Welcome to the Simple Reflex Agents GitHub repository! In this repository, you'll find code and resources related to simple reflex agents, a fundamental concept in artificial intelligence and agent-based systems.

Simple reflex agents are a type of artificial intelligence agent that operates based on a simple set of rules, without any internal memory or ability to plan ahead. These agents perceive their environment through sensors, apply predefined rules or condition-action pairs to determine their actions, and act accordingly to achieve their goals.

This Jupyter Notebook contains the implementation of a simple reflex agent. The agent perceives its environment, analyzes the current state, and selects actions based on predefined rules; facilitated via the Agent class.

The Environement class provides a simple environment in which the agent operates. It includes functions to simulate the environment, update the agent's perception, and provide feedback based on the agent's actions.

You're currently reading the README file! It provides an overview of the repository, instructions for running the code, and additional resources for understanding simple reflex agents.

To get started with the simple reflex agents implementation in this repository, follow these steps:

1. Clone this repository to your local machine:

git clone https://github.com/FaheemGurkani/Simple-Reflex-Agent.git

2. Navigate to the cloned directory:

cd Simple-Reflex-Agent

• Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig - This book provides a comprehensive introduction to artificial intelligence, including discussions on agent-based systems and simple reflex agents.
• Introduction to Agents - A lecture series from the University of Oxford covering various aspects of agents in artificial intelligence, including simple reflex agents.

I hope you find this repository useful for learning about and experimenting with simple reflex agents! If you have any questions or feedback, don't hesitate to reach out.