多智能体系统

来自集智百科 - 复杂系统|人工智能|复杂科学|复杂网络|自组织
Moonscar（讨论 | 贡献）2020年5月9日 (六) 11:53的版本 （Moved page from wikipedia:en:Multi-agent system (history)）
(差异) ←上一版本 | 最后版本 (差异) | 下一版本→ (差异)
跳到导航 跳到搜索

此词条暂由彩云小译翻译，未经人工整理和审校，带来阅读不便，请见谅。

Simple reflex agent

Simple reflex agent

单纯反射剂


Learning agent

Learning agent

学习代理


A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents[citation needed]. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve.[1] Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning.[2]

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。


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.[3] Applications where multi-agent systems research may deliver an appropriate approach include online trading,[4] disaster response[5][6] and social structure modelling.[7]

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的应用越来越广泛。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、灾害反应和社会结构建模。


Concept

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.

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.

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适用。一个多智能体系统可能包含组合的人-特工团队。


Agents can be divided into types spanning simple to complex. Categories include:

Agents can be divided into types spanning simple to complex. Categories include:

代理可以分为从简单到复杂的类型。类别包括:

  • Passive agents[8] or "agent without goals" (such as obstacle, apple or key in any simple simulation)
  • Cognitive agents (complex calculations)


Agent environments can be divided into:

Agent environments can be divided into:

主体环境可以分为:

  • Virtual
  • Discrete
  • Continuous


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),[9] and dimensionality (whether spatial characteristics are important factors of the environment and the agent considers space in its decision making).[10] 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.[11]

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.

主体环境也可以根据以下属性进行组织: 可达性(是否有可能收集关于环境的完整信息)、确定性(一个行为是否产生一定的影响)、动态性(有多少实体在某一时刻影响环境)、离散性(环境中可能的行为是否是有限的)、情景性(某一时间段内的主体行为是否影响其他时间段)和维度性(空间特征是否是环境的重要因素，主体在做决策时是否考虑空间)。代理操作通常通过适当的中间件进行调解。该中间件为多代理系统提供了一流的设计抽象，提供了管理资源访问和代理协调的方法。


Characteristics

The agents in a multi-agent system have several important characteristics:[12]

The agents in a multi-agent system have several important characteristics:

多智能体系统中的代理人有几个重要特征:


  • Autonomy: agents at least partially independent, self-aware, autonomous
  • Local views: no agent has a full global view, or the system is too complex for an agent to exploit such knowledge
  • Decentralization: no agent is designated as controlling (or the system is effectively reduced to a monolithic system)[13]


Self-organisation and self-direction

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.[citation needed] 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).

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。当代理可以使用任何一种约定的语言共享知识时，在系统通信协议的约束下，这种方法可能导致共同的改进。示例语言是知识查询操作语言(KQML)或代理通信语言(ACL)。


System paradigms

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.

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.

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 组件通常使用加权请求矩阵进行通信，例如。 

 Speed-VERY_IMPORTANT: min=45 mph, 

 Speed-VERY_IMPORTANT: min=45 mph,

速度——非常重要: 每小时45英里, 

 Path length-MEDIUM_IMPORTANCE: max=60 expectedMax=40, 

 Path length-MEDIUM_IMPORTANCE: max=60 expectedMax=40,

路径长度-中等重要性: 最大60预期最大40, 

 Max-Weight-UNIMPORTANT 

 Max-Weight-UNIMPORTANT

最大重量-不重要 

 Contract Priority-REGULAR 

 Contract Priority-REGULAR

合约优先权-定期

and a weighted response matrix, e.g.

and a weighted response matrix, e.g.

