO-NAIW algorithm to optimize the game water of the five chess game system after the parameter optimization. Leveling has obvious advantages.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

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