Societies of agents Summary

Level 2 Cognitive Processing through the

Interaction of Many Agents

Chris Jones,

Maja J Matari?,

Barry Werger

A collection of interacting autonomous entities, called ‘agents’, may be capable of creating complex cognitive processes and behaviors, which could not be achieved by a single agent, without the need for outside centralized coordination or control. INTRODUCTION

Several theories of cognition, most notably Min- skys’s Society of Mind, posit that intelligent behavior can be seen as the result of interaction of simple processes. Minsky states: ‘Very few of our actions and decision come to depend on any single mechanism. Instead, they emerge from conflicts and negotiations among societies of processes that constantly challege one another.’ (Minsky, 1986). The central tenet of such theories of cognition and behaviour is that complex system-level behavior can emerger from the interaction of multiple, possibly numerous, components.

A canonical example of such emergence is the function of he human brain. The brain itself is made up of billions of simple neurons organized into a massively connected network (Nicholls et al., 2001). In general, an individual neuron acts as a comparatively simple processing unit that receives signals from a set of neighboring input neurons, and under appropriate conditions transmits signals to a set of its neighboring output neurons. From this network of interacting neurons emerges the complexity of human cognition and behavior. No single neuron or subset of neurons is responsible for this complexity; rather it is the result of their interactions.

Several disparate research fields are actively in-volved in investigating the principles of interaction among a collection of components. These include cognitive science, computer networks, distributed systems, artificial life, collective robotics, multi-agent systems, as well as others. The rest of this article aims to explain conceptually, and show through examples, how complex cognition and behavior can emerge from the interaction of individual components and how those emergent behaviors can be used in a variety of ways. AGENTS

The term ‘agent’ has become a popular choice for a nontrivial component of a system with many inter-acting component that result in emergent behavior. Precisely defining an agent remains difficult, as agents come in many guises. An agent could be a piece of software, an specific computer on the Internet, a mobile robot, or even a person. In general, an agent is an autonomous entity, situated in an environment, and equipped with some degree of intelligence. Being ‘situated’ places a number of constraints on how the agent can operate (Brooks, 1991; Maes, 1990). It implies that the agent has some means of sensing its environment, but the sensing may be limited and inaccurate. For example, a mobile robot may be situated in an office environment and have a means of sensing the distance to nearby objects; thus its sensing capabilities are both limited and prone to error and

Encyclopedia of Cognitive Science, Nature Publishing Group, Macmillan Reference Limited, Nov 2002

noise. Situatedness also implies an interaction with the environment. Thus, the same robot may be able to drive around, thus affecting the environment with its placement, and perhaps move objects, intentionally or otherwise. Conversely, the actions of a situated agent are influenced by the environment. The objects the robot encounters affect what it senses and how it behaves. Another implication of being situated is that an agent is constrained by environmental characteristics. For example, a mobile robot cannot drive through walls nor can it avoid falling if it drives down a set of stairs. Finally, the agent’s characteristics, i.e., its computational, sensory, and actuation capabilities, influence how it interacts with its environment, which includes other agents. SOCIETIES OF AGENTS

A collection of interacting agents is referred to as a ‘society of agents’. Using this metaphor, a brain is a society of agents, as is a team of mobile robots cooperating on a task. Such agent societies are interesting for a number of reasons. First, for certain tasks and/or environments, a society of agents is the only viable or efficient solution. Second, even for tasks that can be handled by an individual agent, there may be more efficient, adaptive, and robust solutions performed by a society of agents. A society of agents may consist of a homogeneous or heterogeneous collection of agents. In a homogeneous society, all agents are identical, while in a heterogeneous society, agents may have different characteristics. The variations in capability may result in hierarchies, specializations, or various other forms of social organization. Consequently, heterogeneous societies are generally more complex to control but are typically capable of larger set of tasks. The humane immune system (Segel and Cohen, 2001) is an excellent example of society of heterogeneous yet simple agents. It is capable of protecting the body against infection and invarsion by foreign substances of all kinds, whether bacteria, virus, parasite, etc., which can be viewed as a very large set of different ‘tasks’ to be accomplished. Each agent is this society is very specific, but the large number of agents of each kind and in total, combined with the ability to generate additional agents when needed, produces an unprecedented defensive functionality.

