“多智能体系统”的版本间的差异

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|keywords=人工社会,社会分析,计算模型
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|keywords=多智能体,自组织,计算模型
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|description=复杂系统,涌现，计算社会学
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|description=是一种由多个相互作用的智能体组成的计算系统。
 
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#重定向 [[多智能体系统]]
 
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[[Image:IntelligentAgent-SimpleReflex.png|thumb|right|简单反应体]]
 
[[Image:IntelligentAgent-SimpleReflex.png|thumb|right|简单反应体]]
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[[Image:IntelligentAgent-Learning.png|thumb|right|学习主体]]
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[[Image:IntelligentAgent-Learning.png|thumb|right|学习智能体]]
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A '''multi-agent system''' ('''MAS''' or "self-organized system") is a computerized system composed of multiple interacting [[intelligent agent]]s{{citation needed|date=July 2019}}. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a [[monolithic system]] to solve.<ref name="A Multi Agent-Based System for Securing University Campus: Design and Architecture - IEEE Conference Publication 2019">{{cite document | title=A Multi Agent-Based System for Securing University Campus: Design and Architecture - IEEE Conference Publication | date=2019-12-17 | doi=10.1109/ISMS.2010.25 }}</ref> Intelligence may include [[Scientific method|method]]ic, [[Function (computer science)|functional]], [[Algorithm|procedural]] approaches, [[algorithm]]ic [[search algorithm|search]] or [[reinforcement learning]].<ref name="ScienceDirect Topics 2016">{{cite web | title=Multi Agent Systems - an overview | website=ScienceDirect Topics | date=2016-01-01 | url=https://www.sciencedirect.com/topics/chemical-engineering/multi-agent-systems | access-date=2020-01-23}}</ref>
 
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A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning.
 
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'''多主体系统 multi-agent system（MAS）/self-organized system''' ，是一种由多个相互作用的智能体组成的计算系统。多主体系统可以解决一些单个主体或单一性系统难以解决的问题。其智能主要体现在条理性、功能性、程序性的行为方式，以及搜索算法和强化学习上。
 
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Despite considerable overlap, a multi-agent system is not always the same as an [[agent-based model]] (ABM).  The goal of an ABM is to search for explanatory insight into the collective behavior of agents (which don't necessarily need to be "intelligent") obeying simple rules, typically in natural systems, rather than in solving specific practical or engineering problems. The terminology of ABM tends to be used more often in the science, and MAS in engineering and technology.<ref name="Niazi-Hussain">{{cite journal |first1=Muaz |last1=Niazi |first2=Amir |last2=Hussain |year=2011 |title=Agent-based Computing from Multi-agent Systems to Agent-Based Models: A Visual Survey |journal=Scientometrics |volume=89 |issue=2 |pages=479–499 |doi=10.1007/s11192-011-0468-9 |url=https://www.researchgate.net/publication/220365334 |format=PDF|arxiv=1708.05872 }}</ref> Applications where multi-agent systems research may deliver an appropriate approach include online trading,<ref>{{cite journal |first1=Alex |last1=Rogers |first2=E. |last2=David |first3=J. |last3=Schiff |first4=N.R. |last4=Jennings |url=http://eprints.ecs.soton.ac.uk/12716/ |title=The Effects of Proxy Bidding and Minimum Bid Increments within eBay Auctions |journal=ACM Transactions on the Web |volume=1 |issue=2 |pages=9–es |year=2007|doi=10.1145/1255438.1255441 |citeseerx=10.1.1.65.4539 }}</ref> disaster response<ref>{{cite journal |first1=Nathan |last1=Schurr |first2=Janusz |last2=Marecki |first3=Milind |last3=Tambe |first4=Paul |last4=Scerri | first5=Nikhil |last5=Kasinadhuni |first6=J.P. |last6=Lewis |url=http://teamcore.usc.edu/papers/2005/SS105SchurrN.pdf |title=The Future of Disaster Response: Humans Working with Multiagent Teams using DEFACTO |year=2005}}</ref><ref>{{cite journal |last1=Genc|first1=Zulkuf  |url=http://www.gdmc.nl/gi4dmdocs/Gi4DM_2012_Genc.pdf |title=Agent-based information infrastructure for disaster management |journal=Intelligent Systems for Crisis Management |pages=349–355 |date=2013|display-authors=etal|doi=10.1007/_26 |isbn= |series=Lecture Notes in Geoinformation and Cartography }}</ref> and social structure modelling.<ref>{{cite journal |first1=Ron |last1=Sun|authorlink1=Ron Sun |first2=Isaac |last2=Naveh |url=http://jasss.soc.surrey.ac.uk/7/3/5.html |title=Simulating Organizational Decision-Making Using a Cognitively Realistic Agent Model |journal=Journal of Artificial Societies and Social Simulation}}</ref>
 
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Despite considerable overlap, a multi-agent system is not always the same as an agent-based model (ABM).  The goal of an ABM is to search for explanatory insight into the collective behavior of agents (which don't necessarily need to be "intelligent") obeying simple rules, typically in natural systems, rather than in solving specific practical or engineering problems. The terminology of ABM tends to be used more often in the science, and MAS in engineering and technology. Applications where multi-agent systems research may deliver an appropriate approach include online trading, disaster response and social structure modelling.
 
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尽管多主体系统和'''基于主体的模型''' '''Agent-based Model''' 有着很多的重叠，但是它们并不总是相同的。基于主体的模型目标在于解释那些遵循简单规则、可能并不是很“智能”的主体表现出的'''集群行为'''，一般被用于天然系统的研究中，而非实践和工程中解决具体的问题。因此“基于主体的模型”这个词更多地用在科学研究中，而“多主体系统”则更多地用于工程和技术。有关多主体系统的研究可能会对在线交易、灾害应急、社会结构建模等领域有着良好的应用价值。
 
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== Concepts 概念 ==
 
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Multi-agent systems consist of agents and their environment. Typically multi-agent systems research refers to [[software agent]]s. However, the agents in a multi-agent system could equally well be robots, humans or human teams. A multi-agent system may contain combined human-agent teams.
 
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Multi-agent systems consist of agents and their environment. Typically multi-agent systems research refers to software agents. However, the agents in a multi-agent system could equally well be robots, humans or human teams. A multi-agent system may contain combined human-agent teams.
 
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多主体系统由'''主体''' '''Agent'''及其所处的环境组成。典型的多主体系统研究的是[[软件主体]]。然而，多主体系统中的主体也可以是机器人、人类或人类团体。多主体系统还可以包含人类和其它主体的组合。
 
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Agents can be divided into types spanning simple to complex. Categories include:
 
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Agents can be divided into types spanning simple to complex. Categories include:
 
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不同主体可以按照从简单到复杂的顺序分为如下几个类型:
 
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* Passive agents<ref name=yoann2010>{{citation |first1=Yoann |last1=Kubera |first2=Philippe |last2=Mathieu |first3=Sébastien |last3=Picault |url=http://www.lifl.fr/SMAC/publications/pdf/aamas2010-everything.pdf |title=Everything can be Agent! |journal=Proceedings of the Ninth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'2010) |pages=1547–1548 |year=2010 }}</ref> or "agent without goals" (such as obstacle, apple or key in any simple simulation)
 
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* 被动主体，或“无目标主体”（比如障碍物、苹果或在简单仿真中的钥匙）
 
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* Active agents<ref name=yoann2010/> with simple goals (like birds in flocking, or wolf–sheep in [[Lotka–Volterra|prey-predator model]])
 
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* 具有简单目标的主动主体（比如鸟群中的鸟、“掠食者-猎物模型”中的狼和羊）
 
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* Cognitive agents (complex calculations)
 
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* 认知主体（可以进行复杂的计算）
 
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Agent environments can be divided into:
 
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Agent environments can be divided into:
 
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主体所处的环境可以分为:
 
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* Virtual
 
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* 虚拟的
 
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* Discrete
 
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* 离散的
 
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* Continuous
 
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* 连续的
 
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Agent environments can also be organized according to properties such as accessibility (whether it is possible to gather complete information about the environment), determinism (whether an action causes a definite effect), dynamics (how many entities influence the environment in the moment), discreteness (whether the number of possible actions in the environment is finite), episodicity (whether agent actions in certain time periods influence other periods),<ref>{{Russell Norvig 2003}}</ref> and dimensionality (whether spatial characteristics are important factors of the environment and the agent considers space in its decision making).<ref name="Salamon2011">{{cite book | last1 = Salamon | first1 = Tomas | title = Design of Agent-Based Models | location = Repin | publisher = Bruckner Publishing | year= 2011 | page = 22 | isbn =  | url=http://www.designofagentbasedmodels.info/}}</ref> Agent actions are typically mediated via an appropriate middleware. This middleware offers a first-class design abstraction for multi-agent systems, providing means to govern resource access and agent coordination.<ref>{{ cite journal |first1=Danny |last1=Weyns |first2=Amdrea |last2=Omicini |first3=James |last3=Odell |year=2007 |title=Environment as a first-class abstraction in multiagent systems |journal=Autonomous Agents and Multi-Agent Systems |volume=14 |issue=1 |pages=5–30 |id= |url=http://www.cs.kuleuven.be/~danny/papers/2007JAAMAS.pdf |accessdate=2013-05-31 |quote= |doi=10.1007/s10458-006-0012-0 |citeseerx=10.1.1.154.4480 }}{{dead link|date=December 2017 |bot=InternetArchiveBot |fix-attempted=yes }}</ref>
 
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Agent environments can also be organized according to properties such as accessibility (whether it is possible to gather complete information about the environment), determinism (whether an action causes a definite effect), dynamics (how many entities influence the environment in the moment), discreteness (whether the number of possible actions in the environment is finite), episodicity (whether agent actions in certain time periods influence other periods), and dimensionality (whether spatial characteristics are important factors of the environment and the agent considers space in its decision making). Agent actions are typically mediated via an appropriate middleware. This middleware offers a first-class design abstraction for multi-agent systems, providing means to govern resource access and agent coordination.
 
