本文關鍵詞： 不確定性 任務-人員分配 馬爾科夫決策過程(MDP)模型 MATB　出處：《北京交通大學》2017年碩士論文　論文類型：學位論文

【摘要】：動態(tài)任務分配就是將合適的任務實時地分配給合適的成員,以充分利用系統(tǒng)的資源,提高任務的完成績效。在一個不確定性系統(tǒng)中,任務到達的時間是隨機的、不確定的,到達的任務類型是多樣的、變化的,外界環(huán)境的變化會增加系統(tǒng)的不確定性,同時人員受外界以及自身狀態(tài)的影響其處理任務的能力也是波動的。隨著人工智能的發(fā)展,動態(tài)任務分配理論成果越來越成熟,其應用領域也更加廣泛,但仍缺乏對不確定性人機系統(tǒng)中的任務-人員動態(tài)分配的研究�，F有的任務-人員動態(tài)分配研究中,多集中于對外界不確定性的研究,而忽略人員能力波動性也會導致人機系統(tǒng)的不確定性。借鑒多Agent系統(tǒng)在不確定環(huán)境下智能體決策理論,建立馬爾科夫決策過程(Markov Decision Processes,MDP)模型解決不確定性人機系統(tǒng)中任務-人員動態(tài)分配問題,豐富任務-人員動態(tài)分配理論,解決實際不確定性任務-人員動態(tài)分配問題、人員合作模式選擇問題,從而降低不確定事件對系統(tǒng)的不良影響,提高系統(tǒng)的運作效率。論文完成的主要工作如下:(1)針對人機系統(tǒng)外界環(huán)境不確定和作業(yè)者能力波動,建立了基于MDP的人員能力與任務不確定性分配模型,并對MDP模型中的每個參數給出具體定義和說明,論證了其解的存在性,使用改進的策略迭代算法對模型進行了求解。(2)以MATB(Multi-Attribute Task Battery,多屬性任務組)動態(tài)任務環(huán)境為應用背景,對MATB中任務-人員分配以及人員合作方式的選擇問題進行了深入地分析,通過構建合理的人因實驗設計,有效解決了 MDP模型求解所需的腦力負荷值、處理速度概率值、人員差異性等參數的獲取問題。(3)采用MATLAB仿真,對隨機任務-人員分配策略和基于MDP的分配策略進行了比較,驗證了基于MDP分配策略的有效性和優(yōu)越性,并對兩種不同人員合作模式的分配策略進行了對比分析,證明了能力均等的合作方式優(yōu)于能力差異的組合方式。
[Abstract]:Dynamic task allocation is to assign the right task to the right member in real time to make full use of the system's resources and improve the performance of the task. In an uncertain system, the time of task arrival is random and uncertain. The types of tasks that arrive are diverse and varied, and changes in the external environment increase the uncertainty of the system, and the ability of people to handle tasks under the influence of the outside world and their own states is also fluctuating. The theory of dynamic task assignment is becoming more and more mature, and its application field is more extensive. However, there is still a lack of research on dynamic assignment of task-personnel in uncertain man-machine system. Most of the researches focus on the uncertainty of the outside world, but ignoring the volatility of human ability will lead to the uncertainty of the man-machine system. This paper draws lessons from the theory of agent decision-making in the uncertain environment of multi-#en0# system. A Markov Decision process model is established to solve the problem of dynamic assignment of task-personnel in uncertain man-machine system, which enriches the theory of dynamic assignment of task-person, and solves the problem of dynamic assignment of uncertain task-person in practice. In order to reduce the adverse effects of uncertain events on the system and improve the operational efficiency of the system, the main work accomplished in this paper is as follows: 1) aiming at the uncertainty of the external environment of the man-machine system and the fluctuation of the operator's ability, In this paper, the model of personnel ability and task uncertainty assignment based on MDP is established, and each parameter in the MDP model is defined and explained in detail, and the existence of the solution is proved. Using the improved strategy iterative algorithm to solve the model. (2) taking the MATB(Multi-Attribute Task batch (multi-attribute task group) dynamic task environment as the application background, the problem of the task-person assignment and the choice of the cooperation mode in the MATB is deeply analyzed. By constructing a reasonable human factor experimental design, the problem of obtaining parameters such as mental load value, probability value of processing speed, personnel difference and so on for solving MDP model is effectively solved by MATLAB simulation. This paper compares the random task-personnel allocation strategy with the allocation strategy based on MDP, validates the effectiveness and superiority of the allocation strategy based on MDP, and makes a comparative analysis of the allocation strategies of two different personnel cooperation modes. It is proved that the cooperative mode of ability equality is superior to the combination mode of ability difference.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F224

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