**Artificial Intelligence**

**History and Application**

**Types of AI**

**Agents in AI**

**Types of AI Agents**

**Agent Environment**

**Turing Test in AI**

**Search Algorithms**

**Uninformed Search Algorithms**

**Informed Search Algorithms**

**Hill Climbing Algorithm**

**Means Ends Analysis**

**Adversarial Search**

**Mini-Max Algorithm**

**Alpha-Beta Pruning**

**Knowledge-based Agent**

**Knowledge Representation**

**Knowledge Representation Techniques**

**Propositional Logic**

**Rules of Inference**

**The Wumpus World**

**Knowledge Base for Wumpus World**

**First Order Logic**

**Knowledge Engineering in FOL**

**Inference in FOL**

**Unification in FOL**

**Resolution in FOL**

**Forward and Backward Chaining**

**Reasoning in AI**

**Bayesian Belief Network**

# Search Algorithms in Artificial Intelligence

**In this page we will learn What is search algorithms in ai?, Problem solving agents, Search Algorithm Terminologies, Properties of Search Algorithms, Types of search algorithms, Uninformed / Blind Search, Informed Search.**

## What is search algorithms in ai?

Search algorithms are one of the most important areas of AI. This topic will describe all about the search algorithms in AI.

## Problem-solving agents:

Search techniques are universal problem-solving methods in Artificial Intelligence. These search strategies or algorithms were generally employed by rational agents or problem-solving agents in AI to solve a given problem and provide the best outcome. Goal-based agents that use atomic representation are problem-solving agents. We will learn a variety of problem-solving search methods in this area.

## Search Algorithm Terminologies:

**Search:**Searching is a method of solving a search problem in a given search space by following a set of steps. There are three basic variables that can contribute to a search problem:

**Search Space:**A search space is a collection of probable solutions that a system could have.**Search State:**It's the starting point for the agent's quest.**Goal test:**This is a function that looks at the current state and returns whether or not the goal state has been achieved.

**Search tree:**The term "search tree" refers to a tree representation of a search problem. The root node, which corresponds to the initial state, is at the top of the search tree.**Actions:**It gives the agent a description of all the actions that are available to him.**Transition model:**A transition model can be used to represent a description of what each action does.**Path Cost:**It is a function that gives each path a numeric cost.**Goal Node:**It is an action sequence that connects the start node and the goal node.**Optimal Solution:**If a solution has the lowest cost of all the solutions, it is said to be the optimal solution.

## Properties of Search Algorithms:

The four most important properties of search algorithms to compare their efficiency are as follows:

**Completeness:**If a search method guarantees to return a solution if at least one solution exists for any random input, it is said to be complete.**Optimality:**A solution obtained for an algorithm is considered to be optimal if it is guaranteed to be the best solution (lowest route cost) among all other solutions.**Time Complexity:**It is a measurement of how long it takes an algorithm to finish a task.**Space Complexity:**The maximum storage capacity required at any time during the search, as measured by the problem's complexity.

## Types of search algorithms:

We can divide search algorithms into uninformed (Blind search) and informed (Heuristic search) algorithms based on the search difficulties.

## Uninformed / Blind Search:

The uninformed search contains no domain knowledge for example, closeness, the location of the goal. It works in a brute-force manner since it simply contains instructions on how to traverse the tree and locate leaf and goal nodes. Uninformed search is sometimes known as blind search since it searches a search tree without any knowledge of the search space, such as initial state operators and goal tests. It goes through each branch of the tree until it reaches the destination node.

### It can be divided into five main types:

Before learning about Artificial Intelligence, you should have a basic understanding of the following to be able to grasp the concepts easily:

- Breadth-first search
- Uniform cost search
- Depth-first search
- Iterative deepening depth-first search
- Bidirectional Search

## Informed Search

Domain knowledge is used by informed search algorithms. Problem information is available in an educated search, which can help steer the search. Informed search tactics are more likely to find a solution than uninformed search strategies. Heuristic search is another name for informed search.

A heuristic is a method for finding a good answer in a fair amount of time, even if the optimal solution isn't always guaranteed.

Informed search can solve a lot of complicated problems that can't be solved any other way.

A traveling salesman issue is an example of an informed search algorithm.

- Greedy Search
- A* Search