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我们也可以从如下角度考察主体所处的环境，如：可知性（是否可以搜集到关于环境的完整信息）、确定性（行为造成的影响是否是确定的）、动态性（同一时刻有多少主体影响环境）、离散性（主体可以采取的行动是否是有限的）、时序性（主体在某一特定时段内的行为是否影响其它时段）、维度性（环境是否具有鲜明的空间特征，以及主体在做决策时是否考虑空间因素）。主体的行为通常受到'''中间件 Middleware''' 的调控。中间件使得人们可以管理可用资源、调控主体，极大地方便了人们对于多主体系统进行抽象设计。
 
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=== Characteristics 特点 ===
 
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The agents in a multi-agent system have several important characteristics:<ref>{{cite book |first=Michael |last=Wooldridge |title=An Introduction to MultiAgent Systems |publisher=[[John Wiley & Sons]] |year=2002 |pages=366 |isbn=978-0-471-49691-5}}</ref>
 
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The agents in a multi-agent system have several important characteristics:
 
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多主体系统中的主体有如下几个重要特点:
 
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* Autonomy: agents at least partially independent, self-aware, [[Autonomous agent|autonomous]]
 
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* Local views: no agent has a full global view, or the system is too complex for an agent to exploit such knowledge
 
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* Decentralization: no agent is designated as controlling (or the system is effectively reduced to a monolithic system)<ref>{{cite journal |first1=Liviu |last1=Panait |first2=Sean |last2=Luke |url=http://cs.gmu.edu/~eclab/papers/panait05cooperative.pdf|title=Cooperative Multi-Agent Learning: The State of the Art |journal=Autonomous Agents and Multi-Agent Systems |volume=11 |issue=3 |pages=387–434 |year=2005 |doi=10.1007/s10458-005-2631-2|citeseerx=10.1.1.307.6671 }}</ref>
 
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* 自主性：主体至少是部分独立的，可以自我感知、自主行动。
 
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* 局部视野：没有一个主体可以掌握系统的全貌，或者由于系统十分复杂，没有一个主体可以利用全局性的知识。
 
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* 无中心性：不指定任何一个控制性的主体（或者说多主体系统不会退化为一个单一化系统）。
 
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=== Self-organization and Self-direction 自组织与自引导 ===
 
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Multi-agent systems can manifest [[self-organisation]] as well as self-direction and other [[control theory|control paradigms]] and related complex behaviors even when the individual strategies of all their agents are simple.{{citation needed|date=December 2016}} When agents can share knowledge using any agreed language, within the constraints of the system's communication protocol, the approach may lead to a common improvement. Example languages are [[KQML|Knowledge Query Manipulation Language]] (KQML) or [[Agent Communication Language]] (ACL).
 
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Multi-agent systems can manifest self-organisation as well as self-direction and other control paradigms and related complex behaviors even when the individual strategies of all their agents are simple. When agents can share knowledge using any agreed language, within the constraints of the system's communication protocol, the approach may lead to a common improvement. Example languages are Knowledge Query Manipulation Language (KQML) or Agent Communication Language (ACL).
 
  
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即使所有单个主体的策略都很简单，多主体系统也可以表现出自组织、自引导等控制范式以及相关的复杂行为。当主体之间可以在系统通信规范的约束下使用一些约定的语言来共享信息时，这种方法可能带来主体间的共赢。例如'''知识查询操作语言''' '''Knowledge Query Manipulation Language'''和'''主体通信语言 Agent Communication Language'''是这类语言中的两个典型例子。
 
  
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=== System paradigms 系统模式 ===
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'''多智能体系统 multi-agent system（MAS）'''，也称'''多主体系统'''或'''自组织系统 self-organized system '''，是一种由多个相互作用的智能体组成的计算系统。多智能体系统可以解决一些单个多智能体或单一性系统难以解决的问题。<ref name="A Multi Agent-Based System for Securing University Campus: Design and Architecture - IEEE Conference Publication 2019">{{cite document | title=A Multi Agent-Based System for Securing University Campus: Design and Architecture - IEEE Conference Publication | date=2019-12-17 | doi=10.1109/ISMS.2010.25 }}</ref>其智能主要体现在条理性、功能性、程序性的行为方式，以及搜索算法和强化学习<ref name="ScienceDirect Topics 2016">{{cite web | title=Multi Agent Systems - an overview | website=ScienceDirect Topics | date=2016-01-01 | url=https://www.sciencedirect.com/topics/chemical-engineering/multi-agent-systems | access-date=2020-01-23}}</ref>上。
  
  
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尽管多智能体系统和'''基于主体的模型 Agent-based model（ABM）''' 有着很多的重叠，但是它们并不总是相同的。基于主体的模型目标在于解释那些遵循简单规则、可能并不是很“智能”的主体表现出的'''集群行为'''，一般被用于天然系统的研究中，而非实践和工程中解决具体的问题。因此“基于主体的模型”这个词更多地用在科学研究中，而“多智能体系统”则更多地用于工程和技术。<ref name="Niazi-Hussain">{{cite journal |first1=Muaz |last1=Niazi |first2=Amir |last2=Hussain |year=2011 |title=Agent-based Computing from Multi-agent Systems to Agent-Based Models: A Visual Survey |journal=Scientometrics |volume=89 |issue=2 |pages=479–499 |doi=10.1007/s11192-011-0468-9 |url=https://www.researchgate.net/publication/220365334 |format=PDF|arxiv=1708.05872 }}</ref>有关多智能体系统的研究可能会对在线交易、<ref>{{cite journal |first1=Alex |last1=Rogers |first2=E. |last2=David |first3=J. |last3=Schiff |first4=N.R. |last4=Jennings |url=http://eprints.ecs.soton.ac.uk/12716/ |title=The Effects of Proxy Bidding and Minimum Bid Increments within eBay Auctions |journal=ACM Transactions on the Web |volume=1 |issue=2 |pages=9–es |year=2007|doi=10.1145/1255438.1255441 |citeseerx=10.1.1.65.4539 }}</ref> 灾害应急、<ref>{{cite journal |first1=Nathan |last1=Schurr |first2=Janusz |last2=Marecki |first3=Milind |last3=Tambe |first4=Paul |last4=Scerri | first5=Nikhil |last5=Kasinadhuni |first6=J.P. |last6=Lewis |url=http://teamcore.usc.edu/papers/2005/SS105SchurrN.pdf |title=The Future of Disaster Response: Humans Working with Multiagent Teams using DEFACTO |year=2005}}</ref><ref>{{cite journal |last1=Genc|first1=Zulkuf  |url=http://www.gdmc.nl/gi4dmdocs/Gi4DM_2012_Genc.pdf |title=Agent-based information infrastructure for disaster management |journal=Intelligent Systems for Crisis Management |pages=349–355 |date=2013|display-authors=etal|doi=10.1007/_26 |isbn= |series=Lecture Notes in Geoinformation and Cartography }}</ref> 社会结构建模<ref>{{cite journal |first1=Ron |last1=Sun|authorlink1=Ron Sun |first2=Isaac |last2=Naveh |url=http://jasss.soc.surrey.ac.uk/7/3/5.html |title=Simulating Organizational Decision-Making Using a Cognitively Realistic Agent Model |journal=Journal of Artificial Societies and Social Simulation}}</ref>
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等领域有着良好的应用价值。
  
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Many MAS are implemented in computer simulations, stepping the system through discrete "time steps". The MAS components communicate typically using a weighted request matrix, e.g.
 
  
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Many MAS are implemented in computer simulations, stepping the system through discrete "time steps". The MAS components communicate typically using a weighted request matrix, e.g.
 