AGENT INTERACTION

Agents situated in a shared environment have ample opportunity to interact, by directly sensing each other, communicating, coordinating actions, and even competing. The shared environment in which the interactions take place may be an abstract data space, the physical world, or anything in between. A society of software agents may interact through a personal computer or even the entire Internet. Likewise, a society of mobile robots may interact in an office environment or on the surface of Mars. Furthermore, multiple environment types maybe be spanned in a single multi-agent system. For example, in multi-robot systems using behavior-based control (Matari ?, 1994) individual robots are controlled by a collection of internal agents that interact through a computational environment, while the society of physical robots that contains them interacts interact in and through the physical world. The nature of their interactions in a society of agents depends upon such factors as agent capabilities, environmental constraints, and desired local and global behavior. The spectrum of agent interaction is broad. At one end are methods that employ larger number of simple, identical agents, connected together in patterns which lead to useful computation as a result of data flow through the system. At the other end are systems of complex, specialized agents which explicitly negotiate for task assignments and resources. Mechanisms for agent interaction can be broadly classified as fitting into the following, often overlapping categories: interaction through the environment, interaction through sensing, and interaction through communication. Each is described in turn.

Interaction Through the Environment

The first mechanism for interaction among agents is through their shared environment. This form of interaction is indirect in that it consists of no

explicit communication or physical interaction between agents. Instead, the environment itself is used as a medium of indirect communication. This form of interaction is indirect in that it consists of no explicit communication or physical interaction between agents. Instead, the environment itself is used as a medium of indirect communication. This is a powerful method of interaction that can be utilized by very simple agents with no capability for complex reasoning or for direct

communication.

Stigmergy is an example of interaction through the environment employed in a variety of natural insect societies. Originally introduced in the biological sciences to explain some aspects of social insect nest-building behavior, Stigmergy is defined as ‘the process by which the coordination of tasks and the regulation of construction does not depend directly on the workers, but on the constructions themselves’ (McFarland, 1985; Holland and Melhuish, 1999). The notion was originally used to describe the nest-building behavior of termites and ants (Franks and Deneubourg, 1997). It was shown that coordination of building activity in a termite colony was not inherent in the termites themselvers. Instead, the coordination mechanisms were found to be regulated by the task environment, in this case the growing nest structure. A location on the growing nest stimulates a termite’s building behavior, thereby transforming the local nest structure, which in turn stimulates additional building behavior of the same or another termite.

Examples of artificial systems in which agents interact through the environment include distributed construction (Bonabeau et al., 1994), sorting (Deneubourg et al., 1990), clustering (Beckers et al., 1999), object manipulation (Donald et al., 1993) analysis of network congestion (Huberman and Lukose, 1997) and phenomena such as the spread of computer viruses (Minar et al, 1998). Interaction Through Sensing

The second mechanism for interaction among agents is through sensing. As described by Cao et al. (1997), interaction through sensing ‘refers to local interaction that occur between agents as a result of sensing one another, but without explicit communication.’ This form of interaction is also indirect, as there is no explicit communication between agents; however, it requires each agent to be able to distinguish other agents from miscellaneous objects in the environment. Interaction through sensing can be used by an agent to model the behavior of another agent or to determine what another agent is doing in order to

make decisions and respond appropriately. For example, flocking birds use sensing to monitor the actions of other birds in their vicinity in order to make local corrections to their own motion. It has been shown that effective flocking results from quite simple local rules followed by each of the birds in the society (i.e., flock), responding to the direction and velocity of the local neighbors (Reynolds, 1987). Such methods of interaction through sensing can be found in use in mobile robot flocking, following, and foraging (Matari ? 1995), robot soccer (Werger, 1999), robot formations (Fredslund and Matari ?, 2002), and simulations of behaviorally realistic animations of fish schooling (Tu and Terzopoulus, 1994). Other applications of interaction through sensing include human-like physical or visual interaction between physical agents (Murciano and del R. Milian, 1996, Michaud and Vu, 1999; Nicolescu and Matari ?, 2000), including the ability to understand and influence the motives of other physical agents (Breazeal and Scassellati, 1999; Ogata et al., 2000).

Interaction Through Communication

The third mechanism for interaction among agents is through direct communication. Unlike the first two forms of interaction described above, which were indirect, in interaction through communication agents may address other agents directly, either in a system-specific manner or through a standard agent communication protocol such as KQML (Finin et al., 1996) or CORBA (Vinoski, 1997). Such agent-directed

communication can be used to request information or action from other or to respond to request received from others. Communications may be task-related rather than agent-directed, in which case it is made available to all (or a subset) of the

agents in the environment. Two common task-related communication schemes are blackboard architectures (Schwartz, 1995; Gelernter, 1991) and publish/subscribe messaging (Arvola, 1998). In blackboard architectures, agents examine and modify a central data repository; in

publish/suscribe messaging, subscribing agents request to receive certain categories of messages, and publishing agents supply messages to all appropriate subsctibers. In some domains, such as the Internet, communication is reliable and of unlimited range, while in others, such as physical robot interaction, communication range and reliability are important factors in system design (Arkin, 1998; Gerkey and Matari ?, 2001).

