An intelligent agent is primarily used to complement data retrieval tasks, which are traditionally performed manually by humans. Typically, an intelligent agent executes automatically on scheduled time or when manually initiated by the user. It then searches the entire Internet or on user-defined websites to work on the primary search query/request. When a relevancy or match is found, the intelligent agent copies, extracts or lists that data. The collected data is then presented in a raw or report-based format to the user. Some advanced-level intelligent agent utilities use artificial intelligence based data inference matching and retrieval techniques, which allows them to collect higher quality and more relevant data. Popular forms of intelligent agents include shopping agents/bots, news feed/alert agents and Web crawlers.
In, 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 to decide which, if any, action is appropriate. Agents are colloquially known as, from. 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. ) or other computing device. Software Agents may be autonomous or work together with other agents or people. Software agents interacting with people (e.g., environments) may possess human-like qualities such as natural language understanding and speech, personality or embody humanoid form (see ).
Related and derived concepts include (in particular exhibiting some aspect of, such as and ), (capable of modifying the way in which they achieve their objectives), distributed agents (being executed on physically distinct computers), (distributed agents that work together to achieve an objective that could not be accomplished by a single agent acting alone), and (agents that can relocate their execution onto different processors). Nwana's Category of Software Agent The term 'agent' describes a software, an idea, or a concept, similar to terms such as methods, and objects. 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 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. See also: Haag suggests that there are only four essential types of intelligent software agents:. Buyer agents or shopping bots. User or personal agents. Monitoring-and-surveillance agents. Data-mining agents 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. 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 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. Networking and communicating agents Some other examples of current include some filters, game, and server monitoring tools. Indexing bots also qualify as intelligent agents. for browsing the World Wide Web. For serving E-mail, such as Microsoft Outlook.
It communicates with the POP3 mail server, without users having to understand command protocols. It even has rule sets that filter mail for the user, thus sparing them the trouble of having to do it themselves. agent. In Unix-style networking servers, is an HTTP daemon that implements the at the root of the. used to manage telecom devices.
for safety planning or,. Wireless beaconing agent is a simple process hosted single tasking entity for implementing or 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. Design issues 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.).
Dhafer youssef. For software agents to work together efficiently they must share of their data elements. This can be done by having computer systems publish their. The definition of agent processing can be approached from two interrelated directions:.
internal state processing and ontologies for representing knowledge. – standards for specifying communication of tasks Agent systems are used to model real-world systems with 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.
Shopping
Notions and frameworks for agents. (DARPA Agent Markup Language). (Artificial Autonomous Agents Programming Language). (OWL). in systems. (JAT). (JADE).
(arguably an Actor and not Agent oriented paradigm) See also. References.
6Artificial Life
Nwana, H. S. 'Software Agents: An Overview'. Knowledge Engineering Review. Cambridge University Press. 21 (3): 205–244. Schermer, B. W.
Leiden University Press: 140, 205–244. Retrieved 2012-10-30. Wooldridge, M.; Jennings, N. 'Intelligent agents: theory and practice'. Knowledge Engineering Review: 115–152.
Franklin, S.; Graesser, A. University of Memphis, Institute for Intelligent Systems. Archived from on 1996. ^ Wooldridge, Michael J. New York: John Wiley & Sons.
Serenko, A.; Detlor, B. 18 (4): 364–381. Adonisi, M.
![6Artificial Life 6Artificial Life](/uploads/1/2/4/2/124295354/722777777.png)
(PDF) (Diss.). Fac.of Econ.and Mgmt.Sci., Univ.of Pretoria. Serenko, A.; Ruhi, U.; Cocosila, M. Artificial Intelligence & Society: 141–166.
Haag, Stephen (2006). 'Management Information Systems for the Information Age': 224–228. Keystone Click.
Retrieved 2017-09-07. Shirado, Hirokazu; Christakis, Nicholas A. 545 (7654): 370–374. DARKReading from. Retrieved 2017-11-14. External links., Hyacinth S.
Nwana., 11(3):1–40, September 1996. The Foundation for Intelligent Physical Agents.
Java Agent Developing Framework, an Open Source framework developed by Telecom Italia Labs. An Open Source framework to develop SWRL based Agents on top of JADE. A Multi-Agent Platform for Mobile C/C Agents. High-Level Logic (HLL) Open Source Project.
Open source project for PHP and Java developers to write software agents.
When we think of how bots fit into a Customer Service strategy, the typical scenario envisioned is often that of a customer-facing bot, providing customers a conversational support experience powered by automated intelligence. But even with the deployment of a customer-facing bot, most contact centers will still need to support a significant number of customer engagements with the help of a Customer Service Representative (CSR). This post will be the first in a series in which we explore how we can leverage natural, conversational experiences powered by automated intelligence to empower the CSR, by building a contextually-aware Intelligent Agent Assistant, integrated into the Unified Service Desk agent desktop.