Application of AI in Search Techniques: A Guide for Getting Quick Response !!

Abstract:

AI-powered search techniques include: 

 

Personalization

AI-powered search engines can provide personalized results for users based on their location, preferences, and behavior. 

 

Semantic search

This AI-powered search uses natural language processing to understand the meaning of a search and provide relevant results. For example, it can correct typos, find synonyms, and show similar products. 

 

ChatGPT

Bing uses generative AI models to provide more conversational and context-aware answers to user queries. 

 

Information retrieval

AI-powered algorithms can analyze user behavior and large amounts of data to generate more relevant search results. 

 

Some other types of search algorithms include:

Depth-First Search (DFS)

Breadth-First Search (BFS)

Uniform Cost Search (UCS)

Heuristic Search

Pathfinding

Optimization

Game Playing 

Keywords: 

Search Techniques,

Depth-First Search (DFS), Breadth-First Search (BFS), 

Uniform Cost Search (UCS),

Heuristic Search, Pathfinding

Optimization, Game Playing 

Learning Outcomes

After undergoing this article you will be able to understand the following:

1. Overview of Search Techniques

2. What's AI powered search techniques?

3. Why AI powered search techniques is gaining importance?

4. How AI powered search techniques works?

5. What's are the different types of AI powered search techniques?

6. What's the characteristics of AI powered Search Techniques?

7. Steps of AI powered search techniques

8. Techniques of AI powered search techniques

9. Advantages of AI powered search techniques

10. Limitations of AI powered search techniques

11. Strategies for optimising search techniques

12. Conclusions

13. FAQs

References

 1. Overview of Search Techniques

Search techniques are methods used to find relevant data efficiently. They can help narrow down the scope of data and reduce the time spent reviewing irrelevant material. Some search techniques include: 

 

Truncation

This technique allows you to search for all variants of a root word at once. For example, you can enter the root word followed by an asterisk (*) or question mark (?). 

 

Phrase searching

This technique treats a combination of search terms as a single phrase. To perform a phrase search, you can put the search terms in quotation marks. 

 

Boolean searching

This technique uses Boolean operators, like AND, OR, and NOT, to help narrow down or broaden search results. 

 

Google Advanced Search

This technique uses special characters and commands, known as "advanced operators", to find information on Google. 

 

Author searching

This technique is based on the idea that authors tend to write on similar topics in different articles or books. 

 

Basic search

This technique is a good way to get an initial overview of what is available on a topic. 

2. What's AI powered search techniques?

AI-powered search uses artificial intelligence (AI) to improve search engines' ability to understand and interpret user queries. Some AI-powered search techniques include: 

 

Natural language processing

Interprets queries by accounting for synonyms, abbreviations, typos, and more. 

 

Personalization

Delivers personalized results based on user behavior, preferences, and location. 

 

Vector search

Uses machine-learning models to detect semantic relationships between search queries and indexed information. 

 

Heuristic search

Uses heuristics, which are rules of thumb that probably lead to a solution.

3. Why AI powered search techniques is gaining importance?

Yes, search techniques are important because they can help you find relevant information more efficiently and save time: 

 

Improve search results: Search techniques can help you get more relevant results by narrowing down the scope of your search. 

 

Save time: Search techniques can save you time by making your search more efficient. 

 

Reduce costs: Search techniques can help you reduce costs by avoiding irrelevant material. 

 

Here are some search techniques you can use: 

 

Phrase searching: Use quotation marks to search for an exact phrase. 

 

Truncation: Use an asterisk to search for different word endings or variants of a word. 

 

Wild cards: Use a question mark to replace one or more characters in a term. 

 

Boolean operators: Use operators like AND, OR, and NOT to combine concepts and narrow or broaden your search results. 

 

Parentheses: Use parentheses to group search terms and create complex searches. 

 

You can learn more about search techniques by looking for help or instructions when you start using a new computerized resource. 

 

4. How AI powered search techniques works?

An agent is something that perceives and acts in an environment. Problem solving agents decide what to do by finding sequences of actions that lead to desirable states. The question is: how does such an agent find the best sequence of actions that achieves its goals when no single action can accomplish the task?

