Trending February 2024 # Google’s New Search Quality Guidelines # Suggested March 2024 # Top 10 Popular

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A must-read for anyone responsible for SEO or Content Marketing

Importance: [rating=5] For all Webmasters, SEO Consultants, Content Producers and Web Designers

Recommended link: Google’s new Search Quality Rating Guideline

Yesterday (November 19th 2024), we saw the release of an updated ‘full’ 160(!) page version of the Search Quality Rating Guidelines. Here’s a sample:

With the adoption of Mobile Devices influencing the search landscape more and more, Google have decided to update its guidelines for Search Quality Raters.

This is big news since Google used to previously to keep these ‘behind closed doors’, but occasionally one would escape into the wild and be dissected. Back in 2013 Google published an abridged version as they looked to “provide transparency on how Google works” after previous leaks of the document in 2008, 2011 and 2012, then in 2014. However, as the use of mobile has rocketed, the need for a “Major” revision of the guidelines was deemed a necessity.

Although this is the full version, this is not the definitive version. Mimi Underwood, Senior Program Manager for Search Growth & Analysis stated:

“The guidelines will continue to evolve as search, and how people use it, changes. We won’t be updating the public document with every change, but we will try to publish big changes to the guidelines periodically.”

So, if you work in search we suggest you download a copy now before people change their mind.

What are the Search Quality Guidelines?

In short, it is a document that will help webmasters and people alike, understand what Google looks for in web pages and what it takes to top the search rankings.

They work this out by using Google’s Search Quality Evaluators (third-party people hired by Google via a third-party agency to rate the search results) to measure a site’s Expertise, Authoritativeness and Trustworthiness, allowing Google to better understand what users want.

Why is it important?

Referring back to the ‘What is it?’ section, it helps you to understand better what it takes to top the search rankings.

And whilst it doesn’t necessarily define the ranking algorithm, it provides you with an insight into what Google are looking for, which, as an SEO Professional, Webmaster, even Website Designer is invaluable information.

How is it structured?

If you’ve read previous incarnations (excluding those who have had a peek at the leaked 2014 version) you’ll see a completely new structure, which has been rewritten from the ground up.

Looking at the monstrous contents page, it’s easy to get overwhelmed, however it’s relatively simple to follow. The first section is the General Guidelines Overview (Pages 4-6), highlighting topics such as the purpose of Search Quality rating, Browser requirements, Ad Blocking extensions, Internet Safety etc.

This is followed by the Page Quality Rating Guidelines (Pages 7-65), which discusses at great detail what Page Quality entails, providing examples of High Expertise, Authority and Trustworthy pages along with the middle tier and lowest tiers. Something interesting about this section is the Your Money or Your Life (YMYL), which discusses pages that could “potentially impact the future happiness, health, or wealth of users”.

The next section looks at Understanding Mobile User Needs (pages 67-86), there is a large emphasis on this part of the report as it is one of the key reasons behind the update. This Brand new section highlights the multiple issues that cause trouble on websites when viewed on a mobile device.

Another new section is the Needs Met Rating Guideline (87-149), which is one of the new ratings for webmasters to determine the quality of the site. It refers to mobile searcher’s needs and questions “how helpful and satisfying the result is for the mobile user?”.

The final section discusses Using the Evaluation platform for the Google Search Quality Evaluators (pages 152-158). It shows the process the Evaluators had to undergo, whilst reporting to google.

Recommended sections

Here’s our analysis of the sections of the parts I felt were critical to read – there’s a lot, and you may think differently!

The sections recommended in the Page Quality Rating (pages 7-65) are:

2.2 What is the purpose of a Webpage? (page 8)

2.3 Your Money Your Life (page 9)

2.6 Website Maintenance (page 15)

2.7 Website Reputation (page 16)

3.0 Overall Page Quality Rating Scale (page 19)

5.0 High-Quality Pages (page 19-23)

7.0 Page Quality Rating: Important Considerations (page 58-59)

11.0 Page Quality Rating FAQs (page 65)

The entire Mobile User Needs (pages 67-86) is worth a scan at the very least.

Needs Met Rating (pages 87-149).

