Agents and Types
In artificial intelligence, an agent is any entity that perceives its environment through sensors and acts upon that environment through actuators. The fundamental formula defining an agent's function is:
Agent = Perception + Action.
At any time, an agent gathers perceptual inputs from its environment and produces actions that influence that environment. Understanding the characteristics of agents is crucial because they serve as foundational constructs in AI systems, enabling interaction with their surroundings and goal-oriented behaviors.
This section categorizes agents based on their complexity and capabilities into five main types:
- Simple Reflex Agents: Operate solely on the current percept using condition-action rules. For instance, a thermostat that activates the heater if it detects a temperature drop below a specific threshold.
- Model-Based Reflex Agents: Maintain an internal state to handle partially observable environments, using models of the world. An example is a robot vacuum that remembers the areas it has already cleaned.
- Goal-Based Agents: Act with the intention of achieving specific goals, utilizing search and planning strategies—like a chess-playing AI that aims to achieve checkmate.
- Utility-Based Agents: Focus on maximizing a utility function, navigating trade-offs between competing goals, such as a self-driving car that optimizes for speed, safety, and fuel efficiency.
- Learning Agents: Improve their performance based on experience and have components for learning from their environment. For instance, recommendation engines that adapt based on user interaction.
Recognizing these agent types helps in designing intelligent systems that operate effectively within complex environments.