There are many search algorithms that can be used to solve different problems; however, some are more efficient than others. A group of general-purpose search algorithms are also known as uninformed or blind algorithms since they are not given no information about the problem other than its definition. Those include depth first search, depth first search and iterative deepening. Informed algorithms are those that have some idea of where to look for solutions to a given problem. Those include best first search and A*. These algorithms tend to make use of a heuristic to come up with a better, more accurate solution to the problem.

The first step in problem sovling is to correctly formulate the problem itself as well as the goal. The problem consists of four parts: the initial state, a set of actions, a goal test function and a path cost function. Once the problem is formulated, a solution is found by a search throughout the state space, or the set of all states reachable from the initial state. The path through the state space form the intinital state to goal state is a solution. Search algorithms are evaluated on the basis of completeness, optimality, time and space complexity.

Search techniques are used to find relevant information from information systems, which can be online or in-house. Some search techniques include: 

 

Linear search

A fundamental and simple search method that compares each element in a list to the item being searched. 

 

Interpolation search

An efficient technique that estimates the position of a key value in an array. It can be faster than binary search when the array elements are uniformly distributed. 

 

Ternary search

A technique that divides a sorted array into three parts to determine which part contains the element being searched for. 

 

Binary search

A technique that uses the divide and conquer algorithm to split an array into two halves. 

 

Jump search

A technique that uses a divide and conquer approach to search a sorted array by jumping a set number of components. 

 

Exponential search

A technique that finds a possible range for the target element and then performs a binary search within that range. 

5. What's are the different types of AI powered search techniques?

Some common types of search algorithms in artificial intelligence (AI) include: 

 

Depth-First Search (DFS)

This algorithm starts at the deepest level and works its way up, exploring as far as possible along each branch before backtracking. It's often used when a solution that covers many possibilities is needed. 

Depth-first is an unintelligent algorithm (i.e., no heuristic is used) which starts at an initial state and proceeds as follows:

1. Check if current node is a goal state.
2. If not, expand the node, choose a successor of the node, and repeat.
3.  If a node is a terminal state (but not a goal state), or all successors of a node have been checked, return to that node’s parent and try another successor.  (The failed states need not stay in memory.) 

The initial node is checked for a goal state, then expanded.	One of the initial node’s successors is chosen; it is checked, then expanded.	This process repeats until a goal state or a terminal state is reached.

If a terminal state is not a goal, the node is deleted, and the search returns to that node’s parent and tries a different successor.	If a node has no remaining successors, it is deleted itself (just as a node with no successors to begin with), and its parent tries another successor.	This process continues until a goal state is found, or every possibility is tried (i.e., all the successors of the initial node and the initial node itself fail to lead to a goal state).

The primary strength of the depth first search is its efficient use of memory—relatively few nodes need to be kept track of at any given time.  However, the depth first search has a number of important weaknesses: 

Þ    There is no guarantee that the solution this algorithm finds will be the “cheapest,” i.e. the one that requires the least number of steps from the initial state.  For example, in the above scenario, all possibilities using the initial state’s left successor will be considered before any possibilities using the right successor.  A solution starting with the left option and requiring 10, 100, or 1000 steps would be returned, even if the right option solved the problem in 1 step!

Þ    In some cases, a depth first search may get caught in an infinite loop and never find a viable solution.  Take a maze, for instance; even if the search is programmed to never go back the way it came, the search could easily go around in a circle without realizing it is covering the same ground over and over again.  Since a depth-first search evaluates possibilities in an arbitrary—but usually consistent—order (for instance, in navigating a maze, the algorithm might always attempt north, then east, then south, then west), if it arrives at a state it doesn’t realize it’s seen before, it will always make the same choice and keep looping.

Thus, depth first search is ideal when:

Þ    Efficient memory use is important

Þ    Any solution to the problem will do

Þ    There is little chance of infinite looping

 

Breadth-First Search (BFS)

This graph search algorithm starts at the root node and expands the successor node before expanding further along breadthwise. It's an uninformed search algorithm, meaning it doesn't use any domain knowledge to solve a problem. 

It is also known as an  unintelligent searching algorithm, breadth-first searching expands a node and checks each of its successors for a goal state before expanding any of the original node's successors (unlike depth-first search).

