Multi Agent Systems

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Multi-agent system Learning agent A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Examples of problems which are appropriate to multi-agent systems research include online trading,[1] disaster response,[2] and modelling social structures.[3]

Contents [hide]

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1 Overview 2 Multi-agent system basics o 2.1 Multiple agent systems paradigms o 2.2 Properties 3 The study of multi-agent systems 4 Frameworks 5 Applications in the real world 6 See also 7 References

8 Further reading

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[edit] Overview The agents in a multi-agent system have several important characteristics:[4] • • •

Autonomy: the agents are at least partially autonomous Local views: no agent has a full global view of the system, or the system is too complex for an agent to make practical use of such knowledge Decentralization: there is no one controlling agent (or the system is effectively reduced to a monolithic system)[5]

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 can manifest self-organization and complex behaviors even when the individual strategies of all their agents are simple. Agents can share knowledge using any agreed language, within the constraints of the system's communication protocol. Example languages are Knowledge Query Manipulation Language (KQML) or FIPA's Agent Communication Language (ACL).

[edit] Multi-agent system basics [edit] Multiple agent systems paradigms Many MAS systems are implemented in computer simulations, stepping the system through discrete "time steps". The MAS components communicate typically using a weighted request matrix, e.g. Speed-VERY_IMPORTANT: min=45mph, Path length-MEDIUM_IMPORTANCE: max=60 expectedMax=40, Max-Weight-UNIMPORTANT Contract Priority-REGULAR

and a weighted response matrix, e.g. Speed-min:50 but only if weather sunny, Path length:25 for sunny / 46 for rainy Contract Priority-REGULAR note - ambulance will override this priority and you'll have to wait

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 more short communication steps between sides,

also considering other components, evolving "contracts", and the restriction sets of the component algorithms. Another paradigm commonly used with MAS systems is the pheromone, where components "leave" information for other components "next in line" or "in the vicinity". These "pheromones" may "evaporate" with time, that is their values may decrease (or increase) with time.

[edit] Properties MAS systems, also referred to as "self-organized systems", 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 physical 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. The main feature which is achieved when developing multi-agent systems, if they work, is flexibility, since a multi-agent system can be added to, modified and reconstructed, without the need for detailed rewriting of the application. These systems also tend to be rapidly self-recovering and failure proof, usually due to the heavy redundancy of components and the self managed features, referred to, above.

[edit] The study of multi-agent systems The study of multi-agent systems is concerned with the development and analysis of sophisticated artificial intelligence problem solving and control architectures for multiple-agent systems. Topics of research in MAS include: • • • • • • • • • •

agent-oriented software engineering beliefs, desires, and intentions (BDI) cooperation and coordination organisation communication negotiation distributed problem solving multi-agent learning scientific communities dependability and fault-tolerance

[edit] Frameworks While ad hoc multi-agent systems are often created from scratch by researchers and developers, some frameworks have arisen that implement common standards (such as the FIPA agent system platforms and communication languages). These frameworks save developers time and also aid in the standardization of MAS development.

[edit] Applications in the real world Please help improve this section by expanding it. Further information might be found on the talk page. (December 2008) Multi-agent systems are applied in the real world to graphical applications such as computer games. Agent systems have been used in films.[6] They are also used for coordinated defence systems. Other applications include transportation, logistics, graphics, GIS as well as in many other fields. It is widely being advocated for use in networking and mobile technologies, to achieve automatic and dynamic load balancing, high scalability, and self-healing networks.

[edit] See also • • • • • • • •

Agent-based model (ABM) Comparison of agent-based modeling software Artificial brain Artificial intelligence Complex systems Distributed artificial intelligence Emergence Evolutionary computation

• • • • • • • • •

Knowledge Query and Manipulation Language (KQML) Multi-agent planning Pattern oriented modeling PlatBox Project Scientific Community Metaphor Self-organization Simulated reality Social simulation Software agent

Human-based genetic algorithm

Swarm Intelligence

Intelligent agent

Artificial life framework

[edit] References 1. ^ Alex Rogers and E. David and J.Schiff and N.R. Jennings. The Effects of Proxy 2. 3. 4. 5. 6.

