Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Best First Search in Artificial Intelligence

A
Analytics Vidhya Last Updated : 30 Sep, 2024
8 min read

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 help ease 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.

In this article, you will learn about the best first search in AI. We will explain how the best first search algorithm works and why it is important in artificial intelligence. This method helps find the best paths by looking at the most promising options first, making it easier to solve problems in different areas.

Overview:

  • Learn about Best First Search in AI, a heuristic-driven algorithm.
  • Understand how Best First Search algorithm utilizes heuristic functions.
  • Explore the role of Best First Search in AI applications.
  • Discover the implementation details of Best First Search algorithm.
  • Understand the limitations and challenges of Best First Search in AI.

Table of contents

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 in evaluating and choosing the right path at every succeeding step to reach the goal as quickly as possible.

For example, imagine 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. An open list monitors the immediate nodes available to transverse at the moment. In contrast, the CLOSED list monitors the nodes 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 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 find the right and quick path towards the goal, which is called a heuristic search. The user’s current state 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.

Keeping Track

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 states, and its value is always positive.

Algorithmic Details

There are two categories of search algorithms:

Uniformed Algorithm

It is also called a blind method or exhaustive method. The search is done without additional information based on the information already given in the problem statement. For instance, Depth First Search and Breadth First Search.

Informed Algorithm 

The computer system performs the search based on the additional information provided, allowing it to describe the succeeding steps for evaluating the solution or path toward the goal. This popularly known method is the Heuristic method or Heuristic search. Informed methods outperform the blind method regarding cost-effectiveness, efficiency, and overall performance.

There are generally two variants of informed algorithm, i.e., 

  1. Greedy Best First Search: As the name suggests, this algorithm is greedy and chooses the best path available. It uses a heuristic function and search, combined with depth and breadth-first search algorithms, and combines the two algorithms, selecting the most promising node while expanding the node present near the goal node. 
  1. A* Best First Search: This is the most widely used type of best-first search in AI. It is efficient due to the combined features of greedy best-first search and UCS. Compared to greedy search, A* uses a heuristic function to find the shortest path. It is quick and utilizes UCS with varied forms of heuristic function. 

The differences between the best first search and A* searches are given in the table below.

ParametersBest First SearchA* Search
Past knowledgeNo prior knowledge.Past knowledge involved
Completeness Not completeComplete
Optimal May not optimal  Always optimal 
Evaluation Function f(n)=h(n)Where h(n) is heuristic functionf(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 depthO(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 memoryAll nodes are present in memory while searching 
Memory Need less memory Need more memory 

Applications

Here are some of the most common use cases of the best first search algorithm:

Robotics 

The best first search guides robots in a challenging situation and makes effective moves to navigate to their destination. Efficient planning is crucial in complex tasks so that robots can evaluate the right paths toward the goal and make informed decisions accordingly.

Game Playing 

It helps game characters observe the threat, avoid obstacles, make the right strategic decisions, and evaluate the accurate path to reach the objectives within the time goal.

Navigation Apps 

The best first search in AI is 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 assess semantically similar phrases or terms to provide relevance. They are extensively used in text summarization and search engines, simplifying task complexity.

Scheduling and Planning 

The best first search in artificial intelligence (AI) finds applications in scheduling work and activities, enabling resource optimization, and meeting deadlines. This functionality is integral to project management, logistics, and manufacturing.

Implementation

To implement it, computer programs write code in different computer languages, such as Python, C, Javascript, C++, and Java. The code provides instructions to the computer system to evaluate the routes, paths, or solutions and use heuristic functions.

Here is a brief overview of the steps for 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: If 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 h(n) value, i.e., heuristic function.
  • Step 4: Expand the node and create its successor.
  • Step 5: Check each successor to see whether they lead to the goal.
  • Step 6: If a successor node leads to the goal, you must return success and terminate the search process. Otherwise, you can 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. If it does not find a node in either list, it adds it to the OPEN list.
  • Step 8: Return to step 2 and iterate.

Challenges and Limitations

The best first search in artificial intelligence has some benefits, but it also has some challenges and limitations.

  1. 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.
  2. This algorithm is good for evaluating the right solution or path but does not guarantee the absolute best routes or solutions and opts for suboptimal routes.
  3. The chances of getting stuck in a loop are higher.
  4. The best first search in AI can be memory intensive in large data. It limits the ability to function effectively in resource-constrained situations.
  5. It prioritizes choosing the right route based on its shorter length and not other factors like its quality. Therefore, evaluating an accurate route can be tricky.

Which Search Algorithm is Best in Artificial Intelligence?

There isn’t a single “best” search algorithm in AI, as the optimal choice depends on the specific problem you’re tackling. Here’s a breakdown of two common types:

Uninformed Search Algorithms: These methods explore all possible paths without knowing the goal’s location. For instance:

  • Breadth-First Search (BFS) is efficient for finding the shortest path in specific scenarios, but it can waste time exploring irrelevant areas.
  • Depth-first search (DFS) is sometimes faster than BFS, yet it risks getting trapped in infinite loops.

Informed Search Algorithms: These utilize additional information, often heuristics, to prioritize which paths to explore first. One widely used example is:

  • A Search:* This algorithm balances BFS and greedy best-first search approaches. It efficiently finds optimal paths by estimating the cost of reaching the goal from any given node.

Each of these techniques involves an iterative deepening search, in which nodes are traversed from the current node towards an optimal path.

Conclusion 

Experience the power of the Best First Search algorithm in Artificial Intelligence with our BlackBelt+ Program. Learn how this algorithm serves as your professional guide in complex environments, aiding computer systems in optimizing paths and making informed decisions. Our program harnesses the potential of the Best First Search algorithm, utilizing heuristic functions to provide intelligent solutions based on prior knowledge. Join us to equip yourself with the skills to navigate complex problems and uncover multiple possibilities for superior solutions. Enroll in our BB+ Program today and elevate your AI expertise!

Hope you understand the best first search and their data structure, differentiating search space in algorithms. Also, with this pathfinding, the best first step is problem-solving and stating their start node for uniform cost search, i.e., true for different search techniques. These best search tutorials not only traverse the different codes like pseudocode, node b

Q1. Which is the best AI search algorithm?

A. A* Search Algorithm is a well-known and powerful AI search algorithm. It utilizes the heuristic function h(n) and past knowledge g(n) to make informed decisions.

Q2. Can greedy search provide an optimal solution?

A. A greedy search does not consider all data and can lead to non-optimal results.

Q3. What is the difference between Dijkstra and Best-First Search?

A. Dijkstra’s algorithm guarantees the shortest path to the goal. In contrast, the best free search does not guarantee the shortest route. It depends on the heuristic function used and the specific problem instance. 

Q4. What is the recursive best first search in AI?

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.

best first searchbest first search algorithmbest first search algorithm examplebest first search algorithm in aibest first search algorithm in artificial intelligencebest first search examplebest first search in aiBest First Search in Artificial Intelligenceexample of best first searchwhat is best first searchwhat is best first search in artificial intelligence
A
Analytics Vidhya

Analytics Vidhya Content team

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Sanjeda Mim
Sanjeda Mim

is greedy best first search algorithm and best first search algorithm same?

123

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag
Congratulations, You Did It!
Well Done on Completing Your Learning Journey. Stay curious and keep exploring!
Informed Search Strategies for State Space Search Solving
Informed Search Strategies for State Space Search Solving
An Introduction to Problem-Solving using Search Algorithms for Beginners
An Introduction to Problem-Solving using Search Algorithms for Beginners

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details