Understanding Search Methods in Artificial Intelligence
Searching algorithms in AI are critical for problem-solving and can be broadly classified into two categories: uninformed and informed searching.
What is Uninformed Searching?
Also known as blind or brute force searching, uninformed search operates without any domain-specific information. Key characteristics include:
- Explores all possible states systematically from the start state
- Checks repeatedly if the current state is the goal state
- Lacks heuristic information or guidance, relying solely on problem definition (start and goal states)
- Guarantees finding an optimal solution by exhaustive search
Example: In the Travelling Salesman Problem (TSP) with 5 cities, uninformed searching would examine all permutations (which is (n-1)! = 4! = 24 routes) to find the shortest route.
Challenges:
- Time complexity grows factorially with the number of cities, making it impractical for large problems (e.g., 100 cities results in 99! possibilities)
- Requires extensive time and computational resources (exponential growth)
What is Informed Searching?
Informed search leverages heuristics, domain-specific knowledge or assumptions, to guide the search process more efficiently.
- Uses heuristic functions (denoted as h(n)) to estimate the cost or distance to the goal
- Helps prioritize paths that are more likely to lead to a solution quickly
- Reduces time and space complexity, often solving problems in polynomial time
- May sacrifice guaranteed optimality for speed and practicality
Example: In TSP, a heuristic like the nearest neighbour approach estimates the next city to visit based on the shortest immediate distance.
Benefits:
- Handles exponentially large state spaces more efficiently
- Lowers computational cost and search time
Trade-offs:
- Solutions are generally good but not always optimal
Real-Life Analogy for Search Strategies
Consider navigating a city:
- Uninformed approach: Explore every street randomly until you find your destination (time-consuming and inefficient).
- Informed approach: Use a map or ask for directions to take the most promising routes (faster but may not be the shortest).
Popular Algorithms
Uninformed Search Algorithms:
- Breadth First Search (BFS): Explores all neighbors level by level
- Depth First Search (DFS): Explores as deep as possible along one path before backtracking
Informed Search Algorithms:
- A* Search: Combines path cost and heuristic estimate for optimal pathfinding
- Greedy Best First Search: Selects nodes closest to the goal per heuristic evaluation
- Heuristic DFS and BFS: Variants incorporating heuristic information
When to Use Which Search?
- Use uninformed search when simplicity is needed, and the state space is manageable or when an optimal solution is essential.
- Use informed search to find quicker, near-optimal solutions in large, complex problem spaces where exhaustive search is impractical.
Summary
Uninformed searching guarantees optimal solutions through exhaustive exploration but faces exponential time costs in large problems. Informed searching reduces time and space complexity by incorporating heuristics, offering efficient solutions with potential trade-offs in optimality. Understanding these differences is essential for selecting appropriate AI search strategies based on problem complexity and resource constraints.
For deeper insights into heuristic-based methods, you may find Understanding the Cuckoo Search Algorithm: Principles and Implementation useful to explore alternative heuristic strategies beyond classical approaches. Additionally, the Complete Crash Course on Artificial Intelligence by iSkill provides broader context on AI techniques including search algorithms.
For deeper insights, explore linked videos on algorithms like A* and Best First Search available in the description box.