FROM AGENT INTERACTION TO COGNITION AND BEHAVIOR

Given a society of interacting agents, how is

complex system-level behavior achieved?

Interaction among the society members is not

sufficient in itself to produce an interesting or

useful global result. In order for the interacting

agents to produce coherent global behavior, there

must be some overarching coordination

mechanism that appropriately organizes the

interactions in both space and time.

There are many coordination mechanisms by which to organize the various interactions among agents in order to produce coherent system-level behavior. Self-organization techniques are based on a set of dynamical mechanisms whereby structures appear at the global level of system from interactions among its lower-level components. The rules specifying the interactions among the system’s constituent units are executed on the basis of purely local information, without reference to the global pattern, which is an emergent property of the system rather than a property imposed upon the system by an external ordering influence’. (Bonabeau et al., 1997). Methods such as genetic algorithms (Holland, 1975), machine learning techniques such as reinforcement learning (Sutton and Barto, 1998), and distributed constrains satisfaction (Clearwater et al. 1991) can all be used to design agents and their interactions such that the resulting behavior meets desired system-level goals. Agents may also explicitly negotiate with

each other for resources and task assignments in order to coordinate their behavior. One such approach, employed in human as well as synthetic agent societies, is ‘market-based’ coordination, where individual agents competitively bid for

tasks, which they must either completer or report as broken contracts (Gerkey and Matari ?, 2002). Auchtions are a common coordination method in market-based techniques. In auctions, the most appropriate agents are continuously selected and (re)-assigned to various non-terminating to roles (Tambe and Jung, 1999; Werger and Matari ?, 2000). In contrast, more symbolic negotiation protocols based on distributed planning involve

multiplestages, in which agents first share their plans, then criticize them, and finally update them accordingly (Bussmann and Muller, 1992; Kreifelt and von Martial, 1991; Lesser and Corkill, 1981).

Game-theoretic approaches to negotiations have

proven effective in situations where agents may be

deceptive in their communication (Rosenchein and

Zlotkin, 1994). In most complex models of

negotiation-based coordination, agents reason

about the beliefs, desires, and intentions of other

agents, and influence those using specialized

techniques (Brandt et al., 2000).

SUMMARY

As was stated earlier, in systems where complex global behavior emerges from the interactions of a society of simple agents, as the complex function of a society of simple agents, as the complex function of the brain emerges from the interactions of a larger society of neurons, the resulting complexity cannot be attributed to any single agent but instead to the interaction of all agents. Agents and their, often local, interactions with each other and with the environment generate the resulting global behavior of the system – no additional external coordination mechanism is needed. Interaction in the agent society can take place through several mechanisms, including interaction through the environment, through sensing, and through communication. In lieu of a central coordinator, a society of agents coordinates its interactions to produce desired system-level behavior through such mechanisms as self-

organization, machine learning techniques, or more complex negotiation mechanisms.

The notion that complex behavior can arise

from the interaction of simple agents has powerful and far-reaching implications. Many fields, ranging from biology, to artificial intelligence, computer networking, and business management,

all find inspiration and motivation from these principles.

References

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Glossary

Agnet An autonomous entity situated in an environment with some degree of intelligence, sensing, communication, and actuation capabilities. Agent interaction Methods by which agents may exchange information, coordinate action, etc. Interaction through communication A method of interaction among agents through direct communication channels (i.e., radio, Internet, etc.). Interaction through the environment A method of indirect interaction among agents using the environment as the medium of communication.

Interaction through sensing A method of indirect agent interaction through passive sensing of other agents and their actions.

Society of agents A collection of interacting agents situated in a shared environment.

Keywords: Multi-agent systems, emergent behavior, self-organization, distributed systems, society of agents.

