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In machine learning and artificial intelligence research, the “rational agent” is a concept that guides the use of game theory and decision theory in applying artificial intelligence to various real-world scenarios. The rational agent is a theoretical entity based on a realistic model, that has preferences for advantageous outcomes, and will seek to achieve them in a learning scenario.
One of the best ways to understand rational actors is to take an example of some type of commercial artificial intelligence or machine learning project. Suppose a business wants to understand how people will use a complex navigational space like a drive-through with four lanes, or a complex restaurant layout with multiple tables and chairs. The engineers and data scientists will construct profiles and properties for the rational actors – which are modeled on real-life customers. They will then run the machine learning programs with these rational actors in mind and look at the outputs.
Rational actors can be applied in all sorts of ways to artificial intelligence projects. They help people to understand how theoretical humans might use technologies, and how the technologies can learn about human behavior to help other humans make decisions.