Blind Search vs. Heuristic Search in AI

Blind Search vs. Heuristic Search in AI

Brief Introduction to Searches in AI

As artificial intelligence is a domain of computer science and different related engineering branches. The searches are the most important part of computer science, artificial intelligence, and different operation research. Searches are very important when there are software-based agents working in a bigger artificial intelligence model. The searches can be seen in different games and mostly single-player games, like Sudoku, Online crossword puzzles, and Wordle. In these games, the agent shows the user different moves and with the help of different installed algorithms, the agent tells the user about the particular position or move in the game. 


Types of Searches

There are different types of searches used in artificial intelligence, machine learning, deep reinforcement learning, and neural networks. They are commonly divided into two categories. One of them is uninformed and the other is informed searches. 

  • Uninformed Searches are basically a general type of algorithm which have very little information regarding the status of the search. They didn‚Äôt have any information about the present state, previous state, or coming state and space. It is basically called a blind state as well.
  • Informed searches have information regarding the goal and all the paths that come in between the starting point and the goal. The other common names for informed searches are heuristic search, tree search, and graph search.¬†


Blind or Uninformed Search

A general-purpose algorithm-based search that works on the principle of brute force. As discussed earlier, and by the name of the search it becomes obvious that such a search doesn’t have any additional information about the goal, or the path and all that. The salient features for blind search are as follows. 

  • It doesn‚Äôt have any information about the goal, so it examines each node and consumes time in order to search for the goal.¬†
  • In a blind search, there is no information for search space. The agent creates a path on its way to the search for the goal.¬†
  • As there is no information about the goal, no information for the path between the initial point and the target goal. So, they create their own path by going to each node until they have reached the final node which is the end goal. Once they have reached the goal, the search has been stopped.¬†
  • The process of blind search is time-consuming as they have to examine each and every node and also there is no concept of path planning in blind search as well.¬†
  • The only plus point in blind search is that they can differentiate between the goal node and the non-goal node.

Types of Blind Searches

There are different types of blind searches a brief introduction of each search is given as follows. 

  • Breadth-First Search is a systematic strategy applied in searches. It considers all the nodes first of all at level 1 and after that, it finds the goal at level 2 and finds each and every node. The solution is guaranteed in a breadth-first search. It will always go for the shallowest goal and hence it is the cheapest technique.¬†
  • Uniform Cost Search is again a type of blind search technique. With a uniform cost, the cheapest solution can be obtained as they manipulate the search after reaching each node. They didn‚Äôt go for the shallowest technique like in breadth-first searches.¬†
  • Depth First Searches are those in which the agent searches for the goal in the depth direction, unlike breadth-first searches in which the agent expands each and every node and finds the goal at each and every position. There are several problems associated with this technique if there are a large number of depths in a tree it becomes very time-consuming and at the same time very confusing and complex.¬†
  • Depth Limited Search is a hybrid kind of blind search technique. In this technique, the agent explores the depths first but comes back as soon as it realizes that on this path, it cannot find the goal and rather consume time and energy. So, it comes back to level one and starts expanding node two on the depth side. This technique is very better as compared to the others in blind search techniques.¬†
  • Iterative Deepening Searches are the most improved form the blind search techniques. In in-depth limited searches, there is no clear parameter at which point the depth must have been explored. In this technique, there is a limit, up to which extent a node can be expanded in order to find the goal.

Heuristic or Informed Searches 

Informed searches are those in which there are optimal solutions are available. In such searches, there are spaces as well. The heuristic assigns the real number values to the nodes, and branches and the space provide the solution to the model in order to get the search on that particular area. There are some main features of heuristic searches as follows. 

  • It gives a real and possible solution.
  • The solution can be in the form of a point or state space.¬†
  • They are also capable to provide the path from the initial position to the final position, goal, or the target
  • It is quick and inexpensive as compared to blind searches.
  • It provides feedback to the model as well.
  • The heuristic defined for each node, branch, and goal depends on the user.

Types of Heuristic Searches

There are two types of heuristic searches in artificial intelligence these are depth-first searches and breadth-first searches. Now the question arises here that both of these are also in blind searches as well. These searches in heuristic searches have proper path planning, some heuristics assigned, and information about the goal that has to be achieved which makes them different and efficient from the ones that are discussed in the blind searches. These are better solution providers as compared to the ones in blind searches.


How Algoscale Can Help You

Algoscale offers AI consulting services and create tailored artificial intelligence solutions to provide you with a competitive edge. These solutions range from predictive analysis and human activity identification to semantic search and manual work automation.

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