新时代交互英语读写译课文翻译讲课稿

新时代交互英语读写译课文翻译

新时代交互英语读写译课文翻译- 哈佛学子的成功之道 1 许多不同类型的自助书籍都给读者提出建议，帮助他们培养一定的技能、目标和态度，以获得成功，并拥有一种健康的生活方式。现在，在采访了 1600 名大学生后，马萨诸塞州波士顿市哈佛大学的研究者们找到了促成哈佛学子学业成功、让他们产生满足感的因素。 2 他们的重要发现让哈佛大学做出了改变其策略的决定，这些变化包括：给学生布且更多的小组作业以及在下午安排课程。目的是为了鼓励学生吃晚餐时可以继续讨论他们的课程。 3 研究者们是如何获得他们所需的信息的呢？首先，他们提出很多问题，涉及学生的课余活动、教学质量和指导质量等方方面面。研究者们通过寻找规律来找出某些课程有用的原因。他们询问学生的感受----对自己所学的东西是否感到高兴、满意或兴奋，然后把得到的答案与学生们在学业方面的成功进行比较。这些问题旨在找出是什么让学生高兴，又是什么在帮助他们学习。 4 以下是他们通过调查研究所提出的建议： (1) 了解任课教师。学生每个学期都要听四到五个教师的课。至少要熟悉其中的一位教师并且也要让他们认识你，这样会有使你感到和学校联系在一起。另一个重要的忠告是：向任课教师请教有关课程学习的建议。问一些有关你要做什么才能得到提高的具体问题。例如，像“怎样才能使我第一段的论证更有效？”这样的问题能让你获得所需的建议。研究表明，大多数寻求帮助的学生都能提高他们的成绩，而那些没有寻求帮助的学生则成绩不好或者不及格。 (2)分小组学习。做作业很重要，但是仅仅如此是不够的。为了真正理解学习内容，在学习之余最好和两三个同学组成学习小组，和他们一起完成作业。能这样做的同学会更好地理解学习内容。他们会觉得与课堂的联系更紧密，从而更积极地参与课堂学习。 (3) 留出足够的时间去做好一件事情。在这次哈佛大学的研究中，学习好的学生比学习不好的学生在学习上花的时间更多。学习好的学生可以不间断地学习两到三个小时，而学习不太好的学生一次只学习、二、三十分钟，往往在一天的某些特定时段学习，如晚费前载体育运动后。几乎没有多少学生能意识到抓有不间断的学习时间的必要性。 (4)参加一项活动。你也许认识不到加入一个俱乐部或加入一个团队的重要性，但这恰恰是很重要的。要参加体育俱乐部，你不必是一名优秀的运动员。没有运动天赋的学生可以在体育俱乐部里给教练帮帮忙，尽自己的一份力，比如在比赛或训练期间给队员准备水瓶或切好橙子给他们吃。 学生只要参加了一 项活动，哪怕参与的方式是微不足道的，他们也会以更积极的心态去面对他们的学业。