  
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很多多主体系统是在计算机仿真中实现的，按照离散的“时间步长”逐步地演化。多主体系统中的组分通常使用加权请求矩阵和加权响应矩阵进行通信。下面是几个加权请求矩阵的例子：
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== 概念 ==
  
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  Speed-VERY_IMPORTANT: min=45&nbsp;mph,
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多智能体系统由'''智能体 Agent'''及其所处的环境组成。典型的多智能体系统研究的是[[软件智能体 software agent]]。然而，多智能体系统中的智能体也可以是机器人、人类或人类团体。多智能体系统还可以包含人类和其它智能体的组合。
  
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  Speed-VERY_IMPORTANT: min=45&nbsp;mph,
 
  
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Speed-VERY_IMPORTANT: min=45&nbsp;mph, （速度——非常重要: 最小每小时45英里）
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不同智能体可以按照从简单到复杂的顺序分为如下几个类型:
  
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  Path length-MEDIUM_IMPORTANCE: max=60 expectedMax=40,
 
  
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  Path length-MEDIUM_IMPORTANCE: max=60 expectedMax=40,
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* 被动智能体，<ref name=yoann2010>{{citation |first1=Yoann |last1=Kubera |first2=Philippe |last2=Mathieu |first3=Sébastien |last3=Picault |url=http://www.lifl.fr/SMAC/publications/pdf/aamas2010-everything.pdf |title=Everything can be Agent! |journal=Proceedings of the Ninth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'2010) |pages=1547–1548 |year=2010 }}</ref>或“无目标智能体”（比如障碍物、苹果或在简单仿真中的钥匙）
  
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Path length-MEDIUM_IMPORTANCE: max=60 expectedMax=40, （路径长度——中等重要：最大60，预期最大40）
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* 具有简单目标的主动智能体<ref name=yoann2010/>（比如鸟群中的鸟、“掠食者-猎物模型 Lotka–Volterra”中的狼和羊）
  
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  Max-Weight-UNIMPORTANT
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* 认知智能体（可以进行复杂的计算）
  
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  Max-Weight-UNIMPORTANT
 
  
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Max-Weight-UNIMPORTANT, （最大重量——不重要）
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智能体所处的环境可以分为:
  
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  Contract Priority-REGULAR
 
  
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  Contract Priority-REGULAR
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* 虚拟的 Virtual
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* 离散的 Discrete
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* 连续的 Continuous
  
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Contract Priority-REGULAR, （协议优先级——常规）
 
  
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and a weighted response matrix, e.g.  
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我们也可以从如下角度考察智能体所处的环境，如：可知性 accessibility（是否可以搜集到关于环境的完整信息）、确定性 determinism（行为造成的影响是否是确定的）、动态性 dynamics（同一时刻有多少智能体影响环境）、离散性 discreteness（智能体可以采取的行动是否是有限的）、时序性 episodicity（智能体在某一特定时段内的行为是否影响其它时段）<ref>{{Russell Norvig 2003}}</ref>、维度性 dimensionality（环境是否具有鲜明的空间特征，以及智能体在做决策时是否考虑空间因素）。智能体的行为通常受到'''中间件 Middleware''' 的调控。<ref name="Salamon2011">{{cite book | last1 = Salamon | first1 = Tomas | title = Design of Agent-Based Models | location = Repin | publisher = Bruckner Publishing | year= 2011 | page = 22 | isbn =  | url=http://www.designofagentbasedmodels.info/}}</ref>中间件使得人们可以管理可用资源、调控智能体，极大地方便了人们对于多智能体系统进行抽象设计。<ref>{{ cite journal |first1=Danny |last1=Weyns |first2=Amdrea |last2=Omicini |first3=James |last3=Odell |year=2007 |title=Environment as a first-class abstraction in multiagent systems |journal=Autonomous Agents and Multi-Agent Systems |volume=14 |issue=1 |pages=5–30 |id= |url=http://www.cs.kuleuven.be/~danny/papers/2007JAAMAS.pdf |accessdate=2013-05-31 |quote= |doi=10.1007/s10458-006-0012-0 |citeseerx=10.1.1.154.4480 }}{{dead link|date=December 2017 |bot=InternetArchiveBot |fix-attempted=yes }}</ref>
  
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and a weighted response matrix, e.g.
 
  
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还有加权响应矩阵的例子：
 
  
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  Speed-min:50 but only if weather sunny,
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=== 特点 ===
  
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  Speed-min:50 but only if weather sunny,
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多智能体系统中的智能体有如下几个重要特点：<ref>{{cite book |first=Michael |last=Wooldridge |title=An Introduction to MultiAgent Systems |publisher=[[John Wiley & Sons]] |year=2002 |pages=366 |isbn=978-0-471-49691-5}}</ref>
  
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Speed-min:50 but only if weather sunny, （最小速度：天气晴朗时50）
 
  
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  Path length:25 for sunny / 46 for rainy
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* 自主性：智能体至少是部分独立的，可以自我感知、自主行动。
  
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  Path length:25 for sunny / 46 for rainy
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* 局部视野：没有一个智能体可以掌握系统的全貌，或者由于系统十分复杂，没有一个智能体可以利用全局性的知识。
  
−
Path length:25 for sunny / 46 for rainy, （路径长度: 晴天25，雨天46）
+
* 无中心性：不指定任何一个控制性的智能体（或者说多智能体系统不会退化为一个单一化系统）。<ref>{{cite journal |first1=Liviu |last1=Panait |first2=Sean |last2=Luke |url=http://cs.gmu.edu/~eclab/papers/panait05cooperative.pdf|title=Cooperative Multi-Agent Learning: The State of the Art |journal=Autonomous Agents and Multi-Agent Systems |volume=11 |issue=3 |pages=387–434 |year=2005 |doi=10.1007/s10458-005-2631-2|citeseerx=10.1.1.307.6671 }}</ref>
  
−
  Contract Priority-REGULAR
 
  
−
  Contract Priority-REGULAR
 
  
−
Contract Priority-REGULAR, （协议优先级——常规）
+
=== 自组织与自引导 ===
  
−
  note – ambulance will override this priority and you'll have to wait
 
  
−
  note – ambulance will override this priority and you'll have to wait
+
即使所有单个智能体的策略都很简单，多智能体系统也可以表现出[[自组织]]、自引导等控制范式以及相关的复杂行为。当智能体之间可以在系统通信规范的约束下使用一些约定的语言来共享信息时，这种方法可能带来智能体间的共赢。例如'''知识查询操作语言 Knowledge Query Manipulation Language（KQML）'''和'''智能体通信语言 Agent Communication Language（ACL）'''是这类语言中的两个典型例子。
  
−
note – ambulance will override this priority and you'll have to wait, （注意，救护车比这具有更高的优先级，你必须等待）
 
  
 +
=== 系统模式 ===
  
 +
很多多智能体系统是在计算机仿真中实现的，按照离散的“时间步长 time steps”逐步地演化。多智能体系统中的组分通常使用加权请求矩阵和加权响应矩阵进行通信。下面是几个加权请求矩阵的例子：
  
 +
速度——非常重要: 最小每小时45英里
 +
路径长度——中等重要：最大60，预期最大40
 +
最大重量——不重要
 +
协议优先级——常规
  
−
A challenge-response-contract scheme is common in MAS systems, where
 
  
−
* First a '''"'''Who can?'''"''' question is distributed.
+
加权响应矩阵的例子：
  
−
* Only the relevant components respond: '''"'''I can, at this price'''"'''.
+
最小速度：天气晴朗时50
 +
路径长度: 晴天25，雨天46
 +
协议优先级——常规
 +
注意，救护车具有更高的优先级，你必须等待
  
−
* Finally, a contract is set up, usually in several short communication steps between sides,
 
  
−
also considering other components, evolving "contracts" and the restriction sets of the component algorithms.
+
“请求-响应-协议 challenge-response-contrac”方案在多智能体系统当中很常见，在这个方案中：
−
 
 
−
“请求-响应-协议”方案在多主体系统当中很常见，在这个方案中：
 
−
 
 
−
* 首先，一个主体将一个“谁可以...？”形式的请求分发出去。
 
−
 
 
−
* 其次，与该请求相关的主体会响应道：“我可以，前提是...”。
 
  
 +
* 首先，一个智能体将一个“谁可以...？”形式的请求分发出去。
 +
* 其次，与该请求相关的智能体会响应道：“我可以，前提是...”。
 
* 最后，双方一般会通过几次很短的沟通步骤达成一个协议。
 
* 最后，双方一般会通过几次很短的沟通步骤达成一个协议。
  
−
同时，这个过程也会考虑其它的主体、考虑协议的改进以及算法对主体的约束。
 
−
 
−
 
−
 
−
Another paradigm commonly used with MAS is the "[[pheromone]]", where components leave information for other nearby components. These pheromones may evaporate/concentrate with time, that is their values may decrease (or increase).
 
−
 
−
Another paradigm commonly used with MAS is the "pheromone", where components leave information for other nearby components. These pheromones may evaporate/concentrate with time, that is their values may decrease (or increase).
 
−
 
−
多主体系统经常使用的另一个通信模式是'''信息素''''''Pheromone'''。在此模式下，各主体为附近的其它主体留下信息素来传递信息。随着时间的推移，这些信息素可能“蒸发”或累积，也就是其权重的下降或上升。
 
−
 
−
=== Properties 性质 ===
 
  
 +
同时，这个过程也会考虑其它的智能体、考虑协议的改进以及算法对智能体的约束。
  
  
−
MAS tend to find the best solution for their problems without intervention. There is high similarity here to physical phenomena, such as energy minimizing, where physical objects tend to reach the lowest energy possible within the physically constrained world. For example: many of the cars entering a metropolis in the morning will be available for leaving that same metropolis in the evening.
+
多智能体系统经常使用的另一个通信模式是'''信息素 Pheromone'''。在此模式下，各智能体为附近的其它智能体留下信息素来传递信息。随着时间的推移，这些信息素可能“蒸发”或累积，也就是其权重的下降或上升。
  
−
MAS tend to find the best solution for their problems without intervention. There is high similarity here to physical phenomena, such as energy minimizing, where physical objects tend to reach the lowest energy possible within the physically constrained world. For example: many of the cars entering a metropolis in the morning will be available for leaving that same metropolis in the evening.
 
