learning agent 中文意思是什麼
learning agent
解釋
學習主體-
This paper deeply analyzes and describes the dynamic price mechanism in two aspects : first, through basic matching, the matchmaking agent decides the bargaining collection and the bargaining price. besides, the bargaining agents ( including the purchaser agent and bargainer agent ) dynamically decide the strategy of biding and progress through learning according to either historical experience or practical environment
論文中從兩個方面深入的分析和設計了動態價格機制：一是在撮合主體中通過基本匹配動態確定交易對集以及交易價格；其二是在交易主體（買賣方主體）中通過學習（根據歷史經驗學習、從現場環境中學習）動態確定出價要價策略（競標價格策略） 。 -
Based on the standard of lom ( learning object meta - data ), a model is constructed by using agent technology derived from the distributed artificial intelligence, which is called model of resource retrieval agent system based on the standard of lom ( lrras )
本文在ieee制定的學習對象元數據標準（ learningobjectmetadata ，簡稱lom ）基礎之上，結合分散式人工智慧的agent技術，提出了一種基於lom的資源檢索agent系統（ lrras ）模型。 -
The development of one - wire technique, fieldbus technique and gprs wireless communication technique propose a perfect way of realizing intelligent distribution and measurement. moreover, dai and agent theory provide powerful theoretical support to improve the system ' s ability of intelligent identification, intelligent learning and anti - noise
單總線技術、現場總線技術以及gprs無線通訊技術的發展為智能分佈和測控功能的實現提供了一套令人滿意的解決方案，而分散式人工智慧技術和agent理論為提高系統的智能判別、學習和抗干擾能力提供了有力的理論依據。 -
Firstly, we have put forward a mathematic form ? interest vector - to express the user ' s interest and have given a formula to calculate quantificationally the interest degree of interest item. secondly, we apply the method of reinforcement learning to intelligent learning agent to make it leam the user ' s interest more precision, more quickly and more efficiency, to make it discover the divert of the user ' s interest in time. thirdly, we have developed a mean of information search and filtrate on user ' s interest
這些研究工作主要體現在：提出用戶興趣的數學表示形式? ?興趣向量，並提出定量地計算用戶對興趣項的興趣度的計算方法；提出採用強化學習演算法，使智能學習agent能夠更準確，更快速和更高效地學習到用戶的興趣和及時發現用戶興趣的轉移；提出面向用戶興趣的信息搜索和信息過濾方法；提出一種基於對象的agent編程模式，也稱為用擴充oo建模技術或方法學的適用性來設計agent系統。 -
Fourthly, leaning agent adjusts the domain model and user model adaptively by reinforcement learning and genetic algorithm
第四，作者研究了學習agent使用強化學習、遺傳演算法自適應地調整領域模型和用戶模型。 -
Moreover, this thesis proposes two novel strategies : the weighted fuzzy control strategy for multi objects and the behavior control strategy based on emotion evaluation and associative learning, and these two strategies are applied on an invert - pendulum system and a simulative agent system, respectively
最後，本文分別提出了基於對多性能指標分別加權的專家模糊控制策略，以及基於人工情感評估機制與聯想學習的行為控制策略，並分別將兩種策略應用於非精確倒立擺系統和模擬agent系統。 -
C ) learning ability is the base of agent self - determination behavior
Agent的學習能力是體現智能性的基礎。 -
With an eye to the large space of the state and action, the notions of “ generalization of state ” and “ generalization of action ” are defined, which are used to cut short the state space. since every agent can get similar experiences while learing, a experiences sharing method is proposed to improve the efficiency of learning in this method. however, by using the tsgs - based framework, macrl - japs is proposed. on the purpose of solving the multi - equilibria problem in games, the notion of japs is brought in. this method can ensure the agent to exactly predict the actions of others, and then to achieve the same optimal equilibrium selection