Hello friends You are welcome to Gate Smashers In today's video we are going to discuss
Difference between uninformed and informed searching Which is one of the most important point of artificial intelligence So first of all we are talking about searching without information
We tell uninformed searching And we tell informed searching because the name itself indicates Information
That means searching with information We generally call uninformed searching as brute force method Or blind searching
In that what we do suppose this is my start state If I want to reach to the goal state from start state Denim exploring all the other state from start state
Denim exploring the all which will come in next step In this way after searching all the state spaces Finally I will reach at my goal state
Over here what we are checking again and again Whether it is my goal state or not No
Then after that we are searching all the states So over here I have only knowledge That this is my start state
And this is my goal state But over here I do not have any knowledge of domain or problem Or you can say that over here I do not have any guide
Which can guide me that I have to go on this path Or I don't have to go on this path But in informed searching we are using information
And we tell that information Heuristic This is very important point
I will explain you with a simple example Let's say travelling salesman problem If we talk about travelling salesman problem
Let's say there are 5 cities And we have connected 5 cities In this way
There are 5 cities and we have connected them in this way Now let's say what we do in travelling salesman problem What is our problem actually
I want to start from one city I want to cover minimum distance I want to again reach at my city
And I have to cover all the cities With the minimum distance And if we talk let's say I have 5 cities
I have 5 cities So if we use uninformed searching over here We apply brute force method
What is the meaning of brute force That you have to go on all the possibilities And if you will go on all the possibilities
Then according to TSP problem There will be (n-1)! Possibilities How much will be a searching space
(n-1)! If you have 5 number of cities That is value of n
Then (5-1)! That is 4! Which is what? 24
There will be 24 possibilities There will be 24 states After covering all the states you will get a final optimal solution
It is guaranteed that you will get an optimal solution But if we talk over here you have to apply exponentially time Because if you make this number of cities as 100
Do it 200 Then obviously 99! Or 199! Which is a very large number
So over here I do not have any other knowledge I know this is my start state I have to reach at goal state
Without any guide Without any thumb rule Without any heuristic value
But if we talk about informed searching Then in informed searching we use Heuristic method
As we use heuristic method in travelling salesman problem Simple meaning of heuristic is assumption We have to assume that how we will reach at that place
Now over here we use heuristic only when If anyone asks you when we use heuristic search Then what is the biggest reason of it
Whenever our state space Grows in exponential power Then over there time complexity is so much
Which we generally call as NP problem, which means non polynomial problem And to solve non polynomial problem Definitely my cost will also increase
Because it will take a lot of time Space also will be required a lot So obviously my cost will increase a lot
So what we want If we can solve this problem in polynomial time Show obviously it is not that easy to solve the problem in polynomial time
So to solve in polynomial time We have to use heuristic method somewhere So heuristic method helps you
Like in travelling salesman problem We use nearest Neighbour With nearest Neighbour method
We find out heuristic value for each node It helps us as a guide You want to go from one place to another in your city
Let's say in some other city In your city you might be knowing If you want to go from one place to another place in some other city
So which is one method, uninformed You use brute force method Rome in each lane
Keep on going at each place And keep on checking is this my goal state Is this my goal state
Is this my goal state or not But what is better than that You take the knowledge of domain
You take the knowledge of that city Ask to someone If you will use guide over there
Then you will definitely benefit Your time will reduce But in this you will have to compromise with optimality
Because this method is quick What have I written over here it is a quick solution It is a time consuming solution
So if we talk then obviously we will have to go in 24 state phase If we take a simple example Let's say 24 puzzle problem
As there is 8 queen problem, 15 puzzle problem In 24 puzzle problem You might have to go up to 10^24 states
That means you will have to check this many values Which is practically impossible A very big value
Because in this your time complexity is B^d That is a branch factor
And other than branch factor how much deep you are going into the values So obviously your time complexity will increase that much So you will have to grow exponentially
In uninformed searching But if you are using heuristic You got an advantage
You can solve in polynomial But you will have to compromise with optimality That means it will give a good solution
It can give a good solution But this might give an optimal solution or this might not given optimal solution You don't have to take guarantee of that
But this will always give you an optimal solution that is guaranteed Next this I have already told you More complexity is there in terms of time also and space also
But over here my complexity is reduced So due to that if we talk about cost Then my cost will also reduce over here
So this is time consuming and this is a quick solution Hand over here if we talk about the real life example Or if I talk on its bases
Which are the popular algorithms That is depth first search Breadth first search
What we do in breath first search We go to the first level from the starting state We cover them all
Then we go on the second level We cover them all In this way our state space keeps on increasing
In the same way if we talk about depth first search In that also we go in One Direction in deep When we do not get any solution
We backtrack So over there also my state space is quite more But if we talk about informed
So in informed A* algorithm is very important Heuristic DFS is there Or heuristic BFS can also be there
Best first search method is there greedy method is there Where we use heuristic values and heuristic functions So we generally denote heuristic functions
By h(n) So how does DFS and BFS work If we talk from the point of view of artificial intelligence
I have already explained this You will get its link in the description box And over here A*
You consider this as a very important algorithm A* and best first search I have already explained
It's link is also there in the description box So over here you get to know about the basic information about both That what is the difference between an informed and informed searching
Thank you
Uninformed searching, also known as blind or brute force searching, explores all possible states systematically without any heuristic or domain-specific information. It guarantees finding an optimal solution by exhaustive search but can be very time-consuming and computationally expensive, especially in large state spaces. Use uninformed search when the problem space is small or when an optimal solution is essential and simplicity is preferred.
