AI Agents and Environments
In AI, an agent is anything that perceives its environment through sensors and acts upon that environment through actuators. Think of a robot vacuum: it senses walls and dirt (perception), then moves and cleans (action).
An AI agent is a system that perceives its surroundings and takes actions to achieve a goal.
Types of Agents
- Simple Reflex Agent: Acts only on current perception. Example: thermostat – if too cold, turn on heat.
- Model‑Based Reflex Agent: Maintains internal state to handle partial information. Example: a car that remembers its speed.
- Goal‑Based Agent: Acts to achieve a specific goal. Example: navigation system finding the shortest route.
- Utility‑Based Agent: Chooses actions to maximize happiness (utility). Example: stock trading bot maximizing profit.
- Learning Agent: Improves over time from experience. Example: recommendation system that learns your taste.
Environment Types
The environment is everything the agent interacts with. Environments can be:
- Fully observable vs. Partially observable (can the agent see everything?)
- Deterministic vs. Stochastic (does action have guaranteed outcome?)
- Single‑agent vs. Multi‑agent (are there other agents?)
- Discrete vs. Continuous (finite set of actions/states?)
Real‑World Example
A self‑driving car is a complex agent:
- Sensors: cameras, LIDAR, GPS
- Actuators: steering wheel, brakes, accelerator
- Goal: reach destination safely
- Environment: roads, other cars, pedestrians (partially observable, stochastic)
Two Minute Drill
- An AI agent perceives its environment and takes action.
- Types: reflex, model‑based, goal‑based, utility‑based, learning.
- Environments can be observable, deterministic, or multi‑agent.
- A self‑driving car is a real‑world example of a complex agent.
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