AI Lesson 4 – Search Algorithms | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

Search Algorithms

Search algorithms are among the earliest and most important ideas in Artificial Intelligence. They form the foundation of how intelligent systems explore possibilities, solve problems, and make decisions. Before machines could learn from data, they needed a way to systematically search through different options and select the best possible solution.

Even today, many AI systems rely on search-based thinking. Whether it is finding the shortest route on a map, solving a puzzle, scheduling tasks, or planning a sequence of actions, search algorithms provide a structured way for machines to move from a starting point to a desired goal.

What Is a Search Problem?

In Artificial Intelligence, a search problem involves finding a sequence of actions that leads from an initial state to a goal state. The system must explore different possibilities and decide which path to follow. This process is guided by a set of rules that define what actions are allowed and how states change.

A typical search problem consists of an initial state, one or more goal states, a set of possible actions, and a way to evaluate or compare different states. The challenge lies in choosing an efficient strategy to explore the search space without wasting time or resources.

Why Search Algorithms Matter in AI

Search algorithms allow AI systems to reason about problems where the solution is not immediately obvious. Instead of guessing randomly, the system follows a systematic approach to examine possible paths and outcomes. This makes search algorithms especially useful in environments where decisions must be made step by step.

Classic AI applications such as game playing, robotics navigation, and automated planning all rely on search techniques. Even modern AI systems that use Machine Learning often incorporate search methods for optimization, decision-making, and inference.

State Space and Search Trees

To understand search algorithms, it is important to visualize the concept of a state space. A state space represents all possible situations that can arise in a problem. Each state corresponds to a specific configuration, and transitions between states occur when actions are taken.

Search algorithms typically explore the state space by constructing a search tree. The root of the tree represents the initial state, and each branch represents a possible action leading to a new state. The goal of the algorithm is to traverse this tree efficiently and locate a goal state.

Uninformed vs Informed Search

Search algorithms are broadly divided into uninformed and informed search methods. This distinction is based on whether the algorithm has additional knowledge about the problem beyond the basic rules.

Uninformed search algorithms explore the search space without any guidance toward the goal. They rely solely on the structure of the problem and systematically examine possibilities. While these methods are simple and guaranteed to find a solution if one exists, they can be inefficient for large problems.

Informed search algorithms use heuristic information to guide the search process. A heuristic is an estimate of how close a given state is to the goal. By using heuristics, informed search methods can prioritize more promising paths and significantly reduce the number of states explored.

Trade-offs in Search Strategies

No single search algorithm is perfect for all problems. Some algorithms prioritize finding a solution quickly, while others focus on finding the most optimal solution. Certain methods require more memory, while others trade memory usage for increased computation time.

Choosing the right search strategy depends on factors such as problem size, time constraints, memory availability, and whether the optimal solution is required. Understanding these trade-offs is essential when designing AI systems that operate efficiently in real-world environments.

Search Algorithms in Real-World Applications

Search algorithms are widely used beyond academic examples. Navigation systems search for optimal routes, scheduling systems search for efficient task assignments, and game-playing agents search for winning strategies. In robotics, search algorithms help machines plan movements while avoiding obstacles.

As you progress through this course, you will see how search concepts evolve into more advanced techniques and how they are implemented using code. Theoretical understanding of search algorithms makes it much easier to grasp these implementations later.

Practice Questions

Practice 1: What is the primary goal of a search problem in AI?



Practice 2: What term describes all possible configurations of a problem?



Practice 3: What is used in informed search to guide the algorithm toward the goal?



Quiz

Quiz 1: Which type of search explores without additional goal-directed knowledge?





Quiz 2: What structure is commonly used to represent explored states?





Quiz 3: What distinguishes informed search from uninformed search?





What’s Coming Next

In the next lesson, we will dive deeper into uninformed and informed search strategies and examine how different algorithms explore the search space in practice.

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