Introduction to Search Algorithms
Search Algorithms play a crucial role in finding out the best optimal solutions among the tonnes of data. In the millions of searches per day, AI applications are efficient in accomplishing the search tasks.
Have you thought about how a search task is completed within a second? Let’s take a look at the example of a Google search, you entered the term “English class online” and at the moment within a second Google, ended up with the best answers for you.
Here is the role of Search Algorithms comes into play!
Search Algorithms are integrated with AI agents as well as ML applications, enabling them to speed up the time and result in the best solution. But is it enough to say for accomplishing the millions of searches per day?
What is Search Algorithms?
A search algorithm is an algorithm that enables AI agents and Machine Learning applications to search a query from their database.
It contains three terms; search space, start goal, and end goal. A search algorithm implements its algorithms to apply the search function upon AI agents and enables them to transform the start goal into an end goal by finding the desired solution.
In simple language, search algorithms work as same as our mind works to find a word from the dictionary.
E.g., search “plastic toy” in Google, and the algorithms search from the Google Search Engine database and result in the desired/relevant answers.
Important of Search Algorithms
- Solving Problems – The search algorithm applies its search function to AI agents and ML applications that allow them to search the query. The search algorithm follows search methods such as applying logical search, problem-solving definitions, etc., that allow the search function to take a deep search and make it easy to clarify the desired answer regarding the user query.
- Search Programming – Through the Search algorithms umpteen AI applications and AI tasks can be programmed to search the desired output. It can make it easy for AI applications to enhance their search power through integrated search algorithms as it speeds up searching about the query in large amounts of datasets.
- Goal-based Agents – A goal-based agents refer to Search algorithms that enhance the power of goal-based agents and help to formulate effective operations of the agents that allow them to release the best line of actions to search the sequence of relevant answers.
- Support Production Systems – Search algorithms are useful for support production systems because they are actually developed by AI technology that is based on rules designed by AI applications which helps the production systems to work with sequences of actions and result in the best optimal solution among the solutions.
- Neural Network Systems – Neural Networks are familiar with AI technology and are connected with search algorithms through neural layers such as the inside layer, outside layer, and inter-connected layer that help in mapping with search algorithms to return the best-desired results.
Properties of Search Algorithms
- Completeness – Completeness refers to the procedure of finding at least one effective & complete solution among a series of multiple solutions. It completes the procedure when search algorithms connect with the database and search for at least one term given under the user query.
- Optimality – Optimality refers to the best optimal solution resulting in the lowest cost path. With the integration of search algorithms, AI applications become capable of searching for the best optimal solution among the available solutions.
- Time Complexity – Time complexity refers to the maximum time taken by the AI agents to accomplish the search tasks and how much time is to be taken based on the complexity of the query.
- Space Complexity – Space complexity refers to the storage and maximum space needed to accomplish the search task. To accomplish a task how much storage is needed is based on the complexity of the task.
How do Search Algorithms Work?
Before the implementation of Search Algorithms, there are a few terms related to the search tasks that play an important role in search accomplishment to map the search function with search algorithms.
However, search algorithms are a broad concept but mainly divided into two categories:
Defining the problem:
- Initial State: Initial State refers to the state in which the search task takes place in the search function. It is the first step taken by the search function toward the search algorithms.
- State Space: State Space refers to the possible initial states that can be taken into account by the search algorithms and be followed from the initial state to the series of the state.
- Actions: Actions define the initial actions performed by the search algorithm to accomplish the task. Actions including terms such as steps, operations, and activities are taken into consideration by the AI agent to refine the search task and present the optimal solution.
- Goal State: The goal state is defined as the endpoint or the desired output an AI agent is looking for regarding the user-entered query. Through search algorithms, an AI agent becomes capable of reaching the endpoint of the task.
- Goal Test: The goal Test is conducted by the search algorithms to determine whether the current state is the goal state or not. The test is accomplished by a search function based on various search-related parameters such as considering user-entered query, partial term from query, etc.
- Path Cost: Path cost is defined as the cost incurred by the AI agent regarding the task accomplishment by the search algorithms.
After following the all steps by an AI agent through the search function, it comes to the role of accomplishing the search space.
Searching in the Space
A search space is defined as the storage or the space utilized by the AI agent for finding the query. Insightly, a search space includes a search tree consisting of possible search solutions as a hierarchy that is used to initiate the configuration of the search tree. An initial state is considered as the root of the search tree, actions are considered as the branches of the search tree, and the desired output is considered as the search tree.
When a solution is searched by the AI agent then Search Algorithms are considered to follow all steps starting from the Initial state and at each step, a test is taken to verify whether the solution is the optimal goal state (desired output) or not. If the AI agent is not found with the desired goal state, then the AI agent considers continuing all the steps again by the Search Algorithms until the best optimal goal state is found.
In today’s world where everything is people looking for attaining by the search point is possible only by the AI technology that has made it possible for us to ping back a search and find out what we are looking for! Search engines like Google and Bing, are using the Search Algorithms broadly and enable their Artificial Intelligence agents to find from their large datasets. This is all made possible only by using the search algorithms being followed from the initial state to the goal state.