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Ever wondered how Artificial Intelligence represents information about the world, uses logic to solve complex problems, answers queries, and makes smart decisions?

Architectural Insight: Separation of Concerns in State Space Search

Imagine you are looking for a key hidden inside a maze.

Imagine you are looking for a key hidden inside a maze.

When developing Artificial Intelligence (AI) based tool to solve complex problems, one of the fundamental concepts used is State Space Search. To understand how AI navigates and solves these challenges, we need to break down its core components: the Domain (the world), the Agent (the problem solver), How the Agent Works, the Search Algorithm (the strategy & data structures), and How Search Algorithms Are Classified.

Have you ever heard the old saying, "Birds of a feather flock together" or "Show me your friends, and I'll tell you who you are"?

Have you ever found yourself trying to make a simple decision, like deciding whether to go out for a walk or stay inside? You probably ask yourself a series of quick questions:

When starting out in Machine Learning (ML), two fundamental algorithms lay the groundwork for everything else: Linear Regression and Logistic Regression. While both use historical data to uncover hidden patterns, they answer fundamentally different types of questions.

AI is no longer just answering questions or creating images; it is now taking action. Welcome to Agentic & Autonomic AI (The Reasoning Era)—the current frontier where AI evolves from passive chatbots into proactive problem solvers. Independent Action and Reasoning In this era, AI works as an autonomous "agent" capable of executing multi-step goals with minimal human supervision. Key highlights of this phase: Active Problem Solving: AI breaks down big goals into manageab

If the previous generation of AI was about perceiving the world (like recognizing faces or converting speech to text), this current generation is all about creation. Welcome to Generative AI (The Transformer Era)—the breakthrough phase where computers shifted from simply analyzing data to generating brand-new content from scratch.

This era marked the shift where machines stopped just following strict rules and started interpreting the world much like humans do—by seeing, hearing, and perceiving patterns. At the heart of deep learning is the goal of mimicking how neurons in the human brain process information. While the basic mathematical concepts existed earlier, two main drivers allowed deep learning to take off: Explosion of Big Data: The massive amount of photos, voice recordings, and text cr

If we are teaching a child how to recognize an apple, what we do ? We show many pictures of apples. The child naturally learns to recognize an apple on their own. We don't explain to the child using mathematical rules about shape and textures of apple.

Domain in State Space Search

When we think of Artificial Intelligence (AI) today, we often picture smart assistants or machine learning models that learn from millions of data points. But before AI could learn on its own, it relied heavily on human logic and strict rules. This early age of artificial intelligence is known as Knowledge-Based AI, or The Symbolic Era.

AI has changed dramatically over time—moving away from rigid, rule-following computer programs to adaptive systems capable of reasoning and independent action. To help make sense of how far technology has come, here is a simple breakdown of the 5 main eras in the evolution of AI:

Complete guide to AI Concepts with Examples. Also includes FAQs.

Includes Frequenty Asked Questions and Programs

What is State Space Search ?

Includes Frequently Asked Programs & Questions.

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