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| − | 此词条暂由彩云小译翻译，翻译字数共736，未经人工整理和审校，带来阅读不便，请见谅。
| + | #重定向 [[多主体系统]] |
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| − | [[Image:IntelligentAgent-SimpleReflex.png|thumb|right|Simple reflex agent]]
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| − | Simple reflex agent
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| − | 单纯反射剂
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| − | [[Image:IntelligentAgent-Learning.png|thumb|right|Learning agent]]
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| − | Learning agent
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| − | 学习代理
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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 | s2cid=10798495 }}</ref> Intelligence may include [[Scientific method|method]]ic, [[Function (computer science)|functional]], [[Algorithm|procedural]] approaches, [[algorithm]]ic [[search algorithm|search]] or [[reinforcement learning]].<ref>{{cite journal|hdl=1874/20827|title=Multi-agent reinforcement learning for traffic light control}}</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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| − | 多智能体系统系统是一个由多个相互作用的智能代理组成的计算机化系统。多智能体系统可以解决单个智能体或单层系统难以解决或不可能解决的问题。智能可能包括有条理的、功能性的、程序性的方法、算法搜索或者强化学习。
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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 |s2cid=17934527 }}</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 |s2cid=207163424 }}</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=10.1007/978-3-642-33218-0_26 |isbn=978-3-642-33217-3 |series=Lecture Notes in Geoinformation and Cartography }}</ref>, target surveillance <ref>{{cite journal |last1=Hu |first1=Junyan |last2=Bhowmick |first2=Parijat|last3=Lanzon |first3=Alexander |title=Distributed Adaptive Time-Varying Group Formation Tracking for Multiagent Systems With Multiple Leaders on Directed Graphs |journal=IEEE Transactions on Control of Network Systems |date=2020 |volume=7 |pages=140–150 |doi=10.1109/TCNS.2019.2913619 |s2cid=149609966 |doi-access=free }}</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, target surveillance and social structure modelling.
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| − | 尽管有相当多的重叠部分，多智能体系统并不总是和个体为本模型一样。反弹道模型的目标是寻找解释性的洞察力，以了解遵守简单规则(通常在自然系统中)的行为体(这些行为体不一定是“智能的”)的集体行为，而不是解决具体的实际或工程问题。作业成本管理的术语在科学领域和工程技术领域越来越多地使用。多智能体系统研究可能提供适当方法的应用包括在线交易、灾害响应、目标监视和社会结构建模。
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| − | == Concept ==
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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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| − | 多智能体系统由智能体及其环境组成。典型的多智能体系统研究是指软件智能体。然而，多智能体系统空间站中的机器人、人类或者人类团队同样有可能。一个多智能体系统可能包含联合的人-特工团队。
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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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| − | 代理可以分为从简单到复杂的类型。类别包括:
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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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| − | * 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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| − | * Cognitive agents (complex calculations)
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| − | Agent environments can be divided into:
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| − | * Virtual
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| − | The agents in a multi-agent system have several important characteristics:
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| − | 多智能体系统中的代理人有几个重要特征:
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| − | * Discrete
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| − | * Continuous
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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 = 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 |s2cid=13347050 }}{{dead link|date=December 2017 |bot=InternetArchiveBot |fix-attempted=yes }}</ref>
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| − | === Characteristics ===
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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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| − | 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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| − | 多智能体系统可以表现出自我组织、自我指导和其他控制范式以及相关的复杂行为，即使其所有智能体的个体策略都很简单。当代理可以共享知识使用任何一种约定的语言，在系统的通信协议的约束，该方法可能导致一个共同的改进。示例语言是知识查询操作语言(KQML)或代理通信语言(ACL)。
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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 |s2cid=19706 }}</ref>
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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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| − | 许多多智能体系统是在计算机仿真中实现的，通过离散的“时间步长”逐步实现系统。MAS 组件通常使用加权的请求矩阵进行通信，例如。
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| − | Speed-VERY_IMPORTANT: min=45 mph,
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| − | 速度-非常重要: 分钟 = 每小时45英里,
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| − | === Self-organisation and self-direction ===
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| − | Path length-MEDIUM_IMPORTANCE: max=60 expectedMax=40,
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| − | 路径长度-medium _ importance: max = 60 expectedMax = 40,
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| − | Max-Weight-UNIMPORTANT
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| − | 最大重量-不重要
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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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| − | Contract Priority-REGULAR
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| − | 合约优先权-定期
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| − | and a weighted response matrix, e.g.
