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第125行： − 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。'''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。+ 第141行：
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第350行： − 其他应用包括运输、物流、制图、制造、电力系统、智能电网和地理信息系统。+ 第407行：
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部分语句翻译调整优化
#重定向 [[多智能体系统]]
#重定向 [[多智能体系统]]
[[Image:IntelligentAgent-SimpleReflex.png|thumb|right|Simple reflex agent 简单反射主体]]
[[Image:IntelligentAgent-SimpleReflex.png|thumb|right|Simple reflex agent 简单反应体]]
Simple reflex agent
Simple reflex agent
【图1 Simple reflex agent 简单反射主体】
【图1 Simple reflex agent 简单反应体】
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.
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.
'''多主体系统''' '''Self-organized System/'Multi-agent System'''，简称'''MAS'''，是一种由多个相互作用的主体组成的计算系统。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。其智能可能体现在条理性、功能性、程序性的行为方式，以及搜索算法和强化学习上。
'''多主体系统''' '''Self-organized System/'Multi-agent System'''，简称'''MAS'''，是一种由多个相互作用的主体组成的计算系统。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。
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.
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.
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，但是他们并不总是相同的。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，而不是在实践和工程解决具体的问题。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。
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。
* 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)
* 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)
* 被动主体，或“无目标的主体”（比如障碍物、苹果或在任何简单仿真中的钥匙）
* 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）
* Active agents<ref name=yoann2010/> with simple goals (like birds in flocking, or wolf–sheep in [[Lotka–Volterra|prey-predator model]])
* Active agents<ref name=yoann2010/> with simple goals (like birds in flocking, or wolf–sheep in [[Lotka–Volterra|prey-predator model]])
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 = 978-80-904661-1-1 | 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>
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>
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.
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.
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).
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).
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。
=== System paradigms 系统模式 ===
=== System paradigms 系统模式 ===
Speed-VERY_IMPORTANT: min=45 mph,
Speed-VERY_IMPORTANT: min=45 mph,
Speed-VERY_IMPORTANT: min=45 mph, （速度——非常重要: 每小时45英里）
Speed-VERY_IMPORTANT: min=45 mph, （速度——非常重要: 最小每小时45英里）
Path length-MEDIUM_IMPORTANCE: max=60 expectedMax=40,
Path length-MEDIUM_IMPORTANCE: max=60 expectedMax=40,
Contract Priority-REGULAR
Contract Priority-REGULAR
Contract Priority-REGULAR, （协议优先性——常规）
Contract Priority-REGULAR, （协议优先级——常规）
and a weighted response matrix, e.g.
and a weighted response matrix, e.g.
Contract Priority-REGULAR
Contract Priority-REGULAR
Contract Priority-REGULAR, （协议优先性——常规）
Contract Priority-REGULAR, （协议优先级——常规）
note – ambulance will override this priority and you'll have to wait
note – ambulance will override this priority and you'll have to wait
note – ambulance will override this priority and you'll have to wait
note – ambulance will override this priority and you'll have to wait
note – ambulance will override this priority and you'll have to wait, （注意，救护车比这个具有更高优先性，你必须等待）
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）
* [[主体挖掘]]
* [[主体挖掘]]
* 科学社群（比如生物的群体、语言的进化和经济体）
* 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
Other applications include transportation, logistics, graphics, manufacturing, power system, smartgrids and GIS.
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。
* [[Pattern-oriented modeling]] 以模式为导向的建模
* [[Pattern-oriented modeling]] 以模式为导向的建模
* [[PlatBox Project]] PlatBox项目
* [[PlatBox Project]] PlatBox 项目
* [[Reinforcement learning]] 强化学习
* [[Reinforcement learning]] 强化学习