Bidding and Minimum Bid Increments within eBay Auctions, ACM Transactions on the Web, 2007 ^ Nathan Schurr and Janusz Marecki and Milind Tambe and Paul Scerri et al. The Future of Disaster Response: Humans Working with Multiagent Teams using DEFACTO, 2005. ^ Ron Sun and Isaac Naveh. Simulating Organizational Decision-Making Using a Cognitively Realistic Agent Model, Journal of Artificial Societies and Social Simulation. ^ Michael Wooldridge, An Introduction to MultiAgent Systems, John Wiley & Sons Ltd, 2002, paperback, 366 pages, ISBN 0-471-49691-X. ^ Liviu Panait, Sean Luke: Cooperative Multi-Agent Learning: The State of the Art. Autonomous Agents and Multi-Agent Systems 11(3): 387-434 (2005) ^ Massive (software) — Film showcase

Software agent Programming paradigms • •

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Agent-oriented Component-based o Flow-based o Pipeline Concatenative Concurrent computing Declarative (contrast: Imperative) o Functional  Dataflow  Cell-oriented (spreadsheets)  Reactive o Goal-directed  Constraint  Constraint logic  Logic  Abductive logic  Inductive logic Event-driven Feature-oriented Function-level (contrast: Value-level) Imperative (contrast: Declarative) o Greater separation of concerns  Aspect-oriented  Subject-oriented o Array (contrast: Scalar) o Automata-based

Object-oriented  Class-based  Prototype-based o Procedural  Structured o Role-oriented o Scalar (contrast: Array) Iterative (contrast: Recursive) Metaprogramming o Attribute-oriented o Automatic  Generic  Template  Policy-based  Language-oriented  Grammar-oriented  Dialecting  Intentional o Reflective Modular Nondeterministic Parallel computing o Process-oriented Programming in the large and programming in the small Recursive (contrast: Iterative) Tree Value-level (contrast: Function-level) o

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In computer science, a software agent is a piece of software that acts for a user or other program in a relationship of agency[1]. Such "action on behalf of" implies the authority to decide which (and if) action is appropriate.[citation needed] The idea is that agents are not strictly invoked for a task, but activate themselves. Related and derived concepts include Intelligent agents (in particular exhibiting some aspect of Artificial Intelligence, such as learning and reasoning), autonomous agents (capable of modifying the way in which they achieve their objectives), distributed agents (being executed on physically distinct computers), multi-agent systems (distributed agents that do not have the capabilities to achieve an objective alone and thus must communicate), and mobile agents (agents that can relocate their execution onto different processors).



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1 Definition o 1.1 Intelligent software agents o 1.2 Autonomous agents o 1.3 Distributed agents o 1.4 Multi-agent systems o 1.5 Mobile agents o 1.6 Fuzzy agents 2 What an agent is not o 2.1 Distinguishing agents from programs o 2.2 Intuitive distinguishing agents from objects o 2.3 Distinguishing agents from expert systems o 2.4 Distinguishing intelligent software agents from intelligent agents in artificial intelligence 3 History 4 Examples o 4.1 Intelligent software agents  4.1.1 Buyer agents (shopping bots)  4.1.2 User agents (personal agents)  4.1.3 Monitoring-and-surveillance (predictive) agents  4.1.4 Data mining agents o 4.2 Other examples 5 Design issues 6 Notes

7 External links

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[edit] Definition

Nwana's Category of Software Agent The term "agent" describes a software abstraction, an idea, or a concept, similar to OOP terms such as methods, functions, and objects.[citation needed] The concept of an agent provides a convenient and powerful way to describe a complex software entity that is capable of acting with a certain degree of autonomy in order to accomplish tasks on

behalf of its user. But unlike objects, which are defined in terms of methods and attributes, an agent is defined in terms of its behavior.[citation needed] Various authors have proposed different definitions of agents, these commonly include concepts such as • • • •

persistence (code is not executed on demand but runs continuously and decides for itself when it should perform some activity) autonomy (agents have capabilities of task selection, prioritization, goaldirected behaviour, decision-making without human intervention) social ability (agents are able to engage other components through some sort of communication and coordination, they may collaborate on a task) reactivity (agents perceive the context in which they operate and react to it appropriately).

The Agent concept is most useful as a tool to analyze systems, not as a prescription. [citation needed] The concepts mentioned above often relate well to the way we naturally think about complex tasks and thus agents can be useful to model such tasks

[edit] Intelligent software agents See also: intelligent agent The design of intelligent agents (or intelligent software agents) is a branch of artificial intelligence research. Capabilities of intelligent agents include:[citation needed] •

ability to adapt Adaptation implies sensing the environment and reconfiguring in response. This can be achieved through the choice of alternative problem-solving-rules or algorithms, or through the discovery of problem solving strategies. Adaptation may also include other aspects of an agent's internal construction, such as recruiting processor or storage resources.

ability to learn Learning may proceed through trial-and-error, then it implies a capability of introspection and analysis of behaviour and success. Alternatively, learning may proceed by example and generalization, then it implies a capacity to abstract and generalize.

[edit] Autonomous agents Autonomous agents are software agents that claim to be autonomous, being selfcontained and capable of making independent decisions, and taking actions to satisfy internal goals based upon their perceived environment. All software agents in important applications are closely supervised by people who start them up, monitor and continually modify their behavior, and shut them down when necessary.[citation needed] The Popek and Goldberg virtualization requirements is a hardware solution to the

supervision problem, which in principle prevents the execution of critical instructions without entering a suitable mode (such as System or Super-user mode).

[edit] Distributed agents Since agents are well suited to include their required resources in their description, they can be designed to be very loosely coupled and it becomes easy to have them executed as independent threads and on distributed processors. Thus they become distributed agents and the considerations of distributed computing apply. Agent code is particularly easy to implement in a distributed fashion and should scale well.[citation needed]

[edit] Multi-agent systems When several agents (inter)act they may form a multi-agent system a.k.a. multipleagent system. Characteristically such agents will not have all data or all methods available to achieve an objective (this can be referred to as "limited viewpoint") and thus will have to collaborate with other agents. Also, there may be little or no global control and thus such systems are sometimes referred to as swarm systems. As with distributed agents, data is decentralized and execution is asynchronous. Earlier, related fields include Distributed Artificial Intelligence (DAI) and distributed problem solving (DPS).

[edit] Mobile agents Main article: mobile agent Agent code that moves itself, including its execution state, on to another machine, to continue execution there. This is also referred to as mobile code. Agents can be used to gather system information, taking back-up of files by copying them in client-server paradigm, monitoring network throughput or to check resources availability and moderating the resource utilization of system by checking the services running on system.

analysis of packets

[edit] Fuzzy agents In computer science a fuzzy agent is a software agent that implements fuzzy logic. This software entity interacts with its environment through an adaptive rule-base and can therefore be considered as a type of intelligent agent.

[edit] What an agent is not It is not useful to prescribe what is, and what is not an agent. However contrasting the term with related concepts may help clarify its meaning: [edit] Distinguishing agents from programs Franklin & Graesser (1997) discuss four key notions that distinguish agents from arbitrary programs: reaction to the environment, autonomy, goal-orientation and persistence. [edit] Intuitive distinguishing agents from objects • • •

Agents are more autonomous than objects. Agents have flexible behaviour, reactive, proactive, social. Agents have at least one thread of control but may have more. (Wooldridge, 2002)[citation needed]

[edit] Distinguishing agents from expert systems • • •

Expert systems are not coupled to their environment; Expert systems are not designed for reactive, proactive behavior. Expert systems do not consider social ability (Wooldridge, 2002)

[edit] Distinguishing intelligent software agents from intelligent agents in artificial intelligence •

Intelligent agents (also known as rational agents) are not just software programs, they may also be machines, human beings, communities of human beings (such as firms) or anything that is capable of goal directed behavior. (Russell & Norvig 2003)

[edit] History The concept of an agent can be traced back to Hewitt's Actor Model (Hewitt, 1977) "A self-contained, interactive and concurrently-executing object, possessing internal state and communication capability."