和一个加权响应矩阵，例如。 

 Speed-min:50 but only if weather sunny, 

 Speed-min:50 but only if weather sunny,

速度分钟: 50但只有当天气晴朗时, 

 Path length:25 for sunny / 46 for rainy

 Path length:25 for sunny / 46 for rainy

路径长度: 晴天25英尺，雨天46英尺 

 Contract Priority-REGULAR

 Contract Priority-REGULAR

合约优先权-定期 

 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，救护车会优先处理这件事，你必须等待


A challenge-response-contract scheme is common in MAS systems, where

A challenge-response-contract scheme is common in MAS systems, where

挑战-响应-契约方案在 MAS 系统中很常见，其中

  • First a "Who can?" question is distributed.
  • Only the relevant components respond: "I can, at this price".
  • 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.

also considering other components, evolving "contracts" and the restriction sets of the component algorithms.

同时考虑其他组件，进化“契约”和组件算法的限制集。


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).

Mas 通常使用的另一个范例是“信息素” ，其中组件为附近的其他组件留下信息。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)。


Properties

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.

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.

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: 许多早晨进入大都市的汽车晚上可以离开同一个大都市。


The systems also tend to prevent propagation of faults, self-recover and be fault tolerant, mainly due to the redundancy of components.

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。


Research

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."[14] 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:

多智能体系统的研究“关注于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体系结构的开发和分析”研究课题包括:

  • agent-oriented software engineering
  • beliefs, desires, and intentions (BDI)
  • organization
  • communication
  • negotiation
  • scientific communities (e.g., on biological flocking, language evolution, and economics)[16][17]

</ref>

/ 参考

  • dependability and fault-tolerance
  • robotics,[18] multi-robot systems (MRS), robotic clusters


Frameworks

Frameworks have emerged that implement common standards (such as the FIPA and OMG MASIF[19] standards). These frameworks e.g. JADE, save time and aid in the standardization of MAS development.[20]

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.

已经出现了实现共同标准的框架(例如 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。


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.[21]

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。


Applications

Applications

应用程序！ ——尽管 MAS 仍然是一个严格的研究课题，但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。-->

MAS have not only been applied in academic research, but also in industry.[22] MAS are applied in the real world to graphical applications such as computer games. Agent systems have been used in films.[23] 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.

多智能体系统不仅应用于学术研究，而且还应用于工业领域。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，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。


Other applications[24] include transportation,[25] logistics,[26] graphics, manufacturing, power system[27], smartgrids[28] and GIS.

Other applications include transportation, logistics, graphics, manufacturing, power system, smartgrids and GIS.

其他应用包括运输、物流、制图、制造、电力系统、智能电网和 GIS。


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, ...[29]. 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.[30]

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.

此外，多智能体系统人工智能(MAAI)被用于模拟社会，其目的是有助于在气候，能源，流行病学，冲突管理，儿童虐待，..。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。


See also


References

  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:10.1007/978-3-642-33218-0_26. ISBN 978-3-642-33217-3.
  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. ISBN 978-80-904661-1-1. 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. ↑ Albrecht, Stefano; Stone, Peter (2017), "Multiagent Learning: Foundations and Recent Trends. Tutorial", IJCAI-17 conference (PDF)
  16. ↑ 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.
  17. ↑ 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.
  18. ↑ 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
  19. ↑ "OMG Document – orbos/97-10-05 (Update of Revised MAF Submission)". www.omg.org. Retrieved 2019-02-19.
  20. ↑ 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)
  21. ↑ "IEEE IES Technical Committee on Industrial Agents (TC-IA)". tcia.ieee-ies.org. Retrieved 2019-02-19.
  22. ↑ 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. 
  23. ↑ "Film showcase". MASSIVE. Retrieved 28 April 2012.
  24. ↑ 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.
  25. ↑ 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.
  26. ↑ 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.
  27. ↑ 模板:Cite document
  28. ↑ 模板:Cite document
  29. ↑ AI can predict your future behaviour with powerful new simulations
  30. ↑ AI can predict your future behaviour with powerful new simulations


Further reading

  • 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. 


External links

  • 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.


模板:Systems

Category:Artificial intelligence

类别: 人工智能

Category:Multi-robot systems

类别: 多机器人系统


This page was moved from wikipedia:en:Multi-agent system. Its edit history can be viewed at 多主体系统/edithistory

取自“https://wiki.swarma.org/index.php?title=多智能体系统&oldid=7400”