Best First Search in Artificial Intelligence
Artificial intelligence has become a part of our lives and aids in our regular activities. Whether we talk about computers, gadgets, or other equipment, AI-based algorithm models are helpful in easing our tasks and time management. One such specific algorithm within the field of AI is Best First Search. It behaves like a smart explorer that helps a computer program make the right decisions for the correct path at each step. The best first search in artificial intelligence eases our task and reduces efforts and time, leading to efficient decision-making and faster goal achievement.
Table of contents
What is the Best First Search?
Best first search (BFS) is a search algorithm that functions at a particular rule and uses a priority queue and heuristic search. It is ideal for computers to evaluate the appropriate and shortest path through a maze of possibilities. Suppose you get stuck in a big maze and do not know how and where to exit quickly. Here, the best first search in AI aids your system program to evaluate and choose the right path at every succeeding step to reach the goal as quickly as possible.
For example, imagine you are playing a video game of Super Mario or Contra where you have to reach the goal and kill the enemy. The best first search aid computer system to control the Mario or Contra to check the quickest route or way to kill the enemy. It evaluates distinct paths and selects the closest one with no other threats to reach your goal and kill the enemy as fast as possible.
The best first search in artificial intelligence is an informed search that utilizes an evaluation function to opt for the promising node among the numerous available nodes before switching (transverse) to the next node. The best first search algorithm in AI utilizes two lists of monitoring the transversal while searching for graph space, i.e., Open and CLOSED list. An Open list monitors the immediate nodes available to transverse at the moment. In contrast, the CLOSED list monitors the nodes that are being transferred already.
Key Concepts of BFS
Here are some key features of the best first search in artificial intelligence:
Evaluation of Path
While using the best first search, your system always seeks possible nodes or paths that can be taken. Then, it picks the most promising or best node or path that is eligible to traverse the shortest distance node or path to reach the goal and exit the maze.
Use of Heuristic Function
The best first search uses a heuristic function in informed decisions. It helps in finding the right and quick path towards the goal, called heuristic search. The current state of the user in the maze is the input of this function, based on which it estimates how close the user is to the goal. Based on the analysis, it assists in reaching the goal in a reasonable time and with minimum steps.
The Best-First Search algorithm in AI assists the computer system in tracking the paths or nodes it has traversed or plans to traverse. It prevents the system from becoming entangled in loops of previously tested paths or nodes and helps avoid errors.
Iteration of Process
The computer program keeps repeating the process of the above three criteria until it reaches the goal and exits the maze. Therefore, the best first search in artificial intelligence consistently reevaluates the nodes or paths that are most promising based on the heuristic function.
What is a Heuristic Function?
The heuristic function refers to the function used in the informed search and evaluation of the best or promising path, route or solution leading to the goal. It helps in estimating the right path in less time. However, the heuristic function does not always provide accurate or optimized results. Sometimes, it generates sub-optimized results. The heuristic function is h(n). It calculates the cost of an optimal route or path between the pair of states, and its value is always positive.
There are basically two categories of search algorithms:
It is also called a blind method or exhaustive method. The search is done without additional information, which means based on the information already given in the problem statement. For instance, Depth First Search and Breadth First Search.
The computer system performs the search based on the additional information provided to it, allowing it to describe the succeeding steps for evaluating the solution or path towards the goal. This popularly known method is the Heuristic method or Heuristic search. Informed methods outperform the blind method in terms of cost-effectiveness, efficiency, and overall performance.
There are generally two variants of informed algorithm, i.e.,
- Greedy Best First Search: Going with the name, this search algorithm is greedy and hence chooses the best path available at the moment. It uses a heuristic function and search, which is combined with depth and breadth-first search algorithms and combines the two algorithms where the most promising node is chosen while expanding the node present in proximity to the goal node.
- A* Best First Search: It is the widely used type of best-first search. The search is efficient in nature due to the presence of combined features of greedy best-first search and UCS. Compared to greedy search, A* uses a heuristic function to look for the shortest path. It is quick and utilizes UCS with varied forms of heuristic function.
