“多智能体系统”的版本间的差异
| 第3行： | 第3行： | ||
#重定向 [[多智能体系统]] | #重定向 [[多智能体系统]] | ||
| − | [[Image:IntelligentAgent-SimpleReflex.png|thumb|right| | + | [[Image:IntelligentAgent-SimpleReflex.png|thumb|right|Simple reflex agent 简单反射主体]] |
Simple reflex agent | Simple reflex agent | ||
| − | + | 【图1 Simple reflex agent 简单反射主体】 | |
| − | [[Image:IntelligentAgent-Learning.png|thumb|right| | + | [[Image:IntelligentAgent-Learning.png|thumb|right|Learning agent 学习主体]] |
Learning agent | Learning agent | ||
| − | + | 【图2 Learning agent 学习主体】 | |
| 第58行： | 第58行： | ||
* 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]]) | ||
| − | * | + | * 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） |
* Cognitive agents (complex calculations) | * Cognitive agents (complex calculations) | ||
| 第208行： | 第208行： | ||
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. | ||
| − | “请求-响应- | + | “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，价格是...”。 | * 其次，与该请求相关的主体会响应道：“我可以，价格是...”。 | ||
2020年7月18日 (六) 21:07的版本
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。
- 重定向 多智能体系统
Simple reflex agent
【图1 Simple reflex agent 简单反射主体】
Learning agent
【图2 Learning agent 学习主体】
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.
多主体系统（英文：Self-organized System 或 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。
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.
尽管多主体系统和基于主体的模型（英文：Agent-based Model，简称ABM）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。
Concepts 概念
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.
多主体系统由主体（Agent）及其所处的环境组成。一般来说，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。
Agents can be divided into types spanning simple to complex. Categories include:
Agents can be divided into types spanning simple to complex. Categories include:
主体可以按照从简单到复杂的顺序分为如下几个类型:
- Passive agents[8] or "agent without goals" (such as obstacle, apple or key in any simple simulation)
- 被动主体，或者“没有目标的主体”（比如简单仿真中的障碍物、苹果或钥匙）
- Active agents[8] with simple goals (like birds in flocking, or wolf–sheep in prey-predator model)
- 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）
- Cognitive agents (complex calculations)
- 认知主体（可以进行复杂的计算）
Agent environments can be divided into:
Agent environments can be divided into:
主体所处的环境可以分为:
- Virtual
- 虚拟的
- Discrete
- 离散的
- Continuous
- 连续的
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.
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）。主体的行为通常收到'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，及大地方便了人们对于多主体系统进行抽象设计。
Characteristics 特点
The agents in a multi-agent system have several important characteristics:[12]
The agents in a multi-agent system have several important characteristics:
多主体系统中的主体有如下几个重要特点:
- 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]
- 自主性：主体至少是部分独立的，可以自我感知
- 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。
- 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）
Self-organization and Self-direction 自组织与自引导
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).
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（英文：Knowledge Query Manipulation Language，简称KQML）和主体通信语言（英文：Agent Communication Language，简称ACL）是这类语言中的两个典型例子。
System paradigms 系统模式
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.
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：
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）
Path length-MEDIUM_IMPORTANCE: max=60 expectedMax=40,
Path length-MEDIUM_IMPORTANCE: max=60 expectedMax=40,
Path length-MEDIUM_IMPORTANCE: max=60 expectedMax=40, （路径长度——中等重要：最大60，预期最大40）
Max-Weight-UNIMPORTANT
Max-Weight-UNIMPORTANT
Max-Weight-UNIMPORTANT, （最大重量——不重要）
Contract Priority-REGULAR
Contract Priority-REGULAR
Contract Priority-REGULAR, （协议优先性——常规）
and a weighted response matrix, e.g.
and a weighted response matrix, e.g.
还有加权响应矩阵的例子：
Speed-min:50 but only if weather sunny,
Speed-min:50 but only if weather sunny,
Speed-min:50 but only if weather sunny, （最小速度：天气晴朗时50）
Path length:25 for sunny / 46 for rainy
Path length:25 for sunny / 46 for rainy
Path length:25 for sunny / 46 for rainy, （路径长度: 晴天25，雨天46）
Contract Priority-REGULAR
Contract Priority-REGULAR
Contract Priority-REGULAR, （协议优先性——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, （注意，救护车比这个具有更高优先性，你必须等待）
A challenge-response-contract scheme is common in MAS systems, where
- 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.
“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，价格是...”。
- 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
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.
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。例如: 许多早晨进入大都市的汽车晚上可以离开它--绝不会（|这句话似乎与上下文无关？）。
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。
Researches 研究
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)
- organization
- communication
- negotiation
</ref>
/ 参考
- dependability and fault-tolerance
- robotics,[18] 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)
- 分布式约束优化 (DCOPs)
- 组织
- 沟通
- 谈判
- 科学社群（比如生物的群体、语言的进化和经济体）
- 依赖性和容错
- 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 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.
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.[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
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.[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.