13.0 Rating Using the Needs Met Scale (page 87)

13.1 Rating Result Blocks: Block Content and Landing Pages (page 87)

13.2 Fully Meets (FullyM) (page 90)

13.4 Moderately Meets (MM) (page 107)

13.6 Fails to Meet (FailsM) (page 112)

14.6 Hard to Use Flag (page 127)

15.0 The Relationship between E-A-T and Needs Met (page 130)

18.0 Needs Met Rating and Freshness (page 141)

19.0 Misspelled and Mistyped Queries and Results (page 143)

20.0 Non-fully Meets Results for URL Queries (page 146)

21.0 Product Queries: Action (Do) vs Information (Know) Intent (page 148)

22.0 Rating Visit-in-Person Intent Queries (page 149)

For a more of an in-depth overview, check out Jennifer Slegg’s post at thesempost.

You're reading Google’s New Search Quality Guidelines

Google Aims To Improve Search Quality With New Feedback Form

Google has recently overhauled its search spam report form to combat search quality issues.

The updated form is part of Google’s approach to improving user experience by addressing problematic content such as paid links, malicious behavior, and low-quality pages.

An Improved User Interface

The redesigned form makes it easier for users to report a broader range of search quality issues.

“Now, you can report spam, paid links, malicious behavior, low quality, and other search quality issues, all in one improved form,” Google announced.

This new form introduces a feature for bulk submissions, allowing users to report up to five pages violating the same policy in a single report.

After submitting a report, users will receive a confirmation email from Google, offering help links to additional resources covering Google’s quality policy and directing them to a forum for personalized support.

What Happens After Reporting?

When user feedback reaches Google, the company has a system to prioritize and address them.

While urgent problems might be addressed immediately, most issues are resolved when Google updates the algorithm.

Google’s John Mueller previously explained how the reporting system works, stating:

“The web is so gigantic, and ever-changing, and people ask us new questions every day. Because of that, our goal is generally to improve the algorithms that pull together the search results over all and not to tweak things for individual queries. This may take a bit of time, but it makes search better for everyone worldwide for the large number of searches that are done every day.”

He adds, “regardless of the contact method, make it easy for Google to recognize the scale and the scope of the problem.”

The exact timeline for Google’s response to user feedback remains unclear and likely depends on the nature and urgency of the reported issue.

The Larger Picture

Overhauling Google’s search spam report form isn’t an isolated move. It comes as part of a more comprehensive effort by the tech giant to improve the quality of search results continually.

Google’s decision to allow bulk submissions of up to five pages suggests the company recognizes the scale of search quality issues and is ready to engage with them more substantially.

The enhanced reporting process can lead to cleaner, more relevant search results for everyone.

In Summary

In a rapidly evolving digital landscape, Google prioritizes user feedback to enhance search results.

With the redesigned search spam report form, Google has a more streamlined avenue for reporting search quality issues.

The new form is the most recent example of Google’s commitment to maintaining high-quality SERPs.

How To Use Google’s New Continued Conversation Option

With Continued Conversation enabled, you only need to say “Hey, Google” one time to activate the device. Afterward, you can continue to ask the assistant to complete tasks without repeating the phrase.

The app will continue listening for eight seconds before turning off completely. To avoid unwanted listening, say “Thank you,” “Thanks, Google,” or “I’m done.” Those phrases will shut it down immediately. If you forget to do that, after the eight seconds have elapsed, the app deletes any recording it may have made.

As of this writing, the Continued Conversation option is only available in the United States and in English.

Enable Continued Conversation Option

To enable Continued Conversation on your Google Home device:

1. Open the Google home app on your phone.

2. In the top-left corner tap the three horizontal lines and verify that you are using the correct email account for your home devices.

3. Select “Continued Conversation.”

4. Turn on the switch.

5. All your Google home devices are now able to use Continued Conversation.

Guarding Your Privacy

When you have a conversation with the Google Assistant on a Home device, lights on the device indicate it is listening. If you do not shut down your device’s ability to record by using one of the key phrases, it will continue monitoring and recording for eight seconds. Then the lights will turn off, and the device is no longer active. Again, the app will delete anything recorded while it waited.