Comparison with A* Search
The differences between the best first search and A* searches are given in the table below.
|Parameters||Best First Search||A* Search|
|Past knowledge||No prior knowledge.||Past knowledge involved|
|Optimal||May not optimal||Always optimal|
|Evaluation Function||f(n)=h(n)Where h(n) is heuristic function||f(n)=h(n)+g(n)Where h(n) is heuristic function and g(n) is past knowledge acquired|
|Time Complexity||O(bm,,,) where b is branching and m is search tree’s maximum depth||O(bm,,,) where b is branching and m is search tree’s maximum depth|
|Space Complexity||Polynomial||O(bm,,) where b is branching and m is search tree’s maximum depth|
|Nodes||When searching, all the fridges or border nodes are kept in memory||All nodes are present in memory while searching|
|Memory||Need less memory||Need more memory|
Here are some of the most common use cases of best first search algorithm:
Best first search guides robots in a challenging situation and takes effective moves to navigate to their destination. Efficient planning is crucial in complex tasks so that it can evaluate the right paths toward the goal and make informed decisions accordingly.
It helps game characters observe the threat, avoid obstacles, make the right decision-making strategic moves and evaluate the accurate path to reach the objectives within the time goal.
The best first search algorithms in AI are used in navigation apps like Google Maps to assist in the quickest routes. When we travel from one location to another, the algorithm considers factors like road conditions, traffic, U-turns, distance, and so on to navigate through the route with fewer obstacles and in less time.
Data Mining and Natural Language Processing
In data mining, artificial intelligence employs the best first search to assess the most suitable features that align with the data, facilitating selection. This reduces computational complexity in machine learning and enhances data model performance.
Best first search algorithms also assess semantically similar phrases or terms to provide relevance. They find extensive use in text summarization and search engines, simplifying task complexity.
Scheduling and Planning
Best first search in artificial intelligence finds application in scheduling work and activities, enabling resource optimization and meeting deadlines. This functionality is integral to project management, logistics, and manufacturing.
Here is a brief overview of steps on how the best first search in artificial intelligence can be implemented.
- Step 1: Choose an initiating node (suppose ‘n’) and place it in the OPEN list.
- Step 2: In case the initiating node is empty, you must stop and return to failure.
- Step 3: Eliminate the node from the OPEN list and place it on the CLOSE list. Here, the node is the lowest value of h(n), i.e., heuristic function.
- Step 4: Expand the node and create its successor.
- Step 5: Check each successor to see whether they are leading to the goal.
- Step 6: If a successor node leads to the goal, you must return success and terminate the search process. Or continue with step 7.
- Step 7: The algorithm analyzes every successor for the evaluation function f(n). Later, it examines whether the nodes are in the OPEN or CLOSED list. In case they do not find the node in either list, it adds them to the OPEN list.
- Step 8: Return to step 2 and iterate.
Challenges and Limitations
There are some benefits of the best first search in artificial intelligence, but they also possess some challenges and limitations.
- The quality of the Heuristic must be good. If you compromise with quality, it may not provide effective estimates, and you may find errors in finding optimal solutions.
- The best first search algorithm in AI is good for evaluating the right solution or path but does not guarantee the absolute best routes or solution and opts for suboptimal routes.
- The chances of getting stuck in a loop are higher.
- The best first search in artificial intelligence can be memory intensive in large data. It limits the ability to function effectively in resource-constrained situations.
- Best first search prioritizes choosing the right route based on the shorter length and not in terms of other factors like the quality of the route. Therefore, the evaluation of an accurate route can be tricky.
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Frequently Asked Questions
A. A* Search Algorithm is a well-known and powerful AI search algorithm. It utilizes the heuristic function h(n) along with the past knowledge g(n) to make informed decisions.
A. A greedy search does not consider all data and, therefore, can lead to non-optimal results.
A. Dijkstra’s algorithm offers a guarantee in determining the shortest path leading to the goal. In contrast, the best free search does not offer a guarantee for the shortest path. It depends on the heuristic function used and the specific problem instance.
A> The recursive best first search belongs to the artificial intelligence algorithm that expands the frontier nodes in the best manner or order. Additionally, it prefers the specific node over others based on the problem-specific information.