In the previous blog, we have discussed the three popular uninformed search strategies: BFS, uniform-cost search and DFS, along with their advantages and disadvantages.

In this blog, we will discuss three more uninformed search algorithms, where two of them is intended to solve the infinity-depth problem of DFS, and the third one is a small improvisation on the idea of searching from the source to the destination.

Note: In the previous blog, you might have noticed this symbol occurring in some of the expressions: ^. This symbol refers to the mathematical expression “raised to the power of”. *a^b* means ‘*a*…

Previously, we have discussed on the definition and formulation of the problem, and introduced the concept of general search strategies. In this post, we will discuss on some of the popular classical search algorithms, mainly focusing on the uninformed search algorithms.

Based on the information about the problem available for the search strategies, we can classify the search algorithms into uninformed and informed (or heuristic) search algorithms. For the **uninformed search algorithms**, the strategies have no additional information about the states *beyond that provided by the **problem definition*. All they can do is generate successors and differentiate between goal and…

In the previous series of blogs, we have seen the different structures of the agents based on the nature of the environment it is operating in. In the current series, we will discuss more on the goal-based agent, and the search algorithms that gives the ‘solution’ to the ‘problem’ in these agents.

As mentioned previously, these blogs are very similar to the book “Artificial Intelligence: A Modern Approach”. In fact, this series can be seen as a shorthand version of the book.

We have seen that the reflex agents, whose actions are a direct mapping from the states of the…

In my previous blog post, I have discussed on the Nature of Environment that determines the design of the agent. In this post, we will discuss about the different kinds of agent programs and how to convert them into learning agents that can improvise their agent function and generate better actions.

The agent program takes in the current percept of the environment from the sensors of the agent and returns an action to be performed by the actuators. If you need to depend on the entire percept sequence, the agent will have to remember the percepts.

In a simple **Table-driven…**

We have seen a drastic increase in the demand for Artificial Intelligence and Machine Learning. But before we dive into this magnificent field, let us learn about what an agent is, and the basic designs of an agent, corresponding to its environment.

This is the first of the series of blogs that is intended to give a general idea on the popular search algorithms. These blogs will have a striking similarity with the book “Artificial Intelligence: A Modern Approach”. In fact, this series can be seen as a shorthand version of the book.

An **agent** is anything which can perceive…

AI Enthusiast. Loves Mathematics. Writing Short stories and Quotes are my hobbies.