To be more academic, software agent systems are a direct evolution from Multi-Agent Systems (MAS). MAS evolved from Distributed Artificial Intelligence (DAI), Distributed Problem Solving (DPS) and Parallel AI (PAI), thus inheriting all characteristics (good and bad) from DAI and AI. John Sculley’s 1987 “Knowledge Navigator” video portrayed an image of a relationship between end-users and agents. Being an ideal first, this field experienced a series of unsuccessful top-down implementations, instead of a piece-by-piece, bottom-up approach. The range of agent types is now (from 1990) broad: WWW, search engines, etc.

[edit] Examples [edit] Intelligent software agents See also: Intelligent agent Haag (2006) suggests that there are only four essential types of intelligent software agents:[2] 1. 2. 3. 4.

Buyer agents or shopping bots User or personal agents Monitoring-and-surveillance agents Data Mining agents

[edit] Buyer agents (shopping bots) Buyer agents travel around a network (i.e. the internet) retrieving information about goods and services. These agents, also known as 'shopping bots', work very efficiently for commodity products such as CDs, books, electronic components, and other onesize-fits-all products. is a good example of a shopping bot. The website will offer you a list of books that you might like to buy on the basis of what you're buying now and what you have bought in the past. Another example is used on eBay. At the bottom of the page there is a list of similar products that other customers who did the same search looked at. This is because it is assumed the user tastes are relatively similar and they will be interested in the same products. This technology is known as collaborative filtering. [edit] User agents (personal agents) User agents, or personal agents, are intelligent agents that take action on your behalf. In this category belong those intelligent agents that already perform, or will shortly perform, the following tasks: • •

Check your e-mail, sort it according to the user's order of preference, and alert you when important emails arrive. Play computer games as your opponent or patrol game areas for you.

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Assemble customized news reports for you. There are several versions of these, including newshub and CNN. Find information for you on the subject of your choice. Fill out forms on the Web automatically for you, storing your information for future reference Scan Web pages looking for and highlighting text that constitutes the "important" part of the information there "Discuss" topics with you ranging from your deepest fears to sports

[edit] Monitoring-and-surveillance (predictive) agents Monitoring and Surveillance Agents are used to observe and report on equipment, usually computer systems. The agents may keep track of company inventory levels, observe competitors' prices and relay them back to the company, watch stock manipulation by insider trading and rumors, etc.

service monitoring For example, NASA's Jet Propulsion Laboratory has an agent that monitors inventory, planning, and scheduling equipment ordering to keep costs down, as well as food storage facilities. These agents usually monitor complex computer networks that can keep track of the configuration of each computer connected to the network. [edit] Data mining agents See also: Data mining agents This agent uses information technology to find trends and patterns in an abundance of information from many different sources. The user can sort through this information in order to find whatever information they are seeking. A data mining agent operates in a data warehouse discovering information. A 'data warehouse' brings together information from lots of different sources. "Data mining" is the process of looking through the data warehouse to find information that you can use to take action, such as ways to increase sales or keep customers who are considering defecting. 'Classification' is one of the most common types of data mining, which finds patterns in information and categorizes them into different classes. Data mining agents can also detect major shifts in trends or a key indicator and can detect the presence of new information and alert you to it. For example, the agent may detect a decline in the construction industry for an economy; based on this relayed information construction

companies will be able to make intelligent decisions regarding the hiring/firing of employees or the purchase/lease of equipment in order to best suit their firm.

[edit] Other examples Some other examples of current Intelligent agents include some spam filters, game bots, and server monitoring tools. Search engine indexing bots also qualify as intelligent agents. • •

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User agent - for browsing the World Wide Web Mail transfer agent - For serving E-mail, such as Microsoft Outlook. Why? It communicates with the POP3 mail server, without users having to understand POP3 command protocols. It even has rule sets that filter mail for the user, thus sparing them the trouble of having to do it themselves. SNMP agent DAML (DARPA Agent Markup Language) Jason (multi-agent systems development platform) 3APL (Artificial Autonomous Agents Programming Language) GOAL Agent Programming Language Web Ontology Language (OWL) daemons in Unix-like systems. In Unix-style networking servers, httpd is an HTTP daemon which implements the HyperText Transfer Protocol at the root of the World Wide Web Management agents used to manage telecom devices Crowd simulation for safety planning or 3D computer graphics,

[edit] Design issues Interesting issues to consider in the development of agent-based systems include[citation needed]

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how tasks are scheduled and how synchronization of tasks is achieved how tasks are prioritized by agents how agents can collaborate, or recruit resources, how agents can be re-instantiated in different environments, and how their internal state can be stored, how the environment will be probed and how a change of environment leads to behavioral changes of the agents how messaging and communication can be achieved, what hierarchies of agents are useful (e.g. task execution agents, scheduling agents, resource providers ...).