国际市场营销-知识点整理

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Unit 1 College Life Language study of Reading1: Harvard University’s Habits for Success. ①Many different types of self-help books advise the reader to develop the skills, goals, and attitudes that will both promote success and provide for a healthy lifestyle. [= Through self-help books, a reader can learn how to become successful and prepare for a healthy way of living by developing the skills, goals and attitudes. ] 许多不同类型的自助书籍都给读者提出建议，帮助他们培养一定的技能、目标和态度，以获得成功，并拥有一种健康的生活方式。 develop v. 1) to grow or change into something bigger, stronger, or more advanced, or to make someone or something do this [ advance]发展Chicago developed into a big city in the late 1800s. 2）to design or make a new idea, product, system etc over a period of time 发明，设计 Scientists are developing new drugs to treat arthritis关节炎. 3）to start to have a feeling or quality that then becomes stronger: 逐渐产生 The children are beginning to develop a sense of responsibility. 孩子们逐渐产生了一种责任感。 develop a sense of…/develop a awareness of产生一种…意识 It was in college that he developed a taste for (=started to like) rugby football.在大学的时候他开始喜欢上了橄榄球。 4）if you develop a skill or ability, or if it develops, it becomes stronger or more advanced: 进步，提高 The course is designed to help students develop their speaking skills.这门课是为了提高学生演讲能力而设计的。promote v. 1) To help in the growth or development of 促进，推动，增进 e.g. promote foreign trade 促进对外贸易 e.g. Milk can promote health. 牛奶可以增进健康 2) To give (someone) a higher position or rank 提升，晋升（某人） e.g. The instructor was promoted to professor. 那位讲师升为教授。（to 后表示地位之名词不加冠词 3）To bring (goods) to public notice in order to encourage people to buy 推销/促销（货物）promote a new product; promoter：（事业）推动者，赞助人，（公司）创立者 promotion：晋级，促进；促销get/be given a promotion provide for 1) to make the necessary future arrangements for 为…做好准备 It is highly important to provide for the future.预先做好准备非常重要。 2)to support; supply with the things necessary for life供养，抚养 He had to provide for five children.他要供养五个孩子。 3)provide sb. With sth / provide sth for sb. lifestyle n. 生活方式；lifework 一生的事业 Lifetime: the chance of a lifetime；in one’s lifetime; lifetime employment 终生雇佣 ②contribute （to）v. 1)to help in causing a situation, event, or condition起促成作用,有助于，成为（---的）原因 e.g.A proper amount of exercise contributes to good health. Various factors contributed to his downfall.各种因素导致了他的垮台 2) to join with others in giving (money, help, etc) 捐献；捐助；贡献出；提供（时间，精力等） e.g. He contributed a lot of money to the charity. e.g. She contributed many good ideas to the discussion. Contribution n. make great contributions to --- ③tip n. 1) a helpful piece of advice 小建议，小窍门，劝告，告诫 Take my tip and keep well away from that place. The manual手册，指南is full of useful tips. 2) a small amount of money given as a gift, usually in addition to the official price, for a small service performed 小费Shall I leave a tip for the waiter? 3) the usually pointed end of something （某物的）尖端 a town at the southern tip of India on/at the tip of one's tongue: not quite able to be remembered 就在嘴边，但记不起来了 Now what's her name? It's on the tip of my tongue. the tip of the iceberg: a small sign of a much larger situation, problem, etc.冰山一角；重大情况（问题等）露出表面的极小一部分；The official statistics on drug addiction are only the tip of the iceberg; the real figure may well be much higher. ④specific adj. 1) detailed and exact; clear in meaning or explanation 明确的，具体的e.g. He came here for a specific aim. 2) particular；fixed, determined, or named 特定的，一定的，（至于名词前） e.g. a specific sum of money 一定金额 3) limited to；found only in 仅限于----的；特有的；固有的 e.g. This disease is specific to horses. ⑤effective adj.

二十一世纪大学实用英语综合教程第四册第一单元习题答案和课文翻译

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新标准大学英语综合教程3课文summary

↓↓↓ 大英3课文Summary UNIT 1 1.1 catching crabs In the fall of our final year,our mood changed.The relaxed atmosphere had disappeared, and peer group pressure to work hard was strong. Meanwhile,at the back of everyone’s mind was what we would do next after graduation. As for me,I wanted to travel,and I wanted to be a writer.I braced myself for some resistance to the idea from my father,who wanted me to go to law school,and follow his path through life. However,he supported what I wanted but he made me think about it by watching the crabs.The cage was full of crabs. One of them was trying to escape,but each time it reached the top the other crabs pulled it back.In the end it gave up lengthy struggle to escape and started to prevent other crabs from escaping.By watching crabs,my father told me not to be pulled back by others,and to get to know himself better. 1.2We are all dying Life is short.We never quite know when we become coffin dwellers or trampled ash in the rose garden of some local ceremony.So there’s no p oint in putting our dreams on the back burner until the right time arrives.Now is the time to do what we want to do. Make the best of our short stay and fill our life with the riches on offer so that when the reaper arrives,we’ve achieved much instead of regrets. UNIT 2 2.1superman The extract from Johnny Panic and the Bible of Dreams by Sylvia Plath is a combination of her real life and imaginary life in her childhood.In the real life,Plath was a winner of the prize for drawing the best Civil Defense signs,lived by an airport and had an Uncle who bore resemblance to Superman.In her imagination,the airport was her Mecca and Jerusalem because of her flying dreams.Superman fulfilled her dream at the moment. David Stirling,a bookish boy,also worship Superman.During the recess at school,he and the author played Superman https://www.wendangku.net/doc/771762425.html,pared with their school-mates who played the routine games,they felt they were outlaws but had a sense of windy superiority.They also found a stand-in,Sheldon Fein, who later invented tortures. 2.2cultual childhoods Historically,childhood has undergone enormous transformations in terms of children’s responsibilities and parental expectations.Culturally,childhood is socially constructed.The interplay of history and cultural leads to different understanding of childhood,consequently it is advisable not to impose ideas from one culture to understand childhood in another culture. UNIT 3 3.1how we listen For the sake of clarify,we split up the process of listening to music into three hypothetical planes.Firstly,the sensuous plane.It is a kind of brainless but attractive state of mind engendered