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| − | 和一个加权响应矩阵，例如。
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| − | === System paradigms ===
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| − | Speed-min:50 but only if weather sunny,
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| − | 速度分钟: 50但只有当天气晴朗时,
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| − | Path length:25 for sunny / 46 for rainy
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| − | 路径长度: 晴天25英尺，雨天46英尺
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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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| − | Contract Priority-REGULAR
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| − | 合约优先权-定期
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| − | Speed-VERY_IMPORTANT: min=45 mph,
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| − | note – ambulance will override this priority and you'll have to wait
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| − | 注意，救护车会优先处理这件事，你必须等待
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| − | Path length-MEDIUM_IMPORTANCE: max=60 expectedMax=40,
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| − | Max-Weight-UNIMPORTANT
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| − | A challenge-response-contract scheme is common in MAS systems, where
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| − | 在多智能体系统中，挑战-响应-契约机制是一种常见的机制
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| − | Contract Priority-REGULAR
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| − | and a weighted response matrix, e.g.
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| − | Speed-min:50 but only if weather sunny,
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| − | Path length:25 for sunny / 46 for rainy
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| − | also considering other components, evolving "contracts" and the restriction sets of the component algorithms.
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| − | 同时还考虑了其他成分，进化的“契约”和成分算法的约束集。
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| − | Contract Priority-REGULAR
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| − | note – ambulance will override this priority and you'll have to wait
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| − | 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).
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| − | MAS 常用的另一个范例是“信息素” ，其中组件为其他邻近组件留下信息。这些信息素可能蒸发/浓缩随着时间的推移，这是他们的价值可能减少(或增加)。
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| − | A challenge-response-contract scheme is common in MAS systems, where
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| − | * First a '''"'''Who can?'''"''' question is distributed.
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| − | * Only the relevant components respond: '''"'''I can, at this price'''"'''.
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| − | 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.
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| − | MAS 倾向于在没有干预的情况下为他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，例如能量最小化，物理物体倾向于在物理约束的世界中达到可能的最低能量。例如: 许多早上进入大都市的汽车晚上可以离开同一个大都市。
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| − | * Finally, a contract is set up, usually in several short communication steps between sides,
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| − | also considering other components, evolving "contracts" and the restriction sets of the component algorithms.
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| − | The systems also tend to prevent propagation of faults, self-recover and be fault tolerant, mainly due to the redundancy of components.
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| − | 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。
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| − | 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).
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| − | === Properties ===
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| − | 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:
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| − | 多智能体系统的研究“涉及到发展和分析复杂的人工智能问题解决和控制体系结构的单个和多个智能体系统。”研究课题包括:
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| − | 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.
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| − | The systems also tend to prevent propagation of faults, self-recover and be fault tolerant, mainly due to the redundancy of components.
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| − | == Research ==
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| − | 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:
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| − | * agent-oriented software engineering
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| − | * beliefs, desires, and intentions ([[BDI software agent|BDI]])
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| − | * [[Consensus dynamics|cooperation and coordination]]
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| − | * [[distributed constraint optimization]] (DCOPs)
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| − | * organization
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| − | * communication
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| − | * negotiation
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| − | * [[cooperative distributed problem solving|distributed problem solving]]
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| − | *[[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>
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| − | 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.
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| − | 已经出现了实现共同标准的框架(如 FIPA 和 OMG MASIF 标准)。这些框架包括。玉，节省时间，有助于 MAS 开发的标准化。
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| − | * [[agent mining]]
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| − | * 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 | s2cid =2637067 | 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| s2cid =14655317 }}
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| − | 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.
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| − | 不过目前，FIPA 或 OMG 还没有主动维护任何标准。在工业环境中进一步开发软件代理的努力是在 IEEE 工业代理技术委员会进行的。
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| − | </ref>
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| − | * dependability and fault-tolerance
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| − | == Applications ==<!-- Although MAS is still strictly a research topic, many graphic computer games today are developed using MAS algorithms and MAS frameworks. -->
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| − | = = 应用程序 = = < ！——尽管 MAS 仍然是一个严格的研究课题，但是今天许多图形化的电脑游戏都是使用 MAS 算法和 MAS 框架开发的。-->
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| − | * 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 |s2cid=2734931 }}</ref> multi-robot systems (MRS), robotic clusters
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| − | 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.