This last observation is key in understanding the primary usefulness of the breadth first search—the most efficient solution (or, at least, a solution at least as efficient as any other solution) is guaranteed, since all possible states reached in less moves have already been checked, and are known not to be goal states.  Related is the feature that a breadth-first search cannot get stuck in an infinite loop; since all possible states of a certain level are checked in order, the state(s) which break out of the loop will be evaluated and expanded (so will the ones in the loop, but unlike depth-first, there is no chance that the loop will be the only thing being checked—unless, of course, the only possible result from the initial state is an infinite loop).

Breadth-first’s primary drawback is in its use of memory.  Since all possible paths are being considered step-by-step in tandem, nodes are essentially never deleted from memory until a goal is found.  Also, since a breath-first search goes “deeper” much more slowly than a depth-first search, a problem with many solutions of all approximately the same depth will take much longer to solve with a breadth-first approach.  For instance, a maze like this

with two paths of approximately equal length will be solved twice as fast with a depth-first search, which goes all the way down one path and finds the solution, rather than taking alternating steps down each path (assuming the depth first search does not get caught in an infinite loop!).

Uniform Cost Search (UCS)

This method expands the least costly node first, similar to choosing paths based on toll costs. 

 

Heuristic Search

This algorithm uses heuristics, which are rules of thumb that are likely to lead to a solution. It's also called informed search or directed search. 

 

Iterative Deepening Search (IDS)

This algorithm increases the depth limit iteratively and executes the DFS until the goal node is found. 

An interesting observation is that the nodes in this search are first checked in the same order they would be checked in a breadth-first-search; however, since nodes are deleted as the search progresses, much less memory is used at any given time.

The drawback to the iterative deepening search is clear from the walkthrough--it can be painfully redundant, rechecking every node it has already checked with each new iteration. The algorithm can be enhanced to remember what nodes it has already seen, but this sacrifices most of the memory efficiency that made the algorithm worthwhile in the first place, and nodes at the maximum level for one iteration will still need to be re-accessed and expanded in the following iteration. Still, when memory is at a premium, iterative deepening is preferable to a plain depth-first search when there is danger of looping or the most efficient solution is desired.

 

Bidirectional Search

This algorithm replaces a single search graph with two smaller graphs, one starting from the initial state and one starting from the goal state. 

A*

Algorithm A* is a best-first search algorithm that relies on an open list and a closed list to find a path that is both optimal and complete towards the goal. It works by combining the benefits of the uniform-cost search and greedy search algorithms. A* makes use of both elements by including two separate path finding functions in its algorithm that take into account the cost from the root node to the current node and estimates the path cost from the current node to the goal node.

The first function is g(n), which calculates the path cost between the start node and the current node. The second function is h(n), which is a heuristic to calculate the estimated path cost from the current node to the goal node. F(n) = g(n) + h(n). It represents the path cost of the most efficient estimated path towards the goal. A* continues to re-evaluate both g(n) and h(n) throughout the search for all of the nodes that it encounters in order to arrive at the minimal cost path to the goal. This algorithm is extremely popular for pathfinding in strategy computer games.

The process for A* is basically this:
1. Create an open list and a closed list that are both empty. Put the start node in the open list.
2. Loop this until the goal is found or the open list is empty:
      a. Find the node with the lowest F cost in the open list and place it in the closed list.
      b. Expand this node and for the adjacent nodes to this node:
            i. If they are on the closed list, ignore.
            ii. If not on the open list, add to open list, store the current node as the parent for this adjacent node, and calculate the             F,G, H costs of the adjacent node.
            iii. If on the open list, compare the G costs of this path to the node and the old path to the node. If the G cost of using the             current node to get to the node is the lower cost, change the parent node of the adjacent node to the current node.             Recalculate F,G,H costs of the node.
3. If open list is empty, fail.

The terms locally finite, admissible, and monotonic all aid in the understanding of when A* can be expected to be complete, meaning that it finds a solution, and optimal, meaning that it finds the solution with the lowest path cost. A locally finite graph is one where none of the nodes on the graph have an infinite branching factor, thus none of the node paths branch forever. A branching factor of a node refers to the amount of new nodes that can be expanded from that node.

A heuristic is admissible if it is always optimistic; it either underestimates the path cost to the goal or provