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Software agent

In computer science, a software agent is a computer program that acts for a user or other program in a relationship of agency, which derives from the Latin agere (to do): an agreement to act on one's behalf. Such "action on behalf of" implies the authority to decide which, if any, action is appropriate. Agents are colloquially known as bots, from robot. They may be embodied, as when execution is paired with a robot body, or as software such as a chatbot executing on a phone (e.g. Siri) or other computing device. Software agents may be autonomous or work together with other agents or people. Software agents interacting with people (e.g. chatbots, human-robot interaction environments) may possess human-like qualities such as natural language understanding and speech, personality or embody humanoid form (see Asimo).

Related and derived concepts include intelligent agents (in particular exhibiting some aspects of artificial intelligence, such as reasoning), autonomous agents (capable of modifying the methods of achieving their objectives), distributed agents (being executed on physically distinct computers), multi-agent systems (distributed agents that work together to achieve an objective that could not be accomplished by a single agent acting alone), and mobile agents (agents that can relocate their execution onto different processors).

Contents

The basic attributes of an autonomous software agent are that agents

  • are not strictly invoked for a task, but activate themselves,
  • may reside in wait status on a host, perceiving context,
  • may get to run status on a host upon starting conditions,
  • do not require interaction of user,
  • may invoke other tasks including communication.
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 host. 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, goal-directed behavior, 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).

Distinguishing agents from programs

All agents are programs, but not all programs are agents. Contrasting the term with related concepts may help clarify its meaning. Franklin & Graesser (1997) discuss four key notions that distinguish agents from arbitrary programs: reaction to the environment, autonomy, goal-orientation and persistence.

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.

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.

Distinguishing intelligent software agents from intelligent agents in AI

  • Intelligent agents (also known as rational agents) are not just computer 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) harv error: no target: CITEREFRussellNorvig2003 (help)

Software agents may offer various benefits to their end users by automating complex or repetitive tasks. However, there are organizational and cultural impacts of this technology that need to be considered prior to implementing software agents.

Organizational impact

Work contentment and job satisfaction impact

People like to perform easy tasks providing the sensation of success unless the repetition of the simple tasking is affecting the overall output. In general implementing software agents to perform administrative requirements provides a substantial increase in work contentment, as administering their own work does never please the worker. The effort freed up serves for a higher degree of engagement in the substantial tasks of individual work. Hence, software agents may provide the basics to implement self-controlled work, relieved from hierarchical controls and interference. Such conditions may be secured by application of software agents for required formal support.

Cultural impact

The cultural effects of the implementation of software agents include trust affliction, skills erosion, privacy attrition and social detachment. Some users may not feel entirely comfortable fully delegating important tasks to software applications. Those who start relying solely on intelligent agents may lose important skills, for example, relating to information literacy. In order to act on a user's behalf, a software agent needs to have a complete understanding of a user's profile, including his/her personal preferences. This, in turn, may lead to unpredictable privacy issues. When users start relying on their software agents more, especially for communication activities, they may lose contact with other human users and look at the world with the eyes of their agents. These consequences are what agent researchers and users must consider when dealing with intelligent agent technologies.

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 of 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.

Buyer agents (shopping bots)

Buyer agents travel around a network (e.g. 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 one-size-fits-all products. Buyer agents are typically optimized to allow for digital payment services used in e-commerce and traditional businesses.

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.
  • Assemble customized news reports for you. There are several versions of these, including 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
  • Facilitate with online job search duties by scanning known job boards and sending the resume to opportunities who meet the desired criteria
  • Profile synchronization across heterogeneous social networks

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, schedules equipment orders to keep costs down, and manages food storage facilities. These agents usually monitor complex computer networks that can keep track of the configuration of each computer connected to the network.

A special case of Monitoring-and-Surveillance agents are organizations of agents used to emulate the Human Decision-Making process during tactical operations. The agents monitor the status of assets (ammunition, weapons available, platforms for transport, etc.) and receive Goals (Missions) from higher level agents. The Agents then pursue the Goals with the Assets at hand, minimizing expenditure of the Assets while maximizing Goal Attainment. (See Popplewell, "Agents and Applicability")

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 many 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.

Networking and communicating agents

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.

  • 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
  • In Unix-style networking servers, httpd is an HTTP daemon that 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,
  • Wireless beaconing agent is a simple process hosted single tasking entity for implementing wireless lock or electronic leash in conjunction with more complex software agents hosted e.g. on wireless receivers.
  • Use of autonomous agents (deliberately equipped with noise) to optimize coordination in groups online.

Software development agents (aka software bots)

Main article: Software bot

Software bots are becoming important in software engineering. An example of a software bot is a bot that automatically repairs continuous integration build failures.

Security agents

Agents are also used in software security application to intercept, examine and act on various types of content. Example include:

  • Data Loss Prevention (DLP) Agents - examine user operations on a computer or network, compare with policies specifying allowed actions, and take appropriate action (e.g. allow, alert, block). The more comprehensive DLP agents can also be used to perform EDR functions.
  • Endpoint Detection and Response (EDR) Agents - monitor all activity on an endpoint computer in order to detect and respond to malicious activities
  • Cloud Access Security Broker (CASB) Agents - similar to DLP Agents, however examining traffic going to cloud applications

Issues to consider in the development of agent-based systems include

  • 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:

  • 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.

Bots can act on behalf of their creators to do good as well as bad. There are a few ways which bots can be created to demonstrate that they are designed with the best intention and are not built to do harm. This is first done by having a bot identify itself in the user-agent HTTP header when communicating with a site. The source IP address must also be validated to establish itself as legitimate. Next, the bot must also always respect a site's robots.txt file since it has become the standard across most of the web. And like respecting the robots.txt file, bots should shy away from being too aggressive and respect any crawl delay instructions.

Notions and frameworks for agents

  1. Nwana, H. S. (1996). "Software Agents: An Overview". Knowledge Engineering Review. 21 (3): 205–244. CiteSeerX10.1.1.50.660. doi:10.1017/s026988890000789x.
  2. Schermer, B. W. (2007). Software agents, surveillance, and the right to privacy: A legislative framework for agent-enabled surveillance(paperback). 21. Leiden University Press. pp. 140, 205–244. hdl:1887/11951. ISBN 978-0-596-00712-6. Retrieved2012-10-30.
  3. Wooldridge, M.; Jennings, N. R. (1995). "Intelligent agents: theory and practice". 10 (2). Knowledge Engineering Review: 115–152.Cite journal requires |journal= ()
  4. Franklin, S.; Graesser, A. (1996). "Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents". Intelligent Agents III Agent Theories, Architectures, and Languages. Lecture Notes in Computer Science. 1193. University of Memphis, Institute for Intelligent Systems. pp. 21–35. doi:10.1007/BFb0013570. ISBN 978-3-540-62507-0.
  5. Wooldridge, Michael J. (2002). An Introduction to Multiagent Systems. New York: John Wiley & Sons. p. 27. ISBN 978-0-471-49691-5.
  6. Serenko, A.; Detlor, B. (2004). "Intelligent agents as innovations"(PDF). 18 (4): 364–381.Cite journal requires |journal= ()
  7. Adonisi, M. (2003). "The relationship between Corporate Entrepreneurship, Market Orientation, Organisational Flexibility and Job satisfaction"(PDF) (Diss.). Fac.of Econ.and Mgmt.Sci., Univ.of Pretoria.Cite journal requires |journal= ()
  8. Serenko, A.; Ruhi, U.; Cocosila, M. (2007). "Unplanned effects of intelligent agents on Internet use: Social Informatics approach"(PDF). 21 (1–2). Artificial Intelligence & Society: 141–166.Cite journal requires |journal= ()
  9. Haag, Stephen (2006). "Management Information Systems for the Information Age": 224–228.Cite journal requires |journal= ()
  10. "Maximize Your Business Impact | How to Use Facebook Chatbots". Keystone Click. 2016-08-26. Retrieved2017-09-07.
  11. Shirado, Hirokazu; Christakis, Nicholas A (2017). "Locally noisy autonomous agents improve global human coordination in network experiments". Nature. 545 (7654): 370–374. Bibcode:2017Natur.545..370S. doi:10.1038/nature22332. PMC5912653. PMID 28516927.
  12. Lebeuf, Carlene; Storey, Margaret-Anne; Zagalsky, Alexey (2018). "Software Bots". IEEE Software. 35: 18–23. doi:10.1109/MS.2017.4541027. S2CID 31931036.
  13. Urli, Simon; Yu, Zhongxing; Seinturier, Lionel; Monperrus, Martin (2018). "How to design a program repair bot? Insights from the Repairnator Project". Proceedings of the 40th International Conference on Software Engineering Software Engineering in Practice - ICSE-SEIP '18. pp. 95–104. arXiv:1811.09852. doi:10.1145/3183519.3183540. ISBN 9781450356596. S2CID 49237449.
  14. https://info.digitalguardian.com/rs/768-OQW-145/images/SC-Labs-DLP-GROUP-TEST-AND-DG-REVIEW.pdf?field_resource_type_value=analyst-reports
  15. "How to Live by the Code of Good Bots". DARKReading from Information World. 27 September 2017. Retrieved2017-11-14.

Software agent
Software agent Language Watch Edit In computer science a software agent is a computer program that acts for a user or other program in a relationship of agency which derives from the Latin agere to do an agreement to act on one s behalf Such action on behalf of implies the authority to decide which if any action is appropriate 1 2 Agents are colloquially known as bots from robot They may be embodied as when execution is paired with a robot body or as software such as a chatbot executing on a phone e g Siri or other computing device Software agents may be autonomous or work together with other agents or people Software agents interacting with people e g chatbots human robot interaction environments may possess human like qualities such as natural language understanding and speech personality or embody humanoid form see Asimo Related and derived concepts include intelligent agents in particular exhibiting some aspects of artificial intelligence such as reasoning autonomous agents capable of modifying the methods of achieving their objectives distributed agents being executed on physically distinct computers multi agent systems distributed agents that work together to achieve an objective that could not be accomplished by a single agent acting alone and mobile agents agents that can relocate their execution onto different processors Contents 1 Concepts 1 1 Distinguishing agents from programs 1 2 Intuitive distinguishing agents from objects 1 3 Distinguishing agents from expert systems 1 4 Distinguishing intelligent software agents from intelligent agents in AI 2 Impact of software agents 2 1 Organizational impact 2 2 Work contentment and job satisfaction impact 2 3 Cultural impact 2 4 History 3 Examples of intelligent software agents 3 1 Buyer agents shopping bots 3 2 User agents personal agents 3 3 Monitoring and surveillance predictive agents 3 4 Data mining agents 3 5 Networking and communicating agents 3 6 Software development agents aka software bots 3 7 Security agents 4 Design issues 4 1 Notions and frameworks for agents 5 See also 6 References 7 External linksConcepts EditThe basic attributes of an autonomous software agent are that agents are not strictly invoked for a task but activate themselves may reside in wait status on a host perceiving context may get to run status on a host upon starting conditions do not require interaction of user may invoke other tasks including communication 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 host But unlike objects which are defined in terms of methods and attributes an agent is defined in terms of its behavior 3 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 goal directed behavior 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 Distinguishing agents from programs Edit All agents are programs but not all programs are agents Contrasting the term with related concepts may help clarify its meaning Franklin amp Graesser 1997 4 discuss four key notions that distinguish agents from arbitrary programs reaction to the environment autonomy goal orientation and persistence Intuitive distinguishing agents from objects Edit 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 5 Distinguishing agents from expert systems Edit Expert systems are not coupled to their environment Expert systems are not designed for reactive proactive behavior Expert systems do not consider social ability 5 Distinguishing intelligent software agents from intelligent agents in AI Edit Intelligent agents also known as rational agents are not just computer 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 amp Norvig 2003 harv error no target CITEREFRussellNorvig2003 help Impact of software agents EditSoftware agents may offer various benefits to their end users by automating complex or repetitive tasks 6 However there are organizational and cultural impacts of this technology that need to be considered prior to implementing software agents Organizational impact Edit Work contentment and job satisfaction impact Edit People like to perform easy tasks providing the sensation of success unless the repetition of the simple tasking is affecting the overall output In general implementing software agents to perform administrative requirements provides a substantial increase in work contentment as administering their own work does never please the worker The effort freed up serves for a higher degree of engagement in the substantial tasks of individual work Hence software agents may provide the basics to implement self controlled work relieved from hierarchical controls and