Macrl - cc在對系統目標的特性進行分析的基礎上，將系統目標進行分解，並採用基於承諾和約定的協作方法實現agent的協作求解;考慮到狀態行為空間可能很大的問題，提出了狀態和行為的泛化的概念，對狀態空間進行了縮減;針對agent在求解過程中學習到的經驗知識的相似性，提出了經驗知識共享的方法以提高學習效率。 -
On the base of analyzing the characteristics of erp system and erp learning, taking apart the leaning contents and learning styles of erp, and integrating agent technology, we present an e - learning model which is used to learn erp, viz
在分析erp軟體復雜性和學習特點的基礎上，對erp的學習內容和學習方式進行了剖析，並結合agent技術，提出了一個erp學習的e - learning模型? ? erp學習模型。 -
Theory and technology of agent originally come from distributed artificial intelligence ( dai ), with the development of the research of dai, some new research fields appear such as coordination theory, distributed reasoning, the learning and communication language between the agents
Agent理論與技術研究最早源於分散式人工智慧（ dai ） ，隨著傳統的人工智慧研究的深入，出現了協商理論、分佈推理、 agent之間的學習和通訊語言等新興的研究領域。 -
After introducing some basic concepts of agent 、 mas and multi - agents learning, the thesis analyses the research actuality and the future developmental directions of rl and multi - agent rl ( marl ). furthermore, the theory and related learning algorithms of them are briefly introduced. on the basis of analyses of pursuit game, aimed at the individual action learner, the thesis extends the rl algorithm for single agent, proposes the macrl - cc algorithm. finally, aimed at the joint action learner, a team - stochastic - games - based ( tsgs - based ) framework for multi - agents cooperative rl is defined
文章首先介紹了agent和多agent系統、以及多agent學習的一些基本概念，然後介紹了強化學習和多agent強化學習的研究現狀和未來發展方向。第二部分對強化學習理論和多agent強化學習理論進行了簡要介紹。在對pursuitgame問題進行初步分析的基礎上，針對獨立行為學習者，擴展了單agent強化學習演算法，提出了基於承諾和約定的多agent協同強化學習方法macrl - cc 。 -
In this thesis, firstly, we give an introduction and analysis to the complexity adaptive system and artificial life which are the mainstream research harvest currently. secondly, we provide a supplement to some theory include stream, diversity and adaptive agent. at last, on the basis of the theories, we complete a validating to the nature selection and heredity variance in computer, a basic conceiving about learning species and a validate model of the theories of the origins of currency and price equilibrium
本文對這方面最主流的研究成果復雜適應系統及人工生命的研究工作作了分析與介紹，並就其中的流、多樣性、適應性主體等理論作了自己的補充，最後我們結合對這些理論的認識，完成了自然選擇及遺傳變異的初步驗證、學習物種的基本構思、貨幣產生及市場價格均衡理論驗證的模型構建，在後續的工作中，我們除了繼續未完成的工作外，還將添加許多新的理論驗證。 -
Our information agent consists of four main function modules : user interface agent, searching agent, interest - learning agent, and result - disposing agent
我們開發的信息agent由四大功能模塊組成，分別是用戶介面agent 、搜索agent 、興趣學習agent 、結果處理agent 。 -
Surrounding the par method, combining the learning theory, java design pattern, agent orient programming, the icai platform was designed using uml and realize by j2ee. the platform can not only teach learners in accordance of their aptitude but also help to develop their programming ability, with the advantages of independence of os, easiness to migrate extend and maintain
文中的程序設計智能教學軟體平臺以薛錦雲教授提出的par方法為主要教學內容，結合建構主義等學習理論，用uml進行系統建模，引入java設計模式與面向agent編程等思想，基於j2ee技術實現；不僅達到了因材施教，提高學員設計正確程序的能力等功能，而且使該教學平臺具有與操作系統無關性、可移植性、可擴充性、可維護性等優點。 -
While, some algorithms of machine learning are introduced to get the intelligence of the individual of hfutagent which makes individual skills in the robocup. finally, we realize the multi - agent cooperation mechanism using the knoledge of soccer experts. in our system, a typical cooperation method in robocup called sbsp is used, and we explains how to use reinforcement learning method to reach the goal of local cooperation, and the offense and defense strategy system is build by decision - theoretic