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。
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、智能电网和地理信息系统。
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)。
See also 参见
- Comparison of agent-based modeling software 基于主体的建模软件对比
- Agent-based computational economics (ACE) 基于主体的计算经济学 (ACE)
- Artificial brain 人工大脑
- Artificial life 人工生命
- Artificial life framework 人工生命框架
- AI mayor AI市长
- Black box 黑箱
- Blackboard system 黑板系统
- Complex systems 复杂系统
- Discrete event simulation 离散事件仿真
- Emergence 涌现
- Game theory 博弈论
- Human-based genetic algorithm 基于人工的遗传算法
- Knowledge Query and Manipulation Language (KQML) 知识查询操作语言
- Microbial intelligence 微生物智能
- Multi-agent planning 多主体规划
- Pattern-oriented modeling 以模式为导向的建模
- PlatBox Project PlatBox项目
- Self-reconfiguring modular robot 自组织模块化机器人
- Simulated reality 模拟现实
- Social simulation 社会仿真
- Software agent 软件主体
- Swarm intelligence 群体智能
- Swarm robotics 群体机器人
References 参考文献
- ↑ 模板:Cite document
- ↑ "Multi Agent Systems - an overview". ScienceDirect Topics. 2016-01-01. Retrieved 2020-01-23.
- ↑ 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.
- ↑ 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.
- ↑ 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) - ↑ 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.
- ↑ Sun, Ron; Naveh, Isaac. "Simulating Organizational Decision-Making Using a Cognitively Realistic Agent Model". Journal of Artificial Societies and Social Simulation.
- ↑ 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
- ↑ 模板:Russell Norvig 2003
- ↑ Salamon, Tomas (2011). Design of Agent-Based Models. Repin: Bruckner Publishing. p. 22. ISBN 978-80-904661-1-1. http://www.designofagentbasedmodels.info/.
- ↑ 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
- ↑ Wooldridge, Michael (2002). An Introduction to MultiAgent Systems. John Wiley & Sons. pp. 366. ISBN 978-0-471-49691-5.
- ↑ 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.
- ↑ "The Multi-Agent Systems Lab". University of Massachusetts Amherst. Retrieved Oct 16, 2009.
- ↑ Albrecht, Stefano; Stone, Peter (2017), "Multiagent Learning: Foundations and Recent Trends. Tutorial", IJCAI-17 conference (PDF)
- ↑ 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.
- ↑ 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.
- ↑ 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
- ↑ "OMG Document – orbos/97-10-05 (Update of Revised MAF Submission)". www.omg.org. Retrieved 2019-02-19.
- ↑ 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) - ↑ "IEEE IES Technical Committee on Industrial Agents (TC-IA)". tcia.ieee-ies.org. Retrieved 2019-02-19.
- ↑ 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.
- ↑ "Film showcase". MASSIVE. Retrieved 28 April 2012.
- ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ 模板:Cite document
- ↑ 模板:Cite document
- ↑ AI can predict your future behaviour with powerful new simulations
- ↑ AI can predict your future behaviour with powerful new simulations
Further Reading 延伸阅读
- Wooldridge, Michael (2002). An Introduction to MultiAgent Systems. John Wiley & Sons. pp. 366. ISBN 978-0-471-49691-5.
- Shoham, Yoav; Leyton-Brown, Kevin (2008). Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press. pp. 496. ISBN 978-0-521-89943-7. http://www.masfoundations.org.
- Mamadou, Tadiou Koné; Shimazu, A.; Nakajima, T. (August 2000). "The State of the Art in Agent Communication Languages (ACL)". Knowledge and Information Systems. 2 (2): 1–26.
- Hewitt, Carl; Inman, Jeff (Nov–Dec 1991). "DAI Betwixt and Between: From "Intelligent Agents" to Open Systems Science" (PDF). IEEE Transactions on Systems, Man, and Cybernetics. 21 (6): 1409–1419. doi:10.1109/21.135685.
- 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.
- Sun, Ron (2006). Cognition and Multi-Agent Interaction. Cambridge University Press. ISBN 978-0-521-83964-8. http://www.cambridge.org/uk/catalogue/catalogue.asp?isbn=0-521-83964-5.
- Keil, David; Goldin, Dina (2006). Weyns, Danny; Parunak, Van; Michel, Fabien. eds. Indirect Interaction in Environments for Multiagent Systems. LNCS 3830. 3830. Springer. pp. 68–87. doi:10.1007/11678809_5. ISBN 978-3-540-32614-4. https://archive.org/details/environmentsform0000e4ma/page/68.
- Whitestein Series in Software Agent Technologies and Autonomic Computing, published by Springer Science+Business Media Group
- Salamon, Tomas (2011). Design of Agent-Based Models : Developing Computer Simulations for a Better Understanding of Social Processes. Bruckner Publishing. ISBN 978-80-904661-1-1. http://www.designofagentbasedmodels.info/.
- Fasli, Maria (2007). Agent-technology for E-commerce. John Wiley & Sons. pp. 480. ISBN 978-0-470-03030-1.
- Cao, Longbing, Gorodetsky, Vladimir, Mitkas, Pericles A. (2009). Agent Mining: The Synergy of Agents and Data Mining, IEEE Intelligent Systems, vol. 24, no. 3, 64-72.
External links 外部链接
- Random Agent-Based Simulations by Borys Biletskyyhttps://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 – Random agent-base simulations for multi-robot system and Belousov-Zhabotinsky reaction. Java applets available.
- 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.
- Janus multiagent Platform – Holonic multiagent execution platform (Apache License), written in Java, and directly supporting the SARL agent-oriented programming language.
- 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: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