Typically, an agent program, using parameters the user has provided, searches all or some part of the internet, gathers information the user is interested in and presents it to them on a periodic or requested basis. Data intelligent agents can extract any specifiable information, such as included keywords or publication date. In agents that employ artificial intelligence (AI), user input is collected using sensors, like microphone or cameras, and agent output is delivered through actuators, like speakers or screens. The practice of having information brought to a user by an agent is called push technology.
Common characteristics of intelligent agents are adaptation based on experience, real time problem solving, analysis of error or success rates and the use of memory-based storage and retrieval.
For enterprises, intelligent agents can be used for applications in data mining, data analytics and customer service and support (CSS). Consumers can also use intelligent agents to compare the prices of similar products and notify the user when a website update occurs.
Intelligent agents are also similar to software agents which are autonomous computer programs.
Types of intelligent agents
Types of intelligent agents are defined by their range of capabilities and degree of intelligence:
· Reflex agents: These agents function in a current state, ignoring past history. Responses are based on the event-condition-action rule (ECA rule) where a user initiates an event and the agent refers to a list of pre-set rules and pre-programmed outcomes.
· Model-based agents: These agents choose an action in the same way as a reflex agent, but they have a more comprehensive view of the environment. A model of the world is programmed into the internal system that incorporates the agent's history.
· Goal-based agents: These agents expand upon the information model-based agents store by also including goal information, or information about desirable situations.
· Utility-based agents: These agents are similar to goal-based agents but provide an extra utility measurement which rates each possible scenario on its desired result and chooses the action that maximizes the outcome. Rating criteria examples could be the probability of success or the resources required.
· Learning agents: These agents have the ability to gradually improve and become more knowledgeable about an environment over time through an additional learning element. The learning element will use feedback to determine how performance elements should be changed to improve gradually.
Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
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