Define percepts, actions, and sensors in the context of agents.

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An agent architecture refers to the underlying design and structure of an intelligent agent that defines how it perceives, processes, decides, and acts within an environment. It is essentially the blueprint that determines how the agent’s components—such as sensors, decision-making mechanisms, memory, and actuators—work together to achieve its goals.

Key aspects of agent architecture:

1. Perception

   • The agent collects information about the environment through sensors (or inputs).

2. Decision-making

   • Based on perceptions, the agent uses reasoning, rules, or learning models to decide what action to take.

3. Action

   • The chosen action is executed through actuators (or outputs) to affect the environment.

4. Feedback Loop

   • After acting, the agent observes the results of its actions, learns from them, and adjusts future behavior.

Common Types of Agent Architectures

1. Reactive Architecture

   • Acts directly based on current perceptions.

   • Fast but limited; does not store past information.

   • Example: Simple robots that avoid obstacles using sensors.

2. Deliberative (Model-based) Architecture

   • Uses a model of the environment, reasoning, and planning to decide actions.

   • More intelligent but computationally heavier.

   • Example: Path-planning in self-driving cars.

3. Hybrid Architecture

   • Combines reactive and deliberative approaches.

   • Balances speed with intelligence.

   • Example: Modern AI assistants or autonomous drones.

4. Learning-based Architecture

   • Uses machine learning to improve performance over time.

   • Example: Recommendation systems, reinforcement learning agents.

✅ In short: An agent architecture is the framework that defines how an intelligent agent senses, thinks, and acts in its environment. It can be reactive, deliberative, hybrid, or learning-based depending on complexity and goals.

Read more :

Define percepts, actions, and sensors in the context of agents.

Explain the difference between symbolic agents and LLM-based agents.

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