You can also set the app to alert you when the device has stopped listening by following these steps in Google home:

1. Tap the “Devices” tab.

2. Select the device you want to give the end of conversation notification.

3. Access “Settings.”

5. Activate the ability to play end sounds.

6. Choose the sound you want to use, and the app will beep to announce that it is no longer listening to you.

If you are worried about what has been saved by the device, you can view and edit your account activity:

1. Tap the “Explore” icon that looks like a compass in the upper-right corner of the app screen.

2. Tap the three dots and select “My Activity.”

Continued Conversation on Your Phone

If you don’t have Google Home devices, you can still use the continued conversation option on your phone with Google Assistant:

1. Open Google Assistant.

2. Tap the “Explore” icon.

3. Select “Settings.”

4. Under Preferences, select “Continued Conversation.”

Enabling an end sound does not work on the phone version of Assistant.

When Won’t Continued Conversation Work?

There are times when Continued Conversation does not work. If you are on a phone call, it will only listen to your first command, so it will not record any further conversation. If an alarm goes off, Continued Conversation will end. Also, anytime you are listening to music, it will only accept one command at a time.

While you can tick off multiple to-do items in one sitting, remember to use correct phrasing for the tasks you are trying to complete. You cannot, for example, ask Google to add spinach to your shopping list and then come back and just say, “milk.” It won’t remember that you were adding items to your list, so you need to repeat the command, “Add milk to my shopping list.”

Hopefully, this will help you use your Google assistant more efficiently without the constant repeating of the trigger phrase.

Tracey Rosenberger

Tracey Rosenberger spent 26 years teaching elementary students, using technology to enhance learning. Now she’s excited to share helpful technology with teachers and everyone else who sees tech as intimidating.

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New Google Mortgage Information Search

Google announced they are rolling out a mortgage information search product. The new service will show in mobile searches.

Google Mortgage Information Search for Mobile

Google’s new service is a collaboration with Consumer Financial Protection Bureau (CFPB).

The CFPB is a United States government organization that regulates the consumer financial products and services.

The new mortgage search tool is available in mobile.

According to the CFPBs About Us page:

with the information, steps, and tools that they need to make smart financial decisions.”

Google is partnering with the U.S. government to provide information that is meant to benefit consumers.

Google Mortgage Information Search

The tool has a tabbed interface. It currently only shows in mobile devices. General mortgage related keywords trigger the new mortgage search engine results page.

The new search feature can be seen as a way to funnel users from high level mortgage related search queries to more specific information, but not necessarily to more specific websites.

Screenshot of Tabbed Interface of Google’s Mortgage Information Search Four Ads Above Mortgage Tools

Screenshot of a Google search ad above the mortgage information tools:

The search results are beneath Google’s mortgage search tools.  But you have to scroll past multiple mortgage related Google features before you get to two search results that in my case was from the same domain.

Then that’s followed by FAQs that have no links to the website of origin.

Did Google “Borrow” Content Without Attribution?

One of the FAQs has content that appears to have been sourced from chúng tôi But there is no link to the source of the information or any other attribution.

It’s possible that BankRate is not the original source of that content. But a search for a snippet of that phrase shows BankRate as the likeliest source.

One Section from Google’s Mortgage Search FAQ: Screenshot from a chúng tôi Page:

The page is visible here.

How does Google’s Mortgage Search Work?

Google’s new mortgage information search provides multiple choices for finding more information about mortgages.

The information is designed to funnel consumers from every point of their mortgage research journey.

According to Google:

list of relevant documents and helpful tips from the CFPB. “

What is Google Mortgage Information Search?

The mortgage information search offers the following tools:

Mortgage calculator

Mortgage rate tool

Step by step mortgage tool

Videos with How-to and 101 level information

Mortgage Calculator Keyword

The mortgage calculator keyword phrase drives traffic to Google’s information search tool.

While Google previously had featured their own calculator, this change may represent a greater disruption in the mortgage calculator search engine results pages (SERPs).

The new mortgage information feature pushes organic listings further down the page.

Mortgage Related Videos

The mortgage related videos seem to be focused on how-to and beginner level information.  Those seeking to gain traffic via videos may do well to focus on that kind of video.

Disruption in Mobile Mortgage SERPs

This may cause disruption in the mobile SERPs for mortgage related keywords. This does not currently affect the desktop SERPs.

The disruption appears to be on general high level type keywords.

A search for Mortgage Rates will trigger the tool. A search Mortgage Rates Massachusetts will also trigger the tool.

But more granular searches like Mortgage Rates Northampton Massachusetts or Mortgage Rates Charlotte North Carolina do not trigger Google’s mortgage information search tool.

So it looks like local related and granular keywords will not trigger the tool.

Those seeking to pick up mortgage related traffic may want to consider pivoting to more granular keyword phrases.

What’s Next from Google?

Does this tool signal the future of Google search?

It’s possible that something like this might pop up in other finance and Your Money or Your Life related topics, where a complicated topic needs a more comprehensive approach.


Read Google’s announcement here:

Find Helpful Information on the Mortgage Process in Search

CFPB About Us Page

Five Questions About Taking Google’s New Phones To Work

Google unveiled a massive strategic shift on Tuesday, announcing that it is officially getting into the business of designing and releasing its own smartphones. The Pixel and Pixel XL, announced at a special event in San Francisco, are the company’s first forays into that market after working with outside manufacturers for several years to produce its Nexus line of devices.

The phones are snazzy gizmos packed with some of the latest features that Google could come up with, like a new intelligent assistant and a high-quality camera. It feels like one of the best Android smartphones on the market and could be a serious contender to take on Apple’s iPhone, especially for people looking to purchase a flagship smartphone.

Google’s launch answered a lot of questions that were floating around after details had leaked over several weeks. But so far the company has been quiet about how the new devices and features will work in enterprises. Here are the five key questions about Pixel phones for business:

How does the Google Assistant interact with device management software?

One of the biggest selling points of the Pixel is the new Google Assistant built into the device. It’s designed to provide people with a personalized search experience, based on the power of Google’s knowledge graph and integrations with partners. The assistant can help users do things like book dinner reservations, summon a car and, of course, search the web.

Google has a deep bench of partner integrations designed to bring features and information from different services into the Google Assistant. In some cases they don’t even require users to have the corresponding applications installed.

But how does that work in an environment where IT has strong opinions about which services should be used for business purposes and data? Google isn’t saying yet.

A help page for Google Now, the precursor to the Assistant, says it is disabled by default when used with a paid Google Cloud account. IT administrators can reinstate access to Google Now, but it’s not clear how much control they will have over what features and integrations users can access.

Will enterprises be able to build private extensions for the Assistant?

If enterprises can embrace the assistant, the next question is whether they’ll be able to build private integrations that work only with phones inside their organization. Giving employees phones that can provide them with enterprise data along with consumer features could be very powerful.

Google already does some of that with its Springboard app for paid users of G Suite, which is designed to help surface important files and facts from an organization’s stored files. By integrating Springboard with the Assistant, Google could provide interesting business functionality.

Does Google have any plans to sell Pixel phones to enterprises?

While Microsoft’s Surface Pro 3 was first pitched to consumers, the company quickly got into the business of working with partners to sell it as a business device. The Surface Enterprise Initiative has more than 10,000 partners selling Microsoft tablets to companies, and Microsoft continues to pour resources into it. The Pixel seems well-suited to receive similar treatment, especially as Google continues to build out its enterprise-focused business.

The company is working to push its G Suite productivity tools for enterprises, and it would seem like selling Pixel phones alongside those would make a ton of sense. But Google hasn’t said anything about its enterprise plans for the phone.

What’s not yet clear is how much control IT managers will have over the automatic updates. Google already allows companies using mobile device management software to control when updates get installed and whether users are notified of updates when they’re available. It seems likely Google would offer the same for the Pixel, but Google hasn’t said how device management features would work with the new update functionality.

Will 24/7 chat support for these devices increase or decrease enterprise appeal?

The other marquee feature for consumers is 24/7 chat support, which gives them an easy path to help. This seems like it would come in handy for helping employees when the IT helpdesk isn’t available, but it also presents a security problem.

Google’s support representatives can use screen-sharing built into the Pixel to see what’s going on with users’ devices. For those people who have sensitive company data on their Pixels, that could be an issue. Google hasn’t said whether administrators will be able to disable live chat, turn off the screen-sharing feature, block screen sharing of enterprise apps, or some combination of management features.

Still, the company has some time to answer all these questions. The phone was first made available for pre-order on Tuesday, but won’t be available at retail until Oct. 20.

Uninformed Search Algorithms: Exploring New Possibilities (Updated 2023)


Whoever it may be (humans or machine learning models) need to think of all possible ways to reach the goal state(if it exists) from the initial state or current state, all the consequences, etc. Similarly, AI systems or python programming uses various search algorithms for a particular goal state(if it exists) or for some problem-solving. ‘Uninformed Search’, as the name suggests, means the machine blindly follows the algorithm regardless of whether right or wrong, efficient or in-efficient.

These algorithms are brute force operations, and they don’t have additional information about the search space; the only information they have is on how to traverse or visit the nodes in the tree. Thus uninformed search algorithms are also called blind search algorithms. The search algorithm produces the search tree without using any domain knowledge, which is a brute force in nature. They don’t have any background information like informed search algorithms on how to approach the goal or whatsoever. But these are the basics of search algorithms in AI.

Learning Objectives

Learn to compare between them and choose the most befitting one for your model.

This article was published as a part of the Data Science Blogathon.

What is the Uninformed Search? Types of Uninformed Search Algorithms

The different types of uninformed search algorithms used in AI are as follows:

Depth First Search

Breadth-First Search

Depth Limited Search

Uniform Cost Search

Iterative Deepening Depth First Search

Bidirectional Search (if applicable)

But before we go into these search types and you go a step further wandering into any Artificial Intelligence course, let’s get to know the few terms which will be frequently used in the upcoming sections.

State: It provides all the information about the environment.

Goal State: The desired resulting condition in a given problem and the kind of search algorithm we are looking for.

Goal Test: The test to determine whether a particular state is a goal state.

Path/Step Cost: These are integers that represent the cost to move from one node to another node.

Space Complexity: A function describing the amount of space(memory) an algorithm takes in terms of input to the algorithm.

Time Complexity: A function describing the amount of time the algorithm takes in terms of input to the algorithm.

Optimal: Extent of preference of the algorithm

‘b‘ – maximum branching factor in a tree.

‘d‘ – the depth of the least-cost solution.

‘m‘ – maximum depth state space(maybe infinity)

Now let’s dive deep into each type of algorithm.

Depth First Search (DFS)

It is a search algorithm where the search tree will be traversed from the root node. It will be traversing, searching for a key at the leaf of a particular branch. If the key is not found, the searcher retraces its steps back (backtracking) to the point from where the other branch was left unexplored, and the same procedure is repeated for that other branch.

The above image clearly explains the DFS Algorithm. First, the search technique starts from the root node A and then goes to the branch where node B is present (lexicographical order). Then it goes to node D because of DFS, and from D, there is only one node to traverse, i.e., node H. But after node H  does not have any child nodes, we retrace the path in which we traversed earlier and again reach node B, but this time, we traverse through in the untraced path a traverse through node E. There are two branches at node E, but let’s traverse node I (lexicographical order) and then retrace the path as we have no further number of nodes after E to traverse. Then we traverse node J as it is the untraced branch and then again find we are at the end and retrace the path and reach node B and then we will traverse the untraced branch, i.e., through node C, and repeat the same process. This is called the DFS Algorithm.


DFS requires very little memory as it only needs to store a stack of the nodes on the path from the root node to the current node.

It takes less time to reach the goal node than the BFS algorithm [which is explained later](if it traverses in the right path).

There is the possibility that many states keep reoccurring, and there is no guarantee of finding a solution.

The DFS algorithm goes for deep-down searching, and sometimes it may go to the infinite loop.


It occupies a lot of memory space and time to execute when the solution is at the bottom or end of the tree and is implemented using the LIFO Stack data structure[DS].

Complete: No

Time Complexity: O(bm)

Space complexity: O(bm)

Optimal: Yes

Breadth-First Search (BFS)

This is another graph search algorithm in AI that traverses breadthwise to search for the goal in a tree. It begins searching from the root node and expands the successor node before expanding further along breadthwise and traversing those nodes rather than searching depth-wise.

The above figure is an example of a BFS Algorithm. It starts from the root node A and then traverses node B. Till this step, it is the same as DFS. But here, instead of expanding the children of B as in the case of DFS, we expand the other child of A, i.e., node C because of BFS, and then move to the next level and traverse from D to G and then from H to K in this typical example. To traverse here, we have only taken into consideration the lexicographical order. This is how the BFS Algorithm is implemented.


BFS will provide a solution if any solution exists.

If there is more than one solution for a given problem, then BFS will provide the minimal solution which requires the least number of steps.

It requires lots of memory since each level of the tree must be saved in memory to expand to the next level.

BFS needs lots of time if the solution is far away from the root node.


It requires a lot of memory space and is time-consuming if the goal state is at the bottom or end. It uses a FIFO queue DS to implement.

Complete: Yes (assuming b is finite)

Time Complexity: O(bd)

Space complexity: O(bd)

Optimal: Yes, if step cost = 1 (i.e., no cost/all step costs are same)

Uniform Cost Search Algorithm (UCS)

This algorithm is mainly used when the step costs are not the same, but we need the optimal solution to the goal state. In such cases, we use Uniform Cost Search to find the goal and the path, including the cumulative cost to expand each node from the root node to the goal node. It does not go depth or breadth. It searches for the next node with the lowest cost, and in the case of the same path cost, let’s consider lexicographical order in our case.

In the above figure, consider S to be the start node and G to be the goal state. From node S we look for a node to expand, and we have nodes A and G, but since it’s a uniform cost search, it’s expanding the node with the lowest step cost, so node A becomes the successor rather than our required goal node G. From A we look at its children nodes B and C. Since C has the lowest step cost, it traverses through node C. Then we look at the successors of C, i.e., D and G. Since the cost to D is low, we expand along with node D. Since D has only one child G which is our required goal state we finally reach the goal state D by implementing UFS Algorithm. If we have traversed this way, definitely our total path cost from S to G is just 6 even after traversing through many nodes rather than going to G directly where the cost is 12 and 6<<12(in terms of step cost). But this may not work with all cases.


Uniform cost search is an optimal search method because at every state, the path with the least cost is chosen.

It does not care about the number of steps or finding the shortest path involved in the search problem, and it is only concerned about path cost. This algorithm may be stuck in an infinite loop.


Complete: Yes (if b is finite and costs are stepped, costs are zero)

Space complexity: O(b(c/ϵ))

Optimal: Yes (even for non-even cost)

Depth Limited Search (DLS)

DLS is an uninformed search algorithm. This is similar to DFS but differs only in a few ways. The sad failure of DFS is alleviated by supplying a depth-first search with a predetermined depth limit. That is, nodes at depth are treated as if they have no successors. This approach is called a depth-limited search. The depth limit solves the infinite-path problem. Depth-limited search can be halted in two cases:

Standard Failure Value (SFV): The SFV tells that there is no solution to the problem.

Cutoff Failure Value (CFV): The Cutoff Failure Value tells that there is no solution within the given depth limit.

The above figure illustrates the implementation of the DLS algorithm. Node A is at Limit = 0, followed by nodes B, C, D, and E at Limit = 1 and nodes F, G, and H at Limit = 2. Our start state is considered to be node A, and our goal state is node H. To reach node H, we apply DLS. So in the first case, let’s set our limit to 0 and search for the goal.

Since limit 0, the algorithm will assume that there are no children after limit 0 even if nodes exist further. Now, if we implement it, we will traverse only node A as there is only one node in limit 0, which is basically our goal state. If we use SFV, it says there is no solution to the problem at limit 0, whereas LCV says there is no solution for the problem until the set depth limit. Since we could not find the goal, let’s increase our limit to 1 and apply DFS till limit 1, even though there are further nodes after limit 1. But those nodes aren’t expanded as we have set our limit as 1.

Hence nodes A, followed by B, C, D, and E, are expanded in the mentioned order. As in our first case, if we use SFV, it says there is no solution to the problem at limit 1, whereas LCV says there is no solution for the problem until the set depth limit 1. Hence we again increase our limit from 1 to 2 in the notion to find the goal.

Till limit 2, DFS will be implemented from our start node A and its children B, C, D, and E. Then from E, it moves to F, similarly backtracks the path, and explores the unexplored branch where node G is present. It then retraces the path and explores the child of C, i.e., node H, and then we finally reach our goal by applying DLS Algorithm. Suppose we have further successors of node F but only the nodes till limit 2 will be explored as we have limited the depth and have reached the goal state.

This image explains the DLS implementation and could be referred to for better understanding.

Depth-limited search can be terminated with two Conditions of failure:

Standard Failure: it indicates that the problem does not have any solutions.

Cutoff Failure Value: It defines no solution for the problem within a given depth limit.


Depth-limited search is Memory efficient.


Space complexity: O(bl)

Iterative Deepening Depth First Search (IDDFS)

It is a search algorithm that uses the combined power of the BFS and DFS algorithms. It is iterative in nature. It searches for the best depth in each iteration. It performs the Algorithm until it reaches the goal node. The algorithm is set to search until a certain depth and the depth keeps increasing at every iteration until it reaches the goal state.