For software agents to work together efficiently they must share semantics of their data elements. This can be done by having computer systems publish their metadata. The definition of agent processing can be approached from two interrelated directions:

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internal state processing and ontologies for representing knowledge interaction protocols - standards for specifying communication of tasks

Agent systems are used to model real world systems with concurrency or parallel processing. • • • •

Agent Machinery - Engines of various kinds, which support the varying degrees of intelligence Agent Content - Data employed by the machinery in Reasoning and Learning Agent Access - Methods to enable the machinery to perceive content and perform actions as outcomes of Reasoning Agent Security - Concerns related to distributed computing, augmented by a few special concerns related to agents

The agent uses its access methods to go out into local and remote databases to forage for content. These access methods may include setting up news stream delivery to the agent, or retrieval from bulletin boards, or using a spider to walk the Web. The content that is retrieved in this way is probably already partially filtered – by the selection of the newsfeed or the databases that are searched. The agent next may use its detailed searching or language-processing machinery to extract keywords or signatures from the body of the content that has been received or retrieved. This abstracted content (or event) is then passed to the agent’s Reasoning or inferencing machinery in order to decide what to do with the new content. This process combines the event content with the rule-based or knowledge content provided by the user. If this process finds a good hit or match in the new content, the agent may use another piece of its machinery to do a more detailed search on the content. Finally, the agent may decide to take an action based on the new content; for example, to notify the user that an important event has occurred. This action is verified by a security function and then given the authority of the user. The agent makes use of a user-access method to deliver that message to the user. If the user confirms that the event is important by acting quickly on the notification, the agent may also employ its learning machinery to increase its weighting for this kind of event.

Electronic commerce From Wikipedia, the free encyclopedia

(Redirected from E-commerce) Jump to: navigation, search Part of a series on

Electronic commerce Online goods and services Streaming media Electronic books Software Retail services

Banking · Food ordering Online flower delivery · Blu-ray Disc or DVD rental Travel Marketplace services Trading communities Auctions · Online wallet Advertising Price comparison service E-procurement Purchase-to-pay This box: view • talk • edit

Electronic Commerce, commonly known as (electronic marketing) e-commerce or eCommerce, consists of the buying and selling of products or services over electronic systems such as the Internet and other computer networks. The amount of trade conducted electronically has grown extraordinarily with widespread Internet usage. The use of commerce is conducted in this way, spurring and drawing on innovations in electronic funds transfer, supply chain management, Internet marketing, online transaction processing, electronic data interchange (EDI), inventory management systems, and automated data collection systems. Modern electronic commerce typically uses the World Wide Web at least at some point in the transaction's lifecycle, although it can encompass a wider range of technologies such as e-mail as well. A large percentage of electronic commerce is conducted entirely electronically for virtual items such as access to premium content on a website, but most electronic commerce involves the transportation of physical items in some way. Online retailers are sometimes known as e-tailers and online retail is sometimes known as e-tail. Almost all big retailers have electronic commerce presence on the World Wide Web. Electronic commerce that is conducted between businesses is referred to as businessto-business or B2B. B2B can be open to all interested parties (e.g. commodity exchange) or limited to specific, pre-qualified participants (private electronic market). Electronic commerce that is conducted between businesses and consumers, on the other hand, is referred to as business-to-consumer or B2C. This is the type of electronic commerce conducted by companies such as Electronic commerce is generally considered to be the sales aspect of e-business. It also consists of the exchange of data to facilitate the financing and payment aspects of the business transactions.