interference 7 Such conditions may be secured by application of software agents for required formal support Cultural impact Edit The cultural effects of the implementation of software agents include trust affliction skills erosion privacy attrition and social detachment Some users may not feel entirely comfortable fully delegating important tasks to software applications Those who start relying solely on intelligent agents may lose important skills for example relating to information literacy In order to act on a user s behalf a software agent needs to have a complete understanding of a user s profile including his her personal preferences This in turn may lead to unpredictable privacy issues When users start relying on their software agents more especially for communication activities they may lose contact with other human users and look at the world with the eyes of their agents These consequences are what agent researchers and users must consider when dealing with intelligent agent technologies 8 History Edit 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 of 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 Examples of intelligent software agents EditSee also Intelligent agent Buyer agents shopping bots Edit Buyer agents 9 travel around a network e g 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 one size fits all products Buyer agents are typically optimized to allow for digital payment services used in e commerce and traditional businesses 10 User agents personal agents Edit 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 Assemble customized news reports for you There are several versions of these including 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 Facilitate with online job search duties by scanning known job boards and sending the resume to opportunities who meet the desired criteria Profile synchronization across heterogeneous social networksMonitoring and surveillance predictive agents Edit 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 schedules equipment orders to keep costs down and manages food storage facilities These agents usually monitor complex computer networks that can keep track of the configuration of each computer connected to the network A special case of Monitoring and Surveillance agents are organizations of agents used to emulate the Human Decision Making process during tactical operations The agents monitor the status of assets ammunition weapons available platforms for transport etc and receive Goals Missions from higher level agents The Agents then pursue the Goals with the Assets at hand minimizing expenditure of the Assets while maximizing Goal Attainment See Popplewell Agents and Applicability Data mining agents Edit 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 many 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 Networking and communicating agents Edit 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 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 In Unix style networking servers httpd is an HTTP daemon that 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 Wireless beaconing agent is a simple process hosted single tasking entity for implementing wireless lock or electronic leash in conjunction with more complex software agents hosted e g on wireless receivers Use of autonomous agents deliberately equipped with noise to optimize coordination in groups online 11 Software development agents aka software bots Edit Main article Software bot Software bots are becoming important in software engineering 12 An example of a software bot is a bot that automatically repairs continuous integration build failures 13 Security agents Edit Agents are also used in software security application to intercept examine and act on various types of content Example include Data Loss Prevention DLP Agents 14 examine user operations on a computer or network compare with policies specifying allowed actions and take appropriate action e g allow alert block The more comprehensive DLP agents can also be used to perform EDR functions Endpoint Detection and Response EDR Agents monitor all activity on an endpoint computer in order to detect and respond to malicious activities Cloud Access Security Broker CASB Agents similar to DLP Agents however examining traffic going to cloud applicationsDesign issues EditIssues to consider in the development of agent based systems include 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 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 Bots can act on behalf of their creators to do good as well as bad