Informed searching uses heuristic functions to estimate the cost or distance to the goal, guiding the search process toward promising paths. This approach reduces time and space complexity by prioritizing nodes likely to lead to a solution faster, often solving large problems in polynomial time. However, it may sacrifice guaranteed optimality for speed and practicality, making it suitable for complex, large-scale problems where resources are limited.
Common uninformed search algorithms include Breadth First Search (BFS), which explores neighbors level by level, and Depth First Search (DFS), which explores paths deeply before backtracking. Informed search algorithms involve heuristics, such as A* Search, which combines path cost and heuristic estimates for optimal pathfinding, and Greedy Best First Search, which selects nodes closest to the goal per heuristic evaluation.
The main trade-off in informed searching is balancing speed and optimality. While heuristics enable faster and more efficient searches by focusing on promising paths, they may not always find the absolute optimal solution. This trade-off is acceptable in many real-world scenarios where a good solution found quickly is more valuable than a perfect solution that takes impractical amounts of time.
In the TSP, uninformed search examines all permutations of city routes to find the shortest path, which grows factorially and becomes impractical as the number of cities increases. In contrast, informed search methods like the nearest neighbour heuristic estimate the next city based on immediate shortest distance, greatly reducing computational effort at the cost of sometimes not finding the optimal route.
Navigating a city illustrates these search strategies well: an uninformed search is like exploring every street randomly until reaching the destination, which is inefficient and time-consuming. An informed search uses a map or directions to take the most promising routes, arriving faster but not always taking the absolute shortest path.
Consider problem size, resource constraints, and solution requirements. If the state space is manageable and an optimal solution is critical, uninformed search is appropriate. For large, complex problems where exhaustive search is impractical, informed search offers efficient, near-optimal solutions by leveraging heuristics, balancing speed with acceptable accuracy.
Heads up!
This summary and transcript were automatically generated using AI with the Free YouTube Transcript Summary Tool by LunaNotes.
Generate a summary for freeRelated Summaries
Introduction to Artificial Intelligence with Python: Search Algorithms
Explore the foundations of AI, techniques, and algorithms in Python, focusing on search strategies and optimization methods.
Understanding Cuckoo Search Algorithm: A Step-by-Step Guide
Learn the Cuckoo Search Algorithm with detailed examples and calculations for optimization problems.
Understanding Cuckoo Search Algorithm: A Comprehensive Guide
Explore the Cuckoo Search Algorithm's principles, implementation steps, and its mathematical models.
Understanding the Cuckoo Search Algorithm: Principles and Implementation
Learn about the Cuckoo Search Algorithm, its principles, implementation, and applications in optimization problems.
Understanding the Cuckoo Search Algorithm: A Step-by-Step Guide to Optimization
Explore the Cuckoo Search Algorithm and how it optimizes solutions with examples and detailed explanations.
Most Viewed Summaries
Kolonyalismo at Imperyalismo: Ang Kasaysayan ng Pagsakop sa Pilipinas
Tuklasin ang kasaysayan ng kolonyalismo at imperyalismo sa Pilipinas sa pamamagitan ni Ferdinand Magellan.
A Comprehensive Guide to Using Stable Diffusion Forge UI
Explore the Stable Diffusion Forge UI, customizable settings, models, and more to enhance your image generation experience.
Pamamaraan at Patakarang Kolonyal ng mga Espanyol sa Pilipinas
Tuklasin ang mga pamamaraan at patakaran ng mga Espanyol sa Pilipinas, at ang epekto nito sa mga Pilipino.
Mastering Inpainting with Stable Diffusion: Fix Mistakes and Enhance Your Images
Learn to fix mistakes and enhance images with Stable Diffusion's inpainting features effectively.
Pamaraan at Patakarang Kolonyal ng mga Espanyol sa Pilipinas
Tuklasin ang mga pamamaraan at patakarang kolonyal ng mga Espanyol sa Pilipinas at ang mga epekto nito sa mga Pilipino.
If you found this summary useful, consider buying us a coffee. It would help us a lot!