Contents [hide] •

1 History o 1.1 Early development

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o 1.2 Timeline 2 Business applications 3 Government regulations 4 Forms 5 See also 6 Notes 7 References

8 External links

[edit] History [edit] Early development The meaning of electronic commerce has changed over the last 30 years. Originally, electronic commerce meant the facilitation of commercial transactions electronically, using technology such as Electronic Data Interchange (EDI) and Electronic Funds Transfer (EFT). These were both introduced in the late 1970s, allowing businesses to send commercial documents like purchase orders or invoices electronically. The growth and acceptance of credit cards, automated teller machines (ATM) and telephone banking in the 1980s were also forms of electronic commerce. Another form of e-commerce was the airline reservation system typified by Sabre in the USA and Travicom in the UK. Online shopping was invented in the UK in 1979 by Michael Aldrich[citation needed] and during the 1980s it was used extensively particularly by auto manufacturers such as Ford, Peugeot-Talbot, General Motors and Nissan. From the 1990s onwards, electronic commerce would additionally include enterprise resource planning systems (ERP), data mining and data warehousing. The earliest[citation needed] example of many-to-many electronic commerce in physical goods was the Boston Computer Exchange, a marketplace for used computers launched in 1982. The first[citation needed] online information marketplace, including online consulting, was likely the American Information Exchange, another preInternet[clarification needed] online system introduced in 1991. Until 1991, commercial enterprise on the Internet was strictly prohibited.[1] Although the Internet became popular worldwide around 1994, it took about five years to introduce security protocols and DSL allowing continual connection to the Internet. And by the end of 2000, a lot of European and American business companies offered their services through the World Wide Web. Since then people began to associate a word "ecommerce" with the ability of purchasing various goods through the Internet using secure protocols and electronic payment services.

[edit] Timeline • •

1990: Tim Berners-Lee writes the first web browser, WorldWideWeb, using a NeXT computer. 1992: J.H. Snider and Terra Ziporyn publish Future Shop: How New Technologies Will Change the Way We Shop and What We Buy. St. Martin's Press. ISBN 0312063598.

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1994: Netscape releases the Navigator browser in October under the code name Mozilla. Pizza Hut offers pizza ordering on its Web page. The first online bank opens. Attempts to offer flower delivery and magazine subscriptions online. Adult materials also becomes commercially available, as do cars and bikes. Netscape 1.0 is introduced in late 1994 SSL encryption that made transactions secure. 1995: Jeff Bezos launches and the first commercial-free 24 hour, internet-only radio stations, Radio HK and NetRadio start broadcasting. Dell and Cisco begin to aggressively use Internet for commercial transactions. eBay is founded by computer programmer Pierre Omidyar as AuctionWeb. 1998: Electronic postal stamps can be purchased and downloaded for printing from the Web. 1999: sold for US $7.5 million to eCompanies, which was purchased in 1997 for US $149,000. The peer-to-peer filesharing software Napster launches. ATG Stores launches to sell decorative items for the home online. 2000: The dot-com bust. 2002: eBay acquires PayPal for $1.5 billion [2]. Niche retail companies CSN Stores and NetShops are founded with the concept of selling products through several targeted domains, rather than a central portal. 2003: posts first yearly profit. 2007: acquired by R.H. Donnelley for $345 million[3]. 2008: US eCommerce and Online Retail sales projected to reach $204 billion, an increase of 17 percent over 2007[4].

[edit] Business applications Some common applications related to electronic commerce are the following: • • • • • • • • • • •

Email Enterprise content management Instant messaging Newsgroups Online shopping and order tracking Online banking Online office suites Domestic and international payment systems Shopping cart software Teleconferencing Electronic tickets

[edit] Government regulations In the United States, some electronic commerce activities are regulated by the Federal Trade Commission (FTC). These activities include the use of commercial e-mails, online advertising and consumer privacy. The CAN-SPAM Act of 2003 establishes national standards for direct marketing over e-mail. The Federal Trade Commission Act regulates all forms of advertising, including online advertising, and states that advertising must be truthful and non-deceptive.[5] Using its authority under Section 5

of the FTC Act, which prohibits unfair or deceptive practices, the FTC has brought a number of cases to enforce the promises in corporate privacy statements, including promises about the security of consumers’ personal information.[6] As result, any corporate privacy policy related to e-commerce activity may be subject to enforcement by the FTC. The Ryan Haight Online Pharmacy Consumer Protection Act of 2008, which came into law in 2008, amends the Controlled Substances Act to address online pharmacies. [7]

