Beth holds a master's degree in integrated marketing communications, and has worked in journalism and marketing throughout her career.
Learning Useful Information
Imagine you've decided to start driving for a cab service. It's a Friday night and you're running your typical route from the nightlife scene to many of the hotels away from the downtown area. You've become accustomed to taking the Maple Street exit to pick up your customers, but tonight you opt for Palace Road for a change of pace. To your surprise, you discover that Palace Road is not only quicker, but you can avoid the accident-prone intersection at Maple and Vine. You decide that, in the future, you'll be sure to take Palace Road.
You probably guessed there's a deeper meaning to the example. It's an illustration of an idea in artificial intelligence (AI) known as a learning agent.
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- 0:04 Learning Useful Information
- 0:42 What Are Learning Agents?
- 1:14 Learning Agent Components
- 1:46 Application
- 2:29 Lesson Summary
What Are Learning Agents?
The cab driver, in the previous example, learned something new by trying something different. Learning agents operate similarly. A learning agent is a tool in AI that is capable of learning from its experiences. It starts with some basic knowledge and is then able to act and adapt autonomously, through learning, to improve its own performance. Unlike intelligent agents that act on information provided by a programmer, learning agents are able to perform tasks, analyze performance, and look for new ways to improve on those tasks - all on their own.
Learning Agent Components
A learning agent is made up of four basic components:
- The performance element chooses what action to take; it later shifts to a new action based on feedback and suggestions for improvement
- The critic element determines the outcome of the action and gives feedback
- The learning element takes the feedback from the critic element and figures out how to make the action better next time
- The problem generator is tasked with developing new experiences for the learning agent to try; this is the piece that helps the agent continue to learn
Now that we know the components of a learning agent, let's look at it in another driving context: a self-driving car. This car will likely be programmed initially with maps and a basic understanding of traffic lights and driving patterns, for example. It uses this information to initiate the performance element of driving from one location to another. The critic element discovers that braking too hard on a wet road causes the vehicle to nearly slide into the car in front of it. The learning element takes that discovery, and determines that braking too hard on a wet surface could cause a potential accident and looks for an alternative solution. The problem generator determines that the car should experiment with different amounts of brake pressure or distance to improve wet weather driving in the future.
A learning agent is a tool in AI that's capable of learning from its experiences. It begins with some basic information and can act and adapt autonomously to improve on its experiences.
There are four basic components of a learning agent:
- The performance element component selects the action to take
- The critic element gives feedback
- The learning element uses the feedback to make the action better next time, and
- The problem generator suggests new experiences for the learning agent to learn from
In the case of a self-driving car, for example, the car can ''learn'' new ways of braking in wet driving conditions to improve the driving performance of the vehicle when it's raining.
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Learning Agents: Definition, Components & Examples
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