Introduction, types and structure of intelligent agents

Intelligent Agents

Introduction

An Intelligent Agent (IA) is an autonomous entity (software or machine) that perceives its environment through sensors and acts upon the environment using actuators to achieve specific goals.

In Artificial Intelligence (AI), agents are programs or machines that can make decisions, learn, and perform tasks without direct human intervention.

• Example:

  • A self-driving car → Sensors: cameras, radar; Actuators: steering, brakes.

  • A chatbot → Sensors: user text input; Actuators: text response.

Thus, intelligent agents are at the core of AI systems as they bridge the perception-action cycle.

---

Types of Intelligent Agents

Intelligent agents are classified into several categories based on their capabilities and complexity:

1. Simple Reflex Agents

• Act only on the current percept (ignore history).

• Follow a condition-action rule (if condition → then action).

• Example: A thermostat that turns the heater ON if temperature < 20°C.

2. Model-Based Reflex Agents

• Maintain an internal model of the environment.

• Consider both current percepts and past history for decision-making.

• Example: Self-driving car considering road conditions and previous vehicle states.

3. Goal-Based Agents

• Make decisions based on achieving specific goals.

• Use search and planning algorithms to choose the best actions.

• Example: GPS navigation system planning the shortest route to a destination.

4. Utility-Based Agents

• Not only aim to achieve goals but also maximize performance or utility.

• Make trade-offs between multiple goals for best outcomes.

• Example: Airline ticket booking system recommending cheapest + fastest option.

5. Learning Agents

• Continuously learn and improve performance from experience.

• Have components for learning, performance, and feedback.

• Example: Spam email filter that improves as it learns from user input.

---

Structure of Intelligent Agents

The structure of an intelligent agent can be understood as a Perception → Reasoning → Action cycle. It has the following components:

1. Sensors

   • Perceive the environment.

   • Example: Cameras, microphones, text inputs.

2. Actuators

   • Perform actions to affect the environment.

   • Example: Motors, speakers, text output.

3. Agent Program (Decision-Making Unit)

   • Contains the logic and algorithms to map percepts → actions.

   • May include:

     • Knowledge base

     • Inference mechanism

     • Learning module

---

Diagram of an Intelligent Agent Structure

        Environment
            │
        ┌───▼───┐
        │Sensors│  (Percepts/Input)
        └───▲───┘
            │
     ┌──────┴────────┐
     │ Agent Program │  (Decision-making: Rules, Goals, Utility, Learning)
     └──────▲────────┘
            │
        ┌───▼───┐
        │Actuators│ (Actions/Output)
        └────────┘

---

Summary

• Intelligent Agent: An autonomous system that perceives environment (sensors) and acts on it (actuators) to achieve goals.

• Types:

  1. Simple Reflex Agents

  2. Model-Based Reflex Agents

  3. Goal-Based Agents

  4. Utility-Based Agents

  5. Learning Agents

• Structure: Sensors + Agent Program + Actuators working in a continuous perception-action cycle.

---