[edit] Forms Contemporary electronic commerce involves everything from ordering "digital" content for immediate online consumption, to ordering conventional goods and services, to "meta" services to facilitate other types of electronic commerce. On the consumer level, electronic commerce is mostly conducted on the World Wide Web. An individual can go online to purchase anything from books or groceries, to expensive items like real estate. Another example would be online banking, i.e. online bill payments, buying stocks, transferring funds from one account to another, and initiating wire payment to another country. All of these activities can be done with a few strokes of the keyboard. On the institutional level, big corporations and financial institutions use the internet to exchange financial data to facilitate domestic and international business. Data integrity and security are very hot and pressing issues for electronic commerce today.

Electronic business Electronic Business, commonly referred to as "eBusiness" or "e-Business", may be defined as the utilization of information and communication technologies (ICT) in support of all the activities of business. Commerce constitutes the exchange of products and services between businesses, groups and individuals and hence can be seen as one of the essential activities of any business. Hence, electronic commerce or eCommerce focuses on the use of ICT to enable the external activities and relationships of the business with individuals, groups and other businesses [1]. Louis Gerstner, the former CEO of IBM, in his book, Who Says Elephants Can't Dance? attributes the term "e-Business" to IBM's marketing and Internet teams in 1996. Electronic business methods enable companies to link their internal and external data processing systems more efficiently and flexibly, to work more closely with suppliers and partners, and to better satisfy the needs and expectations of their customers. In practice, e-business is more than just e-commerce. While e-business refers to more strategic focus with an emphasis on the functions that occur using electronic capabilities, e-commerce is a subset of an overall e-business strategy. E-commerce

seeks to add revenue streams using the World Wide Web or the Internet to build and enhance relationships with clients and partners and to improve efficiency using the Empty Vessel strategy. Often, e-commerce involves the application of knowledge management systems. E-business involves business processes spanning the entire value chain: electronic purchasing and supply chain management, processing orders electronically, handling customer service, and cooperating with business partners. Special technical standards for e-business facilitate the exchange of data between companies. E-business software solutions allow the integration of intra and inter firm business processes. E-business can be conducted using the Web, the Internet, intranets, extranets, or some combination of these.

Contents [hide] • • • • •

1 Subsets 2 Models o 2.1 Classification by provider and consumer 3 See also 4 References 5 External links o

5.1 Wikibooks

Subsets Applications can be divided into three categories: 1. Internal business systems: o customer relationship management o enterprise resource planning o document management systems o human resources management 2. Enterprise communication and collaboration: o VoIP o content management system o e-mail o voice mail o Web conferencing o Digital work flows (or business process management) 3. electronic commerce - business-to-business electronic commerce (B2B) or business-to-consumer electronic commerce (B2C): o internet shop o supply chain management o online marketing o offline marketing

[edit] Models When organizations go online, they have to decide which e-business models best suit their goals. [2] A business model is defined as the organization of product, service and information flows, and the source of revenues and benefits for suppliers and customers. The concept of e-business model is the same but used in the online presence. The following is a list of the currently most adopted e-business models such as: • • • • • • • • • • • •

E-shops E-commerce E-procurement E-malls E-auctions Virtual Communities Collaboration Platforms Third-party Marketplaces Value-chain Integrators Value-chain Service Providers Information Brokerage Telecommunication

[edit] Classification by provider and consumer Roughly dividing the world into providers/producers and consumers/clients one can classify e-businesses into the following categories: • • • • • • • • •

business-to-business (B2B) business-to-consumer (B2C) business-to-employee (B2E) business-to-government (B2G) government-to-business (G2B) government-to-government (G2G) government-to-citizen (G2C) consumer-to-consumer (C2C) consumer-to-business (C2B)

It is notable that there are comparably less connections pointing "upwards" than "downwards" (few employee/consumer/citizen-to-X models).

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