  
−
多主体系统常常可以在没有干预的情况下为它们的问题找到最好的解决方案，这与物理现象有很高的相似性。比如在能量最小化的例子中，物理主体倾向于在物理条件约束的世界中达到可能的最低能量。
+
=== 性质 ===
  
  
−
The systems also tend to prevent propagation of faults, self-recover and be fault tolerant, mainly due to the redundancy of components.
+
多智能体系统常常可以在没有干预的情况下为它们的问题找到最好的解决方案，这与物理现象有很高的相似性。比如在能量最小化的例子中，物理智能体倾向于在物理条件约束的世界中达到可能的最低能量。
  
−
The systems also tend to prevent propagation of faults, self-recover and be fault tolerant, mainly due to the redundancy of components.
 
  
 
系统还具有防止故障传播、自恢复和容错的特点，这主要是由于组件的冗余性。
 
系统还具有防止故障传播、自恢复和容错的特点，这主要是由于组件的冗余性。
第231行： 第102行：
  
  
−
== Researches 研究 ==
+
== 研究 ==
−
 
 
−
 
 
−
 
 
−
The study of multi-agent systems is "concerned with the development and analysis of sophisticated [[artificial intelligence|AI]] problem-solving and control architectures for both single-agent and multiple-agent systems."<ref>{{cite web |url=http://mas.cs.umass.edu/ |title=The Multi-Agent Systems Lab |publisher=[[University of Massachusetts Amherst]] |accessdate=Oct 16, 2009}}</ref> Research topics include:
 
−
 
 
−
The study of multi-agent systems is "concerned with the development and analysis of sophisticated AI problem-solving and control architectures for both single-agent and multiple-agent systems." Research topics include:
 
−
 
 
−
多主体系统的研究“与单主体和多主体系统中复杂的人工智能问题解决和控制架构的开发和分析有关”。主要的研究主题包括：
 
−
 
 
−
* agent-oriented software engineering
 
−
 
 
−
* beliefs, desires, and intentions ([[BDI software agent|BDI]])
 
−
 
 
−
* [[Consensus dynamics|cooperation and coordination]]
 
−
 
 
−
* [[distributed constraint optimization]] (DCOPs)
 
−
 
 
−
* organization
 
−
 
 
−
* communication
 
−
 
 
−
* negotiation
 
−
 
 
−
* [[cooperative distributed problem solving|distributed problem solving]]
 
−
 
 
−
*[[multi-agent learning]]<ref>{{citation|first1=Stefano |last1=Albrecht |first2=Peter |last2=Stone |year=2017 |contribution=Multiagent Learning: Foundations and Recent Trends. Tutorial |title=IJCAI-17 conference |url=http://www.cs.utexas.edu/~larg/ijcai17_tutorial/multiagent_learning.pdf}}</ref>
 
−
 
 
−
* [[agent mining]]
 
−
 
 
−
* scientific communities (e.g., on biological flocking, language evolution, and economics)<ref>{{ cite journal | last =Cucker | first =Felipe | authorlink = | author2=Steve Smale | year =2007 | title =The Mathematics of Emergence | journal = Japanese Journal of Mathematics | volume = 2| issue = | pages = 197–227| id = | url =http://ttic.uchicago.edu/~smale/papers/math-of-emergence.pdf | accessdate = 2008-06-09 | quote =| doi =10.1007/s11537-007-0647-x | author2-link =Stephen Smale }}</ref><ref>{{ cite journal | last =Shen | first =Jackie (Jianhong) | authorlink = | year =2008 | title =Cucker–Smale Flocking under Hierarchical Leadership | journal =SIAM J. Appl. Math. | volume =68 | issue =3 | pages = 694–719| id = | url =http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=SMJMAP000068000003000694000001&idtype=cvips&gifs=yes | accessdate = 2008-06-09 | quote =| doi =10.1137/060673254 | arxiv =q-bio/0610048}}
 
−
 
 
−
</ref>
 
−
 
 
−
</ref>
 
  
−
/ 参考
+
多智能体系统的研究是与单智能体和多智能体系统中复杂的人工智能问题解决和控制架构的开发和分析有关。<ref>{{cite web |url=http://mas.cs.umass.edu/ |title=The Multi-Agent Systems Lab |publisher=University of Massachusetts Amherst |accessdate=Oct 16, 2009}}</ref>主要的研究主题包括：
−
 
 
−
* dependability and fault-tolerance
 
−
 
 
−
* robotics,<ref>{{citation|last1=Ahmed |first1=S. |pages=459 |last2=Karsiti |first2=M.N. |url=http://eprints.utp.edu.my/320/|title=2007 IEEE International Conference on Electro/Information Technology |year=2007|doi=10.1109/EIT.2007.4374547|contribution=A testbed for control schemes using multi agent nonholonomic robots |isbn=978-1-4244-0940-2 }}</ref> multi-robot systems (MRS), robotic clusters
 
−
 
 
−
* 以主体为导向的软件工程
 
−
 
 
−
* 信念，欲望和动机 ([[BDI]])
 
−
 
 
−
* [[合作和协调]]
 
−
 
 
−
* [[分布式约束优化]] (DCOPs)
 
  
 +
* 以智能体为导向的软件工程
 +
* 信念，欲望和动机 beliefs, desires, and intentions（[[BDI]]）
 +
* 合作和协调
 +
* 分布式约束优化 distributed constraint optimization（DCOPs）
 
* 组织
 
* 组织
−
 
 
* 沟通
 
* 沟通
−
 
 
* 谈判
 
* 谈判
−
 
+
* 分布式问题求解
−
* [[分布式问题求解]]
+
* 多智能体学习
−
 
+
* 智能体挖掘
−
* [[多主体学习]]
+
* 科学化社群（比如生物的群体、语言的进化和经济体）<ref>{{ cite journal | last =Cucker | first =Felipe | authorlink = | author2=Steve Smale | year =2007 | title =The Mathematics of Emergence | journal = Japanese Journal of Mathematics | volume = 2| issue = | pages = 197–227| id = | url =http://ttic.uchicago.edu/~smale/papers/math-of-emergence.pdf | accessdate = 2008-06-09 | quote =| doi =10.1007/s11537-007-0647-x | author2-link =Stephen Smale }}</ref><ref>{{ cite journal | last =Shen | first =Jackie (Jianhong) | authorlink = | year =2008 | title =Cucker–Smale Flocking under Hierarchical Leadership | journal =SIAM J. Appl. Math. | volume =68 | issue =3 | pages = 694–719| id = | url =http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=SMJMAP000068000003000694000001&idtype=cvips&gifs=yes | accessdate = 2008-06-09 | quote =| doi =10.1137/060673254 | arxiv =q-bio/0610048}}</ref>
−
 
 
−
* [[主体挖掘]]
 
−
 
 
−
* 科学化社群（比如生物的群体、语言的进化和经济体）
 
−
 
 
 
* 依赖性和容错
 
* 依赖性和容错
−
 
+
* 机器人学，<ref>{{citation|last1=Ahmed |first1=S. |pages=459 |last2=Karsiti |first2=M.N. |url=http://eprints.utp.edu.my/320/|title=2007 IEEE International Conference on Electro/Information Technology |year=2007|doi=10.1109/EIT.2007.4374547|contribution=A testbed for control schemes using multi agent nonholonomic robots |isbn=978-1-4244-0940-2 }}</ref>多机器人系统 (MRS) 和机器人集群
−
* 机器人学，多机器人系统 (MRS) 和机器人集群
 
−
 
 
−
 
 
−
 
 
−
== Frameworks 框架 ==
 
−
 
 
−
 
 
−
 
 
−
Frameworks have emerged that implement common standards (such as the [[Foundation for Intelligent Physical Agents|FIPA]] and [[Object Management Group|OMG]] MASIF<ref>{{Cite web|url=https://www.omg.org/cgi-bin/doc?orbos/97-10-05|title=OMG Document – orbos/97-10-05 (Update of Revised MAF Submission)|website=www.omg.org|access-date=2019-02-19}}</ref> standards). These frameworks e.g. [[Java Agent Development Framework|JADE]], save time and aid in the standardization of MAS development.<ref>{{cite journal |first1=Salman |last1=Ahmed  |first2=Mohd N. |last2=Karsiti |first3=Herman |last3=Agustiawan |title=A development framework for collaborative robots using feedback control|year=2007|citeseerx=10.1.1.98.879  }}</ref>
 
−
 
 
−
Frameworks have emerged that implement common standards (such as the FIPA and OMG MASIF standards). These frameworks e.g. JADE, save time and aid in the standardization of MAS development.
 
−
 
 
−
一些贯彻共同标准的框架已经出现了（例如 FIPA 和 OMG MASIF 标准）。JADE等框架可以为标准化的多主体系统开发节省时间和精力。
 
−
 
 
−
 
 
−
 
 
−
Currently though, no standard is actively maintained from FIPA or OMG. Efforts for further development of software agents in industrial context are carried out in IEEE IES technical committee on Industrial Agents.<ref>{{Cite web|url=https://tcia.ieee-ies.org/|title=IEEE IES Technical Committee on Industrial Agents (TC-IA)|website=tcia.ieee-ies.org|access-date=2019-02-19}}</ref>
 
−
 
 
−
Currently though, no standard is actively maintained from FIPA or OMG. Efforts for further development of software agents in industrial context are carried out in IEEE IES technical committee on Industrial Agents.
 