There are a few ways which bots can be created to demonstrate that they are designed with the best intention and are not built to do harm This is first done by having a bot identify itself in the user agent HTTP header when communicating with a site The source IP address must also be validated to establish itself as legitimate Next the bot must also always respect a site s robots txt file since it has become the standard across most of the web And like respecting the robots txt file bots should shy away from being too aggressive and respect any crawl delay instructions 15 Notions and frameworks for agents Edit DAML DARPA Agent Markup Language 3APL Artificial Autonomous Agents Programming Language GOAL agent programming language Web Ontology Language OWL daemons in Unix like systems Java Agent Template JAT Java Agent Development Framework JADE SARL agent programming language arguably an Actor and not Agent oriented paradigm See also EditAgent architecture Chatbot Hal 9000 Data loss prevention Endpoint detection and responseReferences Edit Nwana H S 1996 Software Agents An Overview Knowledge Engineering Review 21 3 205 244 CiteSeerX 10 1 1 50 660 doi 10 1017 s026988890000789x Schermer B W 2007 Software agents surveillance and the right to privacy A legislative framework for agent enabled surveillance paperback 21 Leiden University Press pp 140 205 244 hdl 1887 11951 ISBN 978 0 596 00712 6 Retrieved 2012 10 30 Wooldridge M Jennings N R 1995 Intelligent agents theory and practice 10 2 Knowledge Engineering Review 115 152 Cite journal requires journal help Franklin S Graesser A 1996 Is it an Agent or just a Program A Taxonomy for Autonomous Agents Intelligent Agents III Agent Theories Architectures and Languages Lecture Notes in Computer Science 1193 University of Memphis Institute for Intelligent Systems pp 21 35 doi 10 1007 BFb0013570 ISBN 978 3 540 62507 0 a b Wooldridge Michael J 2002 An Introduction to Multiagent Systems New York John Wiley amp Sons p 27 ISBN 978 0 471 49691 5 Serenko A Detlor B 2004 Intelligent agents as innovations PDF 18 4 364 381 Cite journal requires journal help Adonisi M 2003 The relationship between Corporate Entrepreneurship Market Orientation Organisational Flexibility and Job satisfaction PDF Diss Fac of Econ and Mgmt Sci Univ of Pretoria Cite journal requires journal help Serenko A Ruhi U Cocosila M 2007 Unplanned effects of intelligent agents on Internet use Social Informatics approach PDF 21 1 2 Artificial Intelligence amp Society 141 166 Cite journal requires journal help Haag Stephen 2006 Management Information Systems for the Information Age 224 228 Cite journal requires journal help Maximize Your Business Impact How to Use Facebook Chatbots Keystone Click 2016 08 26 Retrieved 2017 09 07 Shirado Hirokazu Christakis Nicholas A 2017 Locally noisy autonomous agents improve global human coordination in network experiments Nature 545 7654 370 374 Bibcode 2017Natur 545 370S doi 10 1038 nature22332 PMC 5912653 PMID 28516927 Lebeuf Carlene Storey Margaret Anne Zagalsky Alexey 2018 Software Bots IEEE Software 35 18 23 doi 10 1109 MS 2017 4541027 S2CID 31931036 Urli Simon Yu Zhongxing Seinturier Lionel Monperrus Martin 2018 How to design a program repair bot Insights from the Repairnator Project Proceedings of the 40th International Conference on Software Engineering Software Engineering in Practice ICSE SEIP 18 pp 95 104 arXiv 1811 09852 doi 10 1145 3183519 3183540 ISBN 9781450356596 S2CID 49237449 https info digitalguardian com rs 768 OQW 145 images SC Labs DLP GROUP TEST AND DG REVIEW pdf field resource type value analyst reports How to Live by the Code of Good Bots DARKReading from Information World 27 September 2017 Retrieved 2017 11 14 External links EditSoftware Agents An Overview Hyacinth S Nwana Knowledge Engineering Review 11 3 1 40 September 1996 Cambridge University Press FIPA The Foundation for Intelligent Physical Agents JADE Java Agent Developing Framework an Open Source framework developed by Telecom Italia Labs European Software Agent Research Center SemanticAgent An Open Source framework to develop SWRL based Agents on top of JADE Mobile C A Multi Agent Platform for Mobile C C Agents HLL High Level Logic HLL Open Source Project Open source project KATO for PHP and Java developers to write software agents Retrieved from https en wikipedia org w index php title Software agent amp oldid 1051339916, wikipedia, wiki, book,

books

, library,

article

, read, download, free, free download, mp3, video, mp4, 3gp, jpg, jpeg, gif, png, picture, music, song, movie, book, game, games.