在本文中，首先介紹了典型的agent結構和mas模型和模擬機器人足球的一些主要模型：設計了一個分層的agent結構? hfutagent ，通過機器學習演算法實現了agent的個體智能；最後結合足球領域專家的知識實現了agent間的協作，其中使用了robocup中一個典型的協作方法- sbsp ，設計了一個通過強化學習的方法來達到agent之間的局部協作，把基於效用的對策論方法引入了hfutteam的進攻體系和防守體系中。 -
This research addressed an urban traffic intelligent control system, which adopts a multi - agents coordination in urban traffic control to coordinate the signal of adjacent intersections for eliminating the congestion of traffic network. an agent represents a signal intersection control, and multi - agents realize coordination of multiple intersections to eliminate congestion. based on recursive modeling method and bayesian learning that enables an agent to select his rational action by examining with other agents by modeling their decision making in conjunction with dynamic belief update. based on this method, a simplified multi - agent traffic control system is established and the results demonstrate its effectiveness. it is very important for its
本文中提出一種城市交通智能控制系統，針對城市交通網路中相鄰交叉口的交通流可能相互沖突，即局部交通流的優化可能引起其他區域交通狀況的惡化的問題，採用多智能體協調控制方法來協調相鄰交叉口處的控制信號消除網路中的交通擁塞.提出以一個智能體的方式實現一個信號燈交叉口控制，對多個信號燈交叉口形成的交通網路採用多智能體協調控制的方式實現網路流量優化來消除擁塞.文中提出由遞歸建模和改進的貝葉斯學習相結合的多智能體系統來使智能體可以確定其他智能體的準確模型並實時更新信息，並基於上述方法在簡單的交通網路模型上建立了多智能體交通控制系統，模擬結果表明了方法的有效性，對實現智能交通系統有重要意義 -
The first chapter analyzes the existing problem of agent and presents the meaning and content, which this paper confined to ; the general structure, mobile agent platform and key technologies and researches of specifications are given in the second chapter ; the paper attaches more to intelligence model of mobile agent, migration plan, training and learning of mobile agent, migration strategy based on correlation, migration strategy based on correlation and neural network in the third chapter4 ; in the fourth chapter we give simulation experiment based on above migration strategy and analyze the experiment result ; the last chapter sums up the researches of this paper and the unsettled problems
最後簡要總結論文的主要工作，並提出今後要做的工作。本文共分五章。第一章分析現有agent存在的問題，提出本文研究內容與意義；第二章概述了agent 、移動agent平臺及關鍵技術、規范的研究情況；本文重點在第三章討論了移動agent智能遷移模型、 agent遷移計劃、 agent學習與訓練，基於相關分析agent遷移策略，基於相關分析與神經網路agent遷移策略；第四章對提出的agent遷移策略進行模擬實驗並分析實驗結果。 -
In our simulation experiment in aglet, stochastic selecting method takes more time than ann method to prove our suppose correct. by learning agent can obtain a more superior result and get the basi s for reasonable choice host. this paper is organized as follow
通過人工神經網路學習后的agent執行時間比隨機選擇時間少，並在aglet平臺對此演算法進行了模擬實驗，通過學習的agent能獲得較優的結果，為合理的選擇host提供依據。 -
In fact, a reinforcement - learning agent learns through its interaction with the environment. mas is often applied into open 、 complex and dynamic enviroment, in which a single agent is insufficient to solve the faced task, so that agents must do their work cooperatively
多agent系統常被應用於開放、復雜、動態變化的環境，單個agent的能力已不能勝任所面臨的任務，尤其是具有相同目標的系統， agent之間必須協同求解。 -
After taking into account the elements of social capital, learning mechanism, learning agent, learning types and the contents, the paper summarizes four models, i. e. the model based the calculated trust, the model based on the interpersonal trust, the model based on the interorganizational trust and the model based on the general trust
然後，在綜合考慮社會資本、學習機制、學習代理、學習類型、學習內容和行業等要素的基礎上，本文歸納出四種學習模式，即基於計算型信任的學習模式、基於人際信任的學習模式、基於組織間信任的學習模式和基於全面信任的學習模式。
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