−
 
 
−
不过目前，FIPA 或 OMG 还没有主动维护任何标准。在工业领域中进一步开发软件主体的努力主要来自 IEEE 工业电子协会的工业主体技术委员会。
 
−
 
 
−
 
 
−
 
 
−
== Applications 应用 ==<!-- Although MAS is still strictly a research topic, many graphic computer games today are developed using MAS algorithms and MAS frameworks. -->
 
−
 
 
−
 
 
−
<!-- Although MAS is still strictly a research topic, many graphic computer games today are developed using MAS algorithms and MAS frameworks. -->
 
−
 
 
−
尽管多主体系统还仍然是一个严格的学术话题，但今天许多图形化的电脑游戏都是使用多主体系统的算法框架开发的。
 
−
 
 
−
MAS have not only been applied in academic research, but also in industry.<ref>{{Cite book|title=Industrial agents : emerging applications of software agents in industry|others=Leitão, Paulo,, Karnouskos, Stamatis|isbn=978-0128003411|location=Amsterdam, Netherlands|oclc=905853947|last1 = Leitão|first1 = Paulo|last2 = Karnouskos|first2 = Stamatis|date = 2015-03-26}}</ref> MAS are applied in the real world to graphical applications such as computer games. Agent systems have been used in films.<ref>{{cite web |publisher=[[Massive (software)|MASSIVE]] |url=http://www.massivesoftware.com/film.html |title=Film showcase|accessdate=28 April 2012}}</ref> It is widely advocated for use in networking and mobile technologies, to achieve automatic and dynamic load balancing, high scalability and self-healing networks. They are being used for coordinated defence systems.
 
−
 
 
−
MAS have not only been applied in academic research, but also in industry. MAS are applied in the real world to graphical applications such as computer games. Agent systems have been used in films. It is widely advocated for use in networking and mobile technologies, to achieve automatic and dynamic load balancing, high scalability and self-healing networks. They are being used for coordinated defence systems.
 
−
 
 
−
除了学术研究之外，多主体系统在工业中也有应用。它在现实世界中不仅被用于电脑游戏等图形应用程序，还已经延伸到了电影中。它在网络和移动技术中实现自动和动态的负载平衡、高可扩展性和自愈网络的作用广为流传。除此之外，多主体系统还被用于协同防御体系中。
 
  
  
 +
== 框架 ==
  
−
Other applications<ref>{{Cite journal|last=Leitao|first=Paulo|last2=Karnouskos|first2=Stamatis|last3=Ribeiro|first3=Luis|last4=Lee|first4=Jay|last5=Strasser|first5=Thomas|last6=Colombo|first6=Armando W.|date=2016|title=Smart Agents in Industrial Cyber–Physical Systems|journal=Proceedings of the IEEE|volume=104|issue=5|pages=1086–1101|doi=10.1109/JPROC.2016.2521931|issn=0018-9219|url=http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-128744}}</ref> include [[transportation]],<ref name="surtrac2012b">Xiao-Feng Xie, S. Smith, G. Barlow. [http://www.wiomax.com/team/xie/paper/ICAPS12.pdf Schedule-driven coordination for real-time traffic network control]. International Conference on Automated Planning and Scheduling (ICAPS), São Paulo, Brazil, 2012: 323–331.</ref> logistics,<ref name="compare">{{Cite journal | last1 = Máhr | first1 = T. S. | last2 = Srour | first2 = J. | last3 = De Weerdt | first3 = M. | last4 = Zuidwijk | first4 = R. | title = Can agents measure up? A comparative study of an agent-based and on-line optimization approach for a drayage problem with uncertainty | doi = 10.1016/j.trc.2009.04.018 | journal = Transportation Research Part C: Emerging Technologies | volume = 18 | pages = 99–119 | year = 2010 | pmid =  | pmc = | citeseerx = 10.1.1.153.770 }}</ref> graphics, manufacturing, [[power system]]<ref name="Generation Expansion Planning Considering Investment Dynamic of Market Participants Using Multi-agent System - IEEE Conference Publication 2019">{{cite document | title=Generation Expansion Planning Considering Investment Dynamic of Market Participants Using Multi-agent System - IEEE Conference Publication | date=2019-12-17 | doi=10.1109/SGC.2018.8777904 }}</ref>, [[smartgrids]]<ref name="Distributed Multi-Agent System-Based Load Frequency Control for Multi-Area Power System in Smart Grid - IEEE Journals & Magazine 2019">{{cite document | title=Distributed Multi-Agent System-Based Load Frequency Control for Multi-Area Power System in Smart Grid - IEEE Journals & Magazine | date=2019-12-17 | doi=10.1109/TIE.2017.2668983 }}</ref> and [[Geographic information system|GIS]].
+
一些贯彻共同标准的框架已经出现了（例如 FIPA 和 OMG MASIF <ref>{{Cite web|url=https://www.omg.org/cgi-bin/doc?orbos/97-10-05|title=OMG Document – orbos/97-10-05 (Update of Revised MAF Submission)|website=www.omg.org|access-date=2019-02-19}}</ref>标准）。JADE等框架可以为标准化的多智能体系统开发节省时间和精力。<ref>{{cite journal |first1=Salman |last1=Ahmed  |first2=Mohd N. |last2=Karsiti |first3=Herman |last3=Agustiawan |title=A development framework for collaborative robots using feedback control|year=2007|citeseerx=10.1.1.98.879  }}</ref>
  
−
Other applications include transportation, logistics, graphics, manufacturing, power system, smartgrids and GIS.
 
  
−
其它应用包括运输、物流、制图、制造、电力系统、智能电网和地理信息系统。
+
不过目前，FIPA 或 OMG 还没有主动维护任何标准。在工业领域中进一步开发软件智能体的努力主要来自 IEEE 工业电子协会的工业智能体技术委员会。<ref>{{Cite web|url=https://tcia.ieee-ies.org/|title=IEEE IES Technical Committee on Industrial Agents (TC-IA)|website=tcia.ieee-ies.org|access-date=2019-02-19}}</ref>
  
  
  
−
Also, [[Distributed_artificial_intelligence#Agents_and_Multi-agent_systems|Multi-agent Systems Artificial Intelligence]] (MAAI) are used for simulating societies, the purpose thereof being helpful in the fields of climate, energy, epidemiology, conflict management, child abuse, ...<ref>[https://www.newscientist.com/article/mg24332500-800-ai-can-predict-your-future-behaviour-with-powerful-new-simulations AI can predict your future behaviour with powerful new simulations]</ref>. Some organisations working on using multi-agent system models include Center for Modelling Social Systems, Centre for Research in Social Simulation, Centre for Policy Modelling, Society for Modelling and Simulation International.<ref>[https://www.newscientist.com/article/mg24332500-800-ai-can-predict-your-future-behaviour-with-powerful-new-simulations AI can predict your future behaviour with powerful new simulations]</ref>
+
== 应用 ==
  
−
Also, Multi-agent Systems Artificial Intelligence (MAAI) are used for simulating societies, the purpose thereof being helpful in the fields of climate, energy, epidemiology, conflict management, child abuse, .... Some organisations working on using multi-agent system models include Center for Modelling Social Systems, Centre for Research in Social Simulation, Centre for Policy Modelling, Society for Modelling and Simulation International.
+
除了学术研究之外，多智能体系统在工业中也有应用。<ref>{{Cite book|title=Industrial agents : emerging applications of software agents in industry|others=Leitão, Paulo,, Karnouskos, Stamatis|isbn=978-0128003411|location=Amsterdam, Netherlands|oclc=905853947|last1 = Leitão|first1 = Paulo|last2 = Karnouskos|first2 = Stamatis|date = 2015-03-26}}</ref>它在现实世界中不仅被用于电脑游戏等图形应用程序，还已经延伸到了电影中。<ref>{{cite web |publisher=[[Massive (software)|MASSIVE]] |url=http://www.massivesoftware.com/film.html |title=Film showcase|accessdate=28 April 2012}}</ref>它在网络和移动技术中实现自动和动态的负载平衡、高可扩展性和自愈网络的作用广为流传。除此之外，多智能体系统还被用于协同防御体系中。
  
−
此外，'''多主体系统人工智能''' '''MAAI''' 被用于模拟社会，以助力气候，能源，流行病学，冲突管理，儿童虐待等方面的工作。一些致力于使用多主体系统模型的组织包括'''社会系统建模中心 Center for Modelling Social Systems'''、'''社会模拟研究中心 Centre for Research in Social Simulation'''、'''政策建模中心 Centre for Policy Modelling'''以及'''国际建模与模拟学会 Society for Modelling and Simulation International'''。
 
