AI agents learn and make decisions by trying things out and getting better at them, just like you do when you’re learning to ride a bike.
Imagine you're playing a game where you guess a number between 1 and 10. Every time you guess wrong, the game gives you a hint: "Too high!" or "Too low!" At first, you might guess randomly, but after a few tries, you start to get smarter about your guesses. That’s how AI agents learn, they try different answers, then use the hints (called feedback) to improve their next try.
How They Make Decisions
When it's time to decide what to do next, AI agents look at all the information they’ve gathered so far. It's like when you're choosing which path to take in a maze, you remember which turns led you closer to the exit and use that memory to pick the best next step.
Sometimes, AI agents even play games with themselves! They imagine different choices and see which ones lead to the best results, it’s like practicing for a test by doing sample questions over and over again.
Examples
- A robot learns to sort toys by trial and error until it gets the order right.
- An app suggests songs based on what you’ve listened to before.
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See also
- What are multiple stakeholders?
- How Does Ancient Philosophy Influence Modern Decision-Making?
- How Do We Decide What Is Fair?
- How do cognitive biases influence our everyday decision-making?
- How Does Humor Influence Decision-Making in Politics?