A rational agent is a computer program that makes independent decisions to achieve goals, using information about its environment. The complexity of the agent depends on the task, and success is measured by the outcome. Rational agents are not omniscient and cannot predict every outcome. Programming courses teach students to create rational agents, which can be used in various fields.
A rational agent is a computer program capable of making autonomous decisions to achieve desired goals. Such programs may gather information about their environment to gather as much data as possible to support their decisions. They can also evaluate the result to determine if the final choice was good and how they could improve the results in the future. Designing rational agents requires knowledge of computer programming and the ability to develop patterns and preferences within a program.
The level of complexity involved may depend on the type of task a program is intended to perform. Some rational agents are simple and can rely heavily on a very basic model. For example, the rational agent might be responsible for checking outgoing mail to ensure that shipping information is complete, to reduce the risk of items being returned to sender. You can use a template of what the addresses should look like to optically scan and accept or reject items for mailing.
More complex agents may need to make more decisions to act on their environments or may require complex models to help them identify specific problems in a given situation. The rational agent’s goal is to select the most optimal outcome, given a set of options and a specific situation. She can measure success based on the response and may be able to learn from this to change behaviors in the future. A rational agent at a nuclear power plant responsible for controlling temperatures in the reactor, for example, knows what affects internal temperatures and how it can regulate them if temperatures rise or fall outside a set range.
Success is not always possible because rational agents are not omniscient. They cannot predict every possible outcome and may not be able to compensate for events beyond their control. When a problem occurs, the agent can evaluate it to find out what happened and if it could have been predicted. If the instruments on a weather balloon had been hit by a meteor, for example, the automated program that selected a launch site and launched the balloon could not have factored this into its calculations. The launch may have failed, but not by anything the rational agent did.
Computer programming courses sometimes include discussions of rational agents, along with simple programming assignments for students to create their own. More complex programs can be developed in information technology, science, medicine, and other fields where automated actors may be needed. The greater the complexity, the greater the functionality.
Protect your devices with Threat Protection by NordVPN