  
 +
其它应用<ref>{{Cite journal|last=Leitao|first=Paulo|last2=Karnouskos|first2=Stamatis|last3=Ribeiro|first3=Luis|last4=Lee|first4=Jay|last5=Strasser|first5=Thomas|last6=Colombo|first6=Armando W.|date=2016|title=Smart Agents in Industrial Cyber–Physical Systems|journal=Proceedings of the IEEE|volume=104|issue=5|pages=1086–1101|doi=10.1109/JPROC.2016.2521931|issn=0018-9219|url=http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-128744}}</ref>包括运输、<ref name="surtrac2012b">Xiao-Feng Xie, S. Smith, G. Barlow. [http://www.wiomax.com/team/xie/paper/ICAPS12.pdf Schedule-driven coordination for real-time traffic network control]. International Conference on Automated Planning and Scheduling (ICAPS), São Paulo, Brazil, 2012: 323–331.</ref> 物流、<ref name="compare">{{Cite journal | last1 = Máhr | first1 = T. S. | last2 = Srour | first2 = J. | last3 = De Weerdt | first3 = M. | last4 = Zuidwijk | first4 = R. | title = Can agents measure up? A comparative study of an agent-based and on-line optimization approach for a drayage problem with uncertainty | doi = 10.1016/j.trc.2009.04.018 | journal = Transportation Research Part C: Emerging Technologies | volume = 18 | pages = 99–119 | year = 2010 | pmid =  | pmc = | citeseerx = 10.1.1.153.770 }}</ref>制图、制造、电力系统、<ref name="Generation Expansion Planning Considering Investment Dynamic of Market Participants Using Multi-agent System - IEEE Conference Publication 2019">{{cite document | title=Generation Expansion Planning Considering Investment Dynamic of Market Participants Using Multi-agent System - IEEE Conference Publication | date=2019-12-17 | doi=10.1109/SGC.2018.8777904 }}</ref>智能电网<ref name="Distributed Multi-Agent System-Based Load Frequency Control for Multi-Area Power System in Smart Grid - IEEE Journals & Magazine 2019">{{cite document | title=Distributed Multi-Agent System-Based Load Frequency Control for Multi-Area Power System in Smart Grid - IEEE Journals & Magazine | date=2019-12-17 | doi=10.1109/TIE.2017.2668983 }}</ref>和地理信息系统。
  
−
== See also 参见 ==
 
  
 +
此外，'''[[多智能体系统人工智能 Multi-agent Systems Artificial Intelligence]]'''（MAAI） 被用于模拟社会，以助力气候，能源，流行病学，冲突管理，儿童虐待等方面的工作。<ref>[https://www.newscientist.com/article/mg24332500-800-ai-can-predict-your-future-behaviour-with-powerful-new-simulations AI can predict your future behaviour with powerful new simulations]</ref>一些致力于使用多智能体系统模型的组织包括'''社会系统建模中心 Center for Modelling Social Systems'''、'''社会模拟研究中心 Centre for Research in Social Simulation'''、'''政策建模中心 Centre for Policy Modelling'''以及'''国际建模与模拟学会 Society for Modelling and Simulation International'''。<ref>[https://www.newscientist.com/article/mg24332500-800-ai-can-predict-your-future-behaviour-with-powerful-new-simulations AI can predict your future behaviour with powerful new simulations]</ref>
  
  
 +
== 参见 ==
 
{{div col}}
 
{{div col}}
  
−
* [[Comparison of agent-based modeling software]] 基于主体的建模软件对比
+
* [[主体建模软件的对比 ]]
  
−
* [[Agent-based computational economics]] (ACE) 基于主体的计算经济学 (ACE)
+
* [[基于主体的计算经济学 Agent-based computational economics]] (ACE)  
  
−
* [[Artificial brain]] 人工大脑
+
* [[人工大脑 Artificial brain]]
  
−
* [[Artificial intelligence]] 人工智能
+
* [[人工智能 Artificial intelligence]]  
  
−
* [[Artificial life]] 人工生命
+
* [[人工生命 Artificial life]]  
  
−
* [[Artificial life framework]] 人工生命框架
+
* [[人工生命框架 Artificial life framework]]  
  
−
* [[AI mayor]] AI市长
+
* [[AI市长 AI mayor]]
  
−
* [[Black box]] 黑箱
+
* [[黑箱 Black box]]  
  
−
* [[Blackboard system]] 黑板系统
+
* [[黑板系统 Blackboard system]]  
  
−
* [[Complex systems]] 复杂系统
+
* [[复杂系统 Complex systems]]  
  
−
* [[Discrete event simulation]] 离散事件仿真
+
* [[离散事件仿真 Discrete event simulation]]  
  
−
* [[Distributed artificial intelligence]] 分布式人工智能
+
* [[分布式人工智能 Distributed artificial intelligence]]  
  
−
* [[Emergence]] 涌现
+
* [[涌现 Emergence]]  
  
−
* [[Evolutionary computation]] 进化计算
+
* [[进化计算 Evolutionary computation]]  
  
−
* [[Game theory]] 博弈论
+
* [[博弈论 Game theory]]  
  
−
* [[Human-based genetic algorithm]] 基于人工的遗传算法
+
* [[基于人工的遗传算法 Human-based genetic algorithm]]  
  
−
* [[Knowledge Query and Manipulation Language]] (KQML) 知识查询操作语言
+
* [[知识查询操作语言 Knowledge Query and Manipulation Language]] (KQML)  
  
−
* [[Microbial intelligence]] 微生物智能
+
* [[微生物智能 Microbial intelligence]]  
  
−
* [[Multi-agent planning]] 多主体规划
+
* [[多主体规划 Multi-agent planning]]  
  
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* [[Pattern-oriented modeling]] 以模式为导向的建模
+
* [[以模式为导向的建模 Pattern-oriented modeling]]  
  
−
* [[PlatBox Project]] PlatBox 项目
+
* [[PlatBox 项目 PlatBox Project]]  
  
−
* [[Reinforcement learning]] 强化学习
+
* [[强化学习 Reinforcement learning]]  
  
−
* [[Scientific community metaphor]] 科学社群隐喻
+
* [[科学社群隐喻 Scientific community metaphor]]  
  
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* [[Self-reconfiguring modular robot]] 自组织模块化机器人
+
* [[自组织模块化机器人 Self-reconfiguring modular robot]]  
  
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* [[Simulated reality]] 模拟现实
+
* [[模拟现实 Simulated reality]]  
  
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* [[Social simulation]] 社会仿真
+
* [[社会仿真Social simulation]]  
  
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* [[Software agent]] 软件主体
+
* [[软件智能体 Software agent]]  
  
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* [[Swarm intelligence]] 群体智能
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* [[群体智能 Swarm intelligence]]  
  
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* [[Swarm robotics]] 群体机器人
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* [[群体机器人 Swarm robotics]]
  
 
{{div col end}}
 
{{div col end}}
  
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== 参考文献 ==
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== References 参考文献 ==
 
−
 
 
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{{reflist|2}}
 
{{reflist|2}}
  
 +
== 进一步阅读 ==
  
  
−
== Further Reading 延伸阅读 ==
 
  
 +
* {{cite book |first=Michael |last=Wooldridge |title=An Introduction to MultiAgent Systems |publisher=John Wiley & Sons |year=2002 |pages=366 |isbn=978-0-471-49691-5}}
  
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+
* {{cite book |first1=Yoav |last1=Shoham |first2=Kevin |last2=Leyton-Brown |url=http://www.masfoundations.org|title=Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations |publisher=Cambridge University Press |year=2008 |pages=496 |isbn=978-0-521-89943-7}}
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* {{cite book |first=Michael |last=Wooldridge |title=An Introduction to MultiAgent Systems |publisher=[[John Wiley & Sons]] |year=2002 |pages=366 |isbn=978-0-471-49691-5}}
 
−
 
 
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* {{cite book |first1=Yoav |last1=Shoham |first2=Kevin |last2=Leyton-Brown |url=http://www.masfoundations.org|title=Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations |publisher=[[Cambridge University Press]] |year=2008 |pages=496 |isbn=978-0-521-89943-7}}
 
  
 
* {{cite journal |last1=Mamadou |first1=Tadiou Koné |last2=Shimazu |first2=A. |last3=Nakajima |first3=T. |title=The State of the Art in Agent Communication Languages (ACL) |journal=Knowledge and Information Systems |volume= 2 |issue=2 |pages=1–26 |date=August 2000|url=https://www.researchgate.net/publication/215705246}}
 
* {{cite journal |last1=Mamadou |first1=Tadiou Koné |last2=Shimazu |first2=A. |last3=Nakajima |first3=T. |title=The State of the Art in Agent Communication Languages (ACL) |journal=Knowledge and Information Systems |volume= 2 |issue=2 |pages=1–26 |date=August 2000|url=https://www.researchgate.net/publication/215705246}}
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* {{cite book |first=Jacques |last=Ferber |title=Multi-Agent Systems: An Introduction to Artificial Intelligence |publisher=Addison-Wesley |year=1999 |isbn=978-0-201-36048-6}}
 
* {{cite book |first=Jacques |last=Ferber |title=Multi-Agent Systems: An Introduction to Artificial Intelligence |publisher=Addison-Wesley |year=1999 |isbn=978-0-201-36048-6}}
  
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* {{cite book |last=Sun |first=Ron |authorlink=Ron Sun |year=2006 |title=Cognition and Multi-Agent Interaction |publisher=[[Cambridge University Press]] |url=http://www.cambridge.org/uk/catalogue/catalogue.asp?isbn=0-521-83964-5 |isbn=978-0-521-83964-8}}
+
* {{cite book |last=Sun |first=Ron |year=2006 |title=Cognition and Multi-Agent Interaction |publisher=Cambridge University Press|url=http://www.cambridge.org/uk/catalogue/catalogue.asp?isbn=0-521-83964-5 |isbn=978-0-521-83964-8}}
  