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| − | 多智能体系统不仅在学术研究中得到了应用，而且在工业上也得到了应用。MAS 在现实世界中应用于图形应用程序，如电脑游戏。代理系统已经在电影中使用。它被广泛应用于网络和移动技术中，以实现自动和动态的负载平衡、高可扩展性和自愈网络。它们被用于协调防御系统。
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| − | == Frameworks ==
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| − | Other applications include transportation, logistics, graphics, manufacturing, power system, smartgrids and GIS.
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| − | 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。
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| − | 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>
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| − | 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.
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| − | 此外，多智能体系统人工智能(MAAI)还可用于模拟社会，其目的是在气候、能源、流行病学、冲突管理、虐待儿童、 ..。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、社会模拟研究中心、政策建模中心、国际建模与模拟学会。
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| − | 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>
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| − | == Applications ==<!-- Although MAS is still strictly a research topic, many graphic computer games today are developed using MAS algorithms and MAS frameworks. -->
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| − | 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.
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| − | Other applications<ref>{{Cite journal|last1=Leitao|first1=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|s2cid=579475|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 | s2cid=199058301 }}</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 | s2cid=31816181 }}</ref> and [[Geographic information system|GIS]].
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| − | 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>
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| − | == See also ==
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| − | {{div col}}
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| − | * [[Comparison of agent-based modeling software]]
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| − | * [[Agent-based computational economics]] (ACE)
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| − | * [[Artificial brain]]
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| − | * [[Artificial intelligence]]
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| − | * [[Artificial life]]
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| − | * [[Artificial life framework]]
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| − | * [[AI mayor]]
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| − | * [[Black box]]
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| − | * [[Blackboard system]]
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| − | * [[Complex systems]]
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| − | * [[Discrete event simulation]]
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| − | * [[Distributed artificial intelligence]]
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| − | * [[Emergence]]
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| − | * [[Evolutionary computation]]
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| − | * [[Game theory]]
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| − | * [[Human-based genetic algorithm]]
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| − | * [[Knowledge Query and Manipulation Language]] (KQML)
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| − | * [[Microbial intelligence]]
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| − | * [[Multi-agent planning]]
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| − | * [[Pattern-oriented modeling]]
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| − | * [[PlatBox Project]]
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| − | * [[Reinforcement learning]]
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| − | * [[Scientific community metaphor]]
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| − | * [[Self-reconfiguring modular robot]]
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| − | * [[Simulated reality]]
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| − | * [[Social simulation]]
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| − | * [[Software agent]]
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| − | * [[Swarm intelligence]]
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| − | * [[Swarm robotics]]
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| − | {{div col end}}
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| − | == References ==
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| − | {{reflist|2}}
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| − | == Further reading ==
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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}}
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| − | * {{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 journal |first1=Carl |last1=Hewitt |first2=Jeff |last2=Inman |title=DAI Betwixt and Between: From "Intelligent Agents" to Open Systems Science |journal=IEEE Transactions on Systems, Man, and Cybernetics |volume=21 |issue=6 |pages=1409–1419 |date=Nov–Dec 1991|url=https://pdfs.semanticscholar.org/7840/bbf6b2fceb014cd3e8eeb2bd81529c7b36b5.pdf|doi=10.1109/21.135685 |s2cid=39080989 }}
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| − | * ''[[Autonomous Agents and Multi-Agent Systems|The Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS)]]''
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| − | * {{cite book |editor-first=Gerhard |editor-last=Weiss |title=Multiagent Systems, A Modern Approach to Distributed Artificial Intelligence |publisher=MIT Press |year=1999 |isbn=978-0-262-23203-6}}
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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}}
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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}}
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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 }}
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| − | * ''[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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| − | * {{Cite book | last=Salamon | given=Tomas | year=2011 | title=Design of Agent-Based Models : Developing Computer Simulations for a Better Understanding of Social Processes | publisher=Bruckner Publishing | isbn=978-80-904661-1-1 | url=http://www.designofagentbasedmodels.info/ | ref=harv }}
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| − | Category:Artificial intelligence
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| − | 类别: 人工智能
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| − | * {{Russell Norvig 2003}}
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| − | Category:Multi-robot systems
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| − | 类别: 多机器人系统
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| − | <noinclude>
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| − | <small>This page was moved from [[wikipedia:en:Multi-agent system]]. Its edit history can be viewed at [[多主体模拟/edithistory]]</small></noinclude>
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| − | [[Category:待整理页面]] | |