Search Algorithms in Artificial Intelligence

22 Apr 2020

Artificial Intelligence (AI), which is the study of rational agents and in essence, it strives to solve problems of enormous combinational complexity, with the assistance of agents. Since there can be more than one solution to a problem, these agents search space for all combinations and use approaches to find the shortest path or a suitable way to reach the final goal. Hence, Search, along with agents, plays a key role in Artificial Intelligence. From providing a conceptual backbone to various searching strategies and algorithms to performing systematic exploration of alternatives, search ensures that the AI machine/system function accurately.


The concept of search in AI isn’t limited to this. It consists of various search algorithms, which are used in the process of problem-solving and finding a solution, once agents perceive the world and make assumptions.


But!
What is the search algorithm? How many types of search algorithms are there? Why are they so crucial for problem-solving?

It is these questions and more that we are going to answer in the following discussion on Search Algorithms. However, before we move on to search algorithms, let’s get a deeper insight into the search in AI and how it works.

Defining Search in Artificial Intelligence:

Search, be it in AI or in general, is a step-by-step process of solving a search problem, in a particular given search space. It accomplishes this by going through various factors of the search problem, which are:


  • Search Space:The initial state of the search problem, search space represents all the possible sets of solutions that a system might have.
  • Start State:It is from this state that the agents begin the process of search.
  • Goal Test:The final state of the search problem, it observes the current state of problem space and returns whether the goal state has been achieved or not.

Once these factors are completed, the solution to the search problems is provided in a sequence of actions, which is known as a plan, which transforms the start state to the goal state. It is here that the role of search algorithms begins, as it helps achieve the plan.

What are Search Algorithms?

Above, we mentioned the importance of Search in AI and how it helps agents perform their basic tasks and reach the final goal. However, it is important to comprehend that without search algorithms, AI machines, be it a virtual assistant or a self-driving car, cannot execute search and find the most suitable solution or route. These algorithms are used to assess problem space and various sequence of actions.


In artificial intelligence, the importance of search algorithms is immense, as they work in the background and help agents achieve the final goal after assessing various scenarios and alternatives.

Properties of Search Algorithms:

However, what makes the search algorithms efficient for this process? Well, there are four essential properties of a search algorithm that ensures its efficiency. These are:

  • Complete: A search algorithm is termed complete as it ensures a return of solution, even in cases where there is at least any solution that exists for any random input.
  • Optimal: It helps deliver optimal solutions, as it to provide the best solution among all the available alternatives.
  • Time Complexity: It is the measure of time taken to complete a task, by a search algorithm as well as the maximum number of nodes created.
  • Space Complexity: Indicates the maximum storage space required during the search, depending on the problem’s complexity.

Types of Search Algorithms in Artificial Intelligence:

In Artificial Intelligence, the types of search algorithms are numerous. However, these are categorized into two categories, which vary from each other excessively. While some are implemented using a priority queue or root note, there are some that have either closed list or an open list of nodes.


These different types of search algorithms are:


a.) Uninformed Search:

Uniformed Search, which is also known as Blind search or Brute-Force Search, does not have any additional information or domain knowledge that can help it reach the goal, other than the one provided in the problem definition.


This type of search algorithm is only focused on reaching the goal and achieving success and can be applied to a variety of search algorithms, as they are not concerned about the target problem.


This search method is further divided into the following types:


  • Breadth-First Search
  • Uniform Cost Search
  • Depth First Search
  • Iterative Deepening Search
  • Bidirectional Search

b.) Informed Search:

Also known as Heuristic Search, this type of search algorithm uses the domain knowledge and follows a heuristic function that estimates the cost of the optimal path between two states as well as how close a state is to the goal.


Informed search is divided into two types of search:


  • Greedy Search
  • A* Tree Search
  • A* Graph Search

What is the Difference Between Uninformed & Informed Search Strategies?

To help you comprehend the difference between uninformed search and informed search strategies, we are here with a detailed comparison of the two, wherein we highlight some important components of the two categories of search algorithms.


Uninformed Search

  • Uninformed Search does not require any additional information to reach the solution.
  • Other Names: Blind or Brute-Force Search.
  • Compared to Informed Search, the cost of problem-solving is high here.
  • It is comparatively less efficient.
  • Can handle large search problems.
  • Slower than an Informed search in finding a solution & uses more computation.
  • Is categorized into:


    • Breadth-First Search
    • Uniform Cost Search
    • Depth First Search
    • Iterative Deepening Search
    • Bidirectional Search

Informed Search

  • Informed Search requires and uses knowledge to traverse search and find the solution.
  • Other names: Heuristic Search.
  • This cost of problem-solving is low or optimal here.
  • Highly efficient, as it time & cost-effective.
  • Cannot handle large search problems.
  • Finds solutions quickly and uses less computation.
  • Is categorized into:


    • Greedy Search
    • A* Tree Search
    • A* Graph Search

Conclusion:

When it comes to problem-solving, artificial intelligence is highly dependent on search algorithms and its various types to reach the goal and perform a task. Without these algorithms, artificial intelligence won’t be able to autonomous, human-like, and adaptable. Hence, search algorithms, along with Intelligent Agents, are the building blocks of AI, which too, are evolving with this futuristic technology.

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