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* {{cite book |first1=David |last1=Keil |first2=Dina |last2=Goldin |url=https://archive.org/details/environmentsform0000e4ma/page/68 |title=Indirect Interaction in Environments for Multiagent Systems |journal=Environments for Multiagent Systems II |volume=3830 |pages=[https://archive.org/details/environmentsform0000e4ma/page/68 68–87] |editor1-first=Danny |editor1-last=Weyns |editor2-first=Van |editor2-last=Parunak |editor3-first=Fabien |editor3-last=Michel |series=LNCS 3830 |publisher=[[Springer Science+Business Media|Springer]] |year=2006 |doi=10.1007/11678809_5 |isbn=978-3-540-32614-4 }}
+
* {{cite book |first1=David |last1=Keil |first2=Dina |last2=Goldin |url=https://archive.org/details/environmentsform0000e4ma/page/68 |title=Indirect Interaction in Environments for Multiagent Systems |journal=Environments for Multiagent Systems II |volume=3830 |pages=[https://archive.org/details/environmentsform0000e4ma/page/68 68–87] |editor1-first=Danny |editor1-last=Weyns |editor2-first=Van |editor2-last=Parunak |editor3-first=Fabien |editor3-last=Michel |series=LNCS 3830 |publisher=Springer Science+Business Media|year=2006 |doi=10.1007/11678809_5 }}
  
 
* ''[https://web.archive.org/web/20090114063602/http://www.whitestein.com/series Whitestein Series in Software Agent Technologies and Autonomic Computing]'', published by Springer Science+Business Media Group
 
* ''[https://web.archive.org/web/20090114063602/http://www.whitestein.com/series Whitestein Series in Software Agent Technologies and Autonomic Computing]'', published by Springer Science+Business Media Group
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* {{Russell Norvig 2003}}
 
* {{Russell Norvig 2003}}
  
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* {{cite book |first=Maria |last=Fasli |title=Agent-technology for E-commerce |publisher=[[John Wiley & Sons]] |year=2007 |pages=480 |isbn=978-0-470-03030-1}}
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* {{cite book |first=Maria |last=Fasli |title=Agent-technology for E-commerce |publisher=John Wiley & Sons|year=2007 |pages=480}}
  
 
* Cao, Longbing, Gorodetsky, Vladimir, Mitkas, Pericles A. (2009). [http://www2.computer.org/portal/web/csdl/doi/10.1109/MIS.2009.45 Agent Mining: The Synergy of Agents and Data Mining], IEEE Intelligent Systems, vol. 24, no. 3, 64-72.
 
* Cao, Longbing, Gorodetsky, Vladimir, Mitkas, Pericles A. (2009). [http://www2.computer.org/portal/web/csdl/doi/10.1109/MIS.2009.45 Agent Mining: The Synergy of Agents and Data Mining], IEEE Intelligent Systems, vol. 24, no. 3, 64-72.
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== External links 外部链接 ==
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== 其他链接 ==
  
 
* [http://www.b-squared.org.ua/static/research/example/BZ.html Random Agent-Based Simulations by Borys Biletskyy]{{dead link|date=December 2017 |bot=InternetArchiveBot |fix-attempted=yes }} – Random agent-base simulations for multi-robot system and Belousov-Zhabotinsky reaction. Java applets available.
 
* [http://www.b-squared.org.ua/static/research/example/BZ.html Random Agent-Based Simulations by Borys Biletskyy]{{dead link|date=December 2017 |bot=InternetArchiveBot |fix-attempted=yes }} – Random agent-base simulations for multi-robot system and Belousov-Zhabotinsky reaction. Java applets available.
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本中文词条由[[用户:Jie|Jie]]翻译，[[用户:Qige96|Ricky]]审校，[[用户:薄荷|薄荷]]欢迎在讨论页面留言。
 
本中文词条由[[用户:Jie|Jie]]翻译，[[用户:Qige96|Ricky]]审校，[[用户:薄荷|薄荷]]欢迎在讨论页面留言。

2020年12月3日 (四) 18:43的版本

简单反应体
学习智能体


多智能体系统 multi-agent system（MAS），也称多主体系统或自组织系统 self-organized system ，是一种由多个相互作用的智能体组成的计算系统。多智能体系统可以解决一些单个多智能体或单一性系统难以解决的问题。[1]其智能主要体现在条理性、功能性、程序性的行为方式，以及搜索算法和强化学习[2]上。


尽管多智能体系统和基于主体的模型 Agent-based model（ABM） 有着很多的重叠，但是它们并不总是相同的。基于主体的模型目标在于解释那些遵循简单规则、可能并不是很“智能”的主体表现出的集群行为，一般被用于天然系统的研究中，而非实践和工程中解决具体的问题。因此“基于主体的模型”这个词更多地用在科学研究中，而“多智能体系统”则更多地用于工程和技术。[3]有关多智能体系统的研究可能会对在线交易、[4] 灾害应急、[5][6] 社会结构建模[7] 等领域有着良好的应用价值。


概念

多智能体系统由智能体 Agent及其所处的环境组成。典型的多智能体系统研究的是软件智能体 software agent。然而，多智能体系统中的智能体也可以是机器人、人类或人类团体。多智能体系统还可以包含人类和其它智能体的组合。


不同智能体可以按照从简单到复杂的顺序分为如下几个类型:


  • 被动智能体，[8]或“无目标智能体”（比如障碍物、苹果或在简单仿真中的钥匙）
  • 具有简单目标的主动智能体[8]（比如鸟群中的鸟、“掠食者-猎物模型 Lotka–Volterra”中的狼和羊）
  • 认知智能体（可以进行复杂的计算）


智能体所处的环境可以分为:


  • 虚拟的 Virtual
  • 离散的 Discrete
  • 连续的 Continuous


我们也可以从如下角度考察智能体所处的环境，如：可知性 accessibility（是否可以搜集到关于环境的完整信息）、确定性 determinism（行为造成的影响是否是确定的）、动态性 dynamics（同一时刻有多少智能体影响环境）、离散性 discreteness（智能体可以采取的行动是否是有限的）、时序性 episodicity（智能体在某一特定时段内的行为是否影响其它时段）[9]、维度性 dimensionality（环境是否具有鲜明的空间特征，以及智能体在做决策时是否考虑空间因素）。智能体的行为通常受到中间件 Middleware 的调控。[10]中间件使得人们可以管理可用资源、调控智能体，极大地方便了人们对于多智能体系统进行抽象设计。[11]


特点

多智能体系统中的智能体有如下几个重要特点：[12]


  • 自主性：智能体至少是部分独立的，可以自我感知、自主行动。
  • 局部视野：没有一个智能体可以掌握系统的全貌，或者由于系统十分复杂，没有一个智能体可以利用全局性的知识。
  • 无中心性：不指定任何一个控制性的智能体（或者说多智能体系统不会退化为一个单一化系统）。[13]


自组织与自引导

即使所有单个智能体的策略都很简单，多智能体系统也可以表现出自组织、自引导等控制范式以及相关的复杂行为。当智能体之间可以在系统通信规范的约束下使用一些约定的语言来共享信息时，这种方法可能带来智能体间的共赢。例如知识查询操作语言 Knowledge Query Manipulation Language（KQML）和智能体通信语言 Agent Communication Language（ACL）是这类语言中的两个典型例子。


系统模式

很多多智能体系统是在计算机仿真中实现的，按照离散的“时间步长 time steps”逐步地演化。多智能体系统中的组分通常使用加权请求矩阵和加权响应矩阵进行通信。下面是几个加权请求矩阵的例子： 

速度——非常重要: 最小每小时45英里
路径长度——中等重要：最大60，预期最大40
最大重量——不重要
协议优先级——常规


加权响应矩阵的例子： 

最小速度：天气晴朗时50
路径长度: 晴天25，雨天46
协议优先级——常规
注意，救护车具有更高的优先级，你必须等待


“请求-响应-协议 challenge-response-contrac”方案在多智能体系统当中很常见，在这个方案中：

  • 首先，一个智能体将一个“谁可以...？”形式的请求分发出去。
  • 其次，与该请求相关的智能体会响应道：“我可以，前提是...”。
  • 最后，双方一般会通过几次很短的沟通步骤达成一个协议。


同时，这个过程也会考虑其它的智能体、考虑协议的改进以及算法对智能体的约束。


多智能体系统经常使用的另一个通信模式是信息素 Pheromone。在此模式下，各智能体为附近的其它智能体留下信息素来传递信息。随着时间的推移，这些信息素可能“蒸发”或累积，也就是其权重的下降或上升。


性质

多智能体系统常常可以在没有干预的情况下为它们的问题找到最好的解决方案，这与物理现象有很高的相似性。比如在能量最小化的例子中，物理智能体倾向于在物理条件约束的世界中达到可能的最低能量。


系统还具有防止故障传播、自恢复和容错的特点，这主要是由于组件的冗余性。


研究

多智能体系统的研究是与单智能体和多智能体系统中复杂的人工智能问题解决和控制架构的开发和分析有关。[14]主要的研究主题包括：

  • 以智能体为导向的软件工程
  • 信念，欲望和动机 beliefs, desires, and intentions（BDI）
  • 合作和协调
  • 分布式约束优化 distributed constraint optimization（DCOPs）
  • 组织
  • 沟通
  • 谈判
  • 分布式问题求解
  • 多智能体学习
  • 智能体挖掘
  • 科学化社群（比如生物的群体、语言的进化和经济体）[15][16]
  • 依赖性和容错
  • 机器人学，[17]多机器人系统 (MRS) 和机器人集群


