本文選題：知識識別 + BDI規(guī)劃　； 參考：《東華大學(xué)》2016年博士論文

【摘要】：智能規(guī)劃指的是某智能體從一特定問題的初始狀態(tài)出發(fā),尋找達(dá)到解決該問題的目標(biāo)狀態(tài)的動作序列。它又稱為自動規(guī)劃,是人工智能研究領(lǐng)域中的一個重要分支,同時也是涵蓋知識表示與推理、人機(jī)交互和認(rèn)知科學(xué)等多領(lǐng)域的交叉學(xué)科研究。本文主要圍繞智能規(guī)劃技術(shù)提出兩種不同規(guī)劃方法來解決規(guī)劃實(shí)際應(yīng)用問題。為了解決智能系統(tǒng)開發(fā)效率較低、成本高的問題,本文提出了一種基于知識識別的BDI(Belief,Desire,Intention)規(guī)劃方法,該方法能實(shí)現(xiàn)智能系統(tǒng)規(guī)劃知識復(fù)用,提高規(guī)劃效率;提出了基于Agent編程框架下的規(guī)劃識別模型,同時引入工作流網(wǎng)表示的行為模型發(fā)現(xiàn)、推導(dǎo)行為序列模式;提出了從工作流網(wǎng)轉(zhuǎn)化為BDI Agent能識別的規(guī)劃結(jié)構(gòu),即規(guī)劃體、上下文環(huán)境識別方法。本文提出的BDI規(guī)劃方法是智能規(guī)劃方法上一種新的嘗試,實(shí)驗(yàn)結(jié)果初步證明了該基于知識識別的BDI規(guī)劃方法的可行性,并具有一定的現(xiàn)實(shí)指導(dǎo)意義。目標(biāo)識別是一種特殊的規(guī)劃識別,它是對規(guī)劃識別的補(bǔ)充和完善。論文提出了一種基于Agent編程框架下的目標(biāo)識別方法。在規(guī)劃識別工作基礎(chǔ)上,根據(jù)是否存在目標(biāo)庫,對BDI目標(biāo)進(jìn)行識別。通過實(shí)驗(yàn),分析出對算法性能有重要影響的因素,即工作流網(wǎng)中的擴(kuò)展節(jié)點(diǎn)、選擇分支因素與平行分支因素,并給出實(shí)驗(yàn)結(jié)果�；谥R識別的BDI規(guī)劃,是一種推導(dǎo)識別面向BDI Agent編程范例的規(guī)劃,規(guī)劃的識別過程也是學(xué)習(xí)BDI Agent程序的過程。為了解決網(wǎng)絡(luò)系統(tǒng)自私節(jié)點(diǎn)合作收斂問題,本文提出了一種基于智能規(guī)劃的協(xié)作策略,它結(jié)合博弈論和規(guī)劃技術(shù)分析行為策略進(jìn)行決策,逐步實(shí)現(xiàn)合作收斂這一目標(biāo)狀態(tài)。本文實(shí)驗(yàn)分析不同靜態(tài)拓?fù)洵h(huán)境下,網(wǎng)絡(luò)節(jié)點(diǎn)能促使自私節(jié)點(diǎn)逐步實(shí)現(xiàn)合作,使網(wǎng)絡(luò)系統(tǒng)達(dá)到穩(wěn)定狀態(tài)。文中實(shí)驗(yàn)證明了一定條件下,提出的基于智能協(xié)作方法性能超出了其他存在的確定性更新策略規(guī)劃方法,包括IBN,IBS以及WSLS等。論文研究成果為推動智能規(guī)劃和規(guī)劃識別的研究提供了新的解決問題的方法和思路�？傊�,智能規(guī)劃是一種問題求解技術(shù),基于知識識別的BDI規(guī)劃方法研究(包括規(guī)劃識別和目標(biāo)識別),能夠?qū)崿F(xiàn)BDI規(guī)劃知識復(fù)用,提高智能系統(tǒng)的開發(fā)效率;基于智能規(guī)劃的協(xié)作策略,能夠指導(dǎo)、調(diào)整無線傳感網(wǎng)絡(luò)節(jié)點(diǎn)的行為策略,實(shí)現(xiàn)網(wǎng)絡(luò)中自私節(jié)點(diǎn)合作收斂這一目標(biāo)狀態(tài),有助于提高網(wǎng)絡(luò)的性能和效率。
[Abstract]:Intelligent planning refers to the action sequence of an agent from the initial state of a specific problem to find the target state to solve the problem. It is also called automatic planning, which is an important branch in the field of artificial intelligence. It is also an interdisciplinary study covering knowledge representation and reasoning, human-computer interaction and cognitive science. In this paper, two different planning methods are proposed to solve the practical planning problems. In order to solve the problem of low efficiency and high cost of intelligent system development, this paper presents a method of BDI Bel Beliefa design planning based on knowledge recognition, which can realize the reuse of intelligent system planning knowledge and improve the planning efficiency. In this paper, a programming identification model based on Agent programming framework is proposed. At the same time, the behavior model of workflow net representation is found, the behavior sequence pattern is deduced, and the planning structure, which can be recognized by BDI Agent, is proposed. Context environment recognition method. The BDI programming method proposed in this paper is a new attempt in intelligent planning method. The experimental results show that the BDI planning method based on knowledge recognition is feasible and has a certain practical significance. Target recognition is a special kind of planning recognition, which is complementary to and perfect for planning recognition. In this paper, a method of target recognition based on Agent programming framework is proposed. On the basis of planning and recognition, the BDI target is recognized according to the existence of target database. Through experiments, the factors that have important influence on the performance of the algorithm are analyzed, that is, the extended nodes in the workflow network, the branch factors and the parallel branching factors are selected, and the experimental results are given. The BDI programming based on knowledge recognition is a kind of programming that deduces and recognizes BDI Agent programming paradigm, and the process of planning identification is also a process of learning BDI Agent program. In order to solve the problem of cooperative convergence of selfish nodes in network systems, a cooperative strategy based on intelligent planning is proposed in this paper, which combines game theory and planning technology to analyze behavior strategies to make decisions, and realize the goal state of cooperative convergence step by step. In this paper, it is experimentally analyzed that network nodes can promote the selfish nodes to cooperate step by step and make the network system stable under different static topology environment. Experiments show that under certain conditions, the proposed intelligent collaboration method outperforms other existing deterministic updating policy planning methods, including IBS and WSLS. The research results of this paper provide a new method and train of thought for the research of intelligent planning and planning identification. In a word, intelligent planning is a kind of problem solving technology. The research of BDI planning method based on knowledge recognition (including planning identification and target recognition) can realize the reuse of BDI planning knowledge and improve the development efficiency of intelligent system. The cooperative strategy based on intelligent planning can direct and adjust the behavior strategy of wireless sensor network nodes and realize the target state of selfish node cooperation convergence in the network which is helpful to improve the performance and efficiency of the network.
【學(xué)位授予單位】：東華大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP18

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本文編號：1990276