In the above figure, let’s consider the goal node to be G and the start state to be A. We perform our IDDFS from node A. In the first iteration, it traverses only node A at level 0. Since the goal is not reached, we expand our nodes, go to the next level, i.e., 1 and move to the next iteration. Then in the next iteration, we traverse the node A, B, and C. Even in this iteration, our goal state is not reached, so we expand the node to the next level, i.e., 2, and the nodes are traversed from the start node or the previous iteration and expand the nodes A, B, C, and D, E, F, G. Even though the goal node is traversed, we go through for the next iteration, and the remaining nodes A, B, D, H, I, E, C, F, K, and G(BFS & DFS) too are explored, and we find the goal state in this iteration. This is the implementation of the IDDFS Algorithm.


It combines the benefits of BFS and DFS search algorithms in terms of fast search and memory efficiency.

The main drawback of IDDFS is that it repeats all the work from the previous phase.


Complete: Yes (by limiting the depth)

Time Complexity: O(bd)

Space complexity: O(bd)

Optimal: Yes (if step cost is uniform)

Systematic: It’s not systematic.

Bidirectional Search (BS)

Before moving into bidirectional search, let’s first understand a few terms.

Forward Search: Looking in front of the end from the start.

Backward Search: Looking from end to the start backward.

So Bidirectional Search, as the name suggests, is a combination of forwarding and backward search. Basically, if the average branching factor going out of node / fan-out, if fan-out is less, prefer forward search. Else if the average branching factor going into a node/fan-in is less (i.e., fan-out is more), prefer backward search. We must traverse the tree from the start node and the goal node, and wherever they meet, the path from the start node to the goal through the intersection is the optimal solution. The BS Algorithm is applicable when generating predecessors is easy in both forward and backward directions, and there exist only 1 or fewer goal states.

This figure provides a clear-cut idea of how BS is executed. We have node 1 as the start/root node and node 16 as the goal node. The algorithm divides the search tree into two sub-trees. So from the start of node 1, we do a forward search, and at the same time, we do a backward search from goal node 16. The forward search traverses nodes 1, 4, 8, and 9, whereas the backward search traverses through nodes 16, 12, 10, and 9. We see that both forward and backward search meets at node 9, called the intersection node. So the total path traced by forwarding search and the path traced by backward search is the optimal solution. This is how the BS Algorithm is implemented.


Since BS uses various techniques like DFS, BFS, DLS, etc., it is efficient and requires less memory.

Implementation of the bidirectional search tree is difficult.


Complete: Yes

Time Complexity: O(bd/2)

Space complexity: O(bd/2)

Optimal: Yes (if step cost is uniform in both forward and backward directions)

Final Interpretations

The Uninformed Search strategy for searching is a multipurpose strategy that combines the power of unguided search and works in a brute-force way. The algorithms of this strategy can be applied to a variety of problems in computer science as they don’t have the information related to state space and target problems, and they do not know how to traverse trees.


This is the complete analysis of all the Uninformed Search Strategies. Each search algorithm is no less than the other, and we can use any one of the search strategies based on the problem. The term ‘uninformed’ means that they do not have any additional information about states or state space. Thus we conclude “uninformed algorithm” is an algorithm that doesn’t use any prior knowledge or heuristics to solve a problem.

Key Takeaways

Uninformed algorithms are used in search problems, where the goal is to find a solution by exploring a large search space.

Uninformed algorithms are often simple to implement and can be effective in solving certain problems, but they may also be less efficient than informed algorithms that use heuristics to guide their search.

Frequently Asked Questions

Q1. What is an uninformed search algorithm in AI?

A. In the context of AI uninformed search algorithm is a type of search algorithm that is used to traverse a search space without the knowledge or heuristics about the problem being solved by it.

Q2. What are the types of uninformed search algorithms?

A. Types of uninformed search algorithms are Depth First Search, Breadth-First Search, Depth Limited Search, Uniform Cost Search, Iterative Deepening Depth First Search, and Bidirectional Search.

Q3. What is the difference between uninformed and informed search algorithms?

A. The difference between uninformed and informed search algorithms is that informed search algorithms use additional knowledge or heuristics to guide the search process, while uninformed search algorithms do not use any additional information.

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