框架

一些贯彻共同标准的框架已经出现了（例如 FIPA 和 OMG MASIF [18]标准）。JADE等框架可以为标准化的多智能体系统开发节省时间和精力。[19]


不过目前，FIPA 或 OMG 还没有主动维护任何标准。在工业领域中进一步开发软件智能体的努力主要来自 IEEE 工业电子协会的工业智能体技术委员会。[20]


应用

除了学术研究之外，多智能体系统在工业中也有应用。[21]它在现实世界中不仅被用于电脑游戏等图形应用程序，还已经延伸到了电影中。[22]它在网络和移动技术中实现自动和动态的负载平衡、高可扩展性和自愈网络的作用广为流传。除此之外，多智能体系统还被用于协同防御体系中。


其它应用[23]包括运输、[24] 物流、[25]制图、制造、电力系统、[26]智能电网[27]和地理信息系统。


此外，多智能体系统人工智能 Multi-agent Systems Artificial Intelligence（MAAI） 被用于模拟社会，以助力气候，能源，流行病学，冲突管理，儿童虐待等方面的工作。[28]一些致力于使用多智能体系统模型的组织包括社会系统建模中心 Center for Modelling Social Systems、社会模拟研究中心 Centre for Research in Social Simulation、政策建模中心 Centre for Policy Modelling以及国际建模与模拟学会 Society for Modelling and Simulation International。[29]


参见

参考文献

  1. ↑ 模板:Cite document
  2. ↑ "Multi Agent Systems - an overview". ScienceDirect Topics. 2016-01-01. Retrieved 2020-01-23.
  3. ↑ Niazi, Muaz; Hussain, Amir (2011). "Agent-based Computing from Multi-agent Systems to Agent-Based Models: A Visual Survey" (PDF). Scientometrics. 89 (2): 479–499. arXiv:1708.05872. doi:10.1007/s11192-011-0468-9.
  4. ↑ Rogers, Alex; David, E.; Schiff, J.; Jennings, N.R. (2007). "The Effects of Proxy Bidding and Minimum Bid Increments within eBay Auctions". ACM Transactions on the Web. 1 (2): 9–es. CiteSeerX 10.1.1.65.4539. doi:10.1145/1255438.1255441.
  5. ↑ Schurr, Nathan; Marecki, Janusz; Tambe, Milind; Scerri, Paul; Kasinadhuni, Nikhil; Lewis, J.P. (2005). "The Future of Disaster Response: Humans Working with Multiagent Teams using DEFACTO" (PDF). {{cite journal}}: Cite journal requires |journal= (help)
  6. ↑ Genc, Zulkuf; et al. (2013). "Agent-based information infrastructure for disaster management" (PDF). Intelligent Systems for Crisis Management. Lecture Notes in Geoinformation and Cartography: 349–355. doi:10.1007/_26.
  7. ↑ Sun, Ron; Naveh, Isaac. "Simulating Organizational Decision-Making Using a Cognitively Realistic Agent Model". Journal of Artificial Societies and Social Simulation.
  8. ↑ 8.0 8.1 Kubera, Yoann; Mathieu, Philippe; Picault, Sébastien (2010), "Everything can be Agent!" (PDF), Proceedings of the Ninth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'2010): 1547–1548
  9. ↑ 模板:Russell Norvig 2003
  10. ↑ Salamon, Tomas (2011). Design of Agent-Based Models. Repin: Bruckner Publishing. p. 22. http://www.designofagentbasedmodels.info/. 
  11. ↑ Weyns, Danny; Omicini, Amdrea; Odell, James (2007). "Environment as a first-class abstraction in multiagent systems" (PDF). Autonomous Agents and Multi-Agent Systems. 14 (1): 5–30. CiteSeerX 10.1.1.154.4480. doi:10.1007/s10458-006-0012-0. Retrieved 2013-05-31.https://en.wikipedia.org/wiki/Defekte_Weblinks?dwl={{{url}}} Seite nicht mehr abrufbar], Suche in Webarchiven: Kategorie:Wikipedia:Weblink offline (andere Namensräume)[http://timetravel.mementoweb.org/list/2010/Kategorie:Wikipedia:Vorlagenfehler/Vorlage:Toter Link/URL_fehlt
  12. ↑ Wooldridge, Michael (2002). An Introduction to MultiAgent Systems. John Wiley & Sons. pp. 366. ISBN 978-0-471-49691-5. 
  13. ↑ Panait, Liviu; Luke, Sean (2005). "Cooperative Multi-Agent Learning: The State of the Art" (PDF). Autonomous Agents and Multi-Agent Systems. 11 (3): 387–434. CiteSeerX 10.1.1.307.6671. doi:10.1007/s10458-005-2631-2.
  14. ↑ "The Multi-Agent Systems Lab". University of Massachusetts Amherst. Retrieved Oct 16, 2009.
  15. ↑ Cucker, Felipe; Steve Smale (2007). "The Mathematics of Emergence" (PDF). Japanese Journal of Mathematics. 2: 197–227. doi:10.1007/s11537-007-0647-x. Retrieved 2008-06-09.
  16. ↑ Shen, Jackie (Jianhong) (2008). "Cucker–Smale Flocking under Hierarchical Leadership". SIAM J. Appl. Math. 68 (3): 694–719. arXiv:q-bio/0610048. doi:10.1137/060673254. Retrieved 2008-06-09.
  17. ↑ Ahmed, S.; Karsiti, M.N. (2007), "A testbed for control schemes using multi agent nonholonomic robots", 2007 IEEE International Conference on Electro/Information Technology, p. 459, doi:10.1109/EIT.2007.4374547, ISBN 978-1-4244-0940-2
  18. ↑ "OMG Document – orbos/97-10-05 (Update of Revised MAF Submission)". www.omg.org. Retrieved 2019-02-19.
  19. ↑ Ahmed, Salman; Karsiti, Mohd N.; Agustiawan, Herman (2007). "A development framework for collaborative robots using feedback control". CiteSeerX 10.1.1.98.879. {{cite journal}}: Cite journal requires |journal= (help)
  20. ↑ "IEEE IES Technical Committee on Industrial Agents (TC-IA)". tcia.ieee-ies.org. Retrieved 2019-02-19.
  21. ↑ Leitão, Paulo; Karnouskos, Stamatis (2015-03-26). Industrial agents : emerging applications of software agents in industry. Leitão, Paulo,, Karnouskos, Stamatis. Amsterdam, Netherlands. ISBN 978-0128003411. OCLC 905853947. 
  22. ↑ "Film showcase". MASSIVE. Retrieved 28 April 2012.
  23. ↑ Leitao, Paulo; Karnouskos, Stamatis; Ribeiro, Luis; Lee, Jay; Strasser, Thomas; Colombo, Armando W. (2016). "Smart Agents in Industrial Cyber–Physical Systems". Proceedings of the IEEE. 104 (5): 1086–1101. doi:10.1109/JPROC.2016.2521931. ISSN 0018-9219.
  24. ↑ Xiao-Feng Xie, S. Smith, G. Barlow. Schedule-driven coordination for real-time traffic network control. International Conference on Automated Planning and Scheduling (ICAPS), São Paulo, Brazil, 2012: 323–331.
  25. ↑ Máhr, T. S.; Srour, J.; De Weerdt, M.; Zuidwijk, R. (2010). "Can agents measure up? A comparative study of an agent-based and on-line optimization approach for a drayage problem with uncertainty". Transportation Research Part C: Emerging Technologies. 18: 99–119. CiteSeerX 10.1.1.153.770. doi:10.1016/j.trc.2009.04.018.
  26. ↑ 模板:Cite document
  27. ↑ 模板:Cite document
  28. ↑ AI can predict your future behaviour with powerful new simulations
  29. ↑ AI can predict your future behaviour with powerful new simulations

进一步阅读

  • Wooldridge, Michael (2002). An Introduction to MultiAgent Systems. John Wiley & Sons. pp. 366. ISBN 978-0-471-49691-5. 
  • Weiss, Gerhard, ed. (1999). Multiagent Systems, A Modern Approach to Distributed Artificial Intelligence. MIT Press. ISBN 978-0-262-23203-6. 
  • Ferber, Jacques (1999). Multi-Agent Systems: An Introduction to Artificial Intelligence. Addison-Wesley. ISBN 978-0-201-36048-6. 
  • Fasli, Maria (2007). Agent-technology for E-commerce. John Wiley & Sons. pp. 480. 


其他链接

  • CORMAS (COmmon Resources Multi-Agent System) An open-source framework for Multi-Agent Systems based on Smalltalk. Spatialized, it focuses on issues related to natural resource management and negotiation between stakeholders.
  • JaCaMo MAS Platform – An open-source platform for Multi-Agent Systems based on Jason, CArtAgO, and Moise.
  • HarTech Technologies – HarTech Technologies developed a dedicated Distributed Multi Agent System Framework used in both simulation and large scale command and control system. This unique framework called the Generic Blackboard (GBB) provides a development framework for such systems which is domain independent. Distributed Multi Agent Framework.
  • MaDKit is a lightweight open source Java library for designing and simulating Multi-Agent Systems. MaDKit is built upon the AGR (Agent/Group/Role) organizational model: agents are situated in groups and play roles, MAS are conceived as artificial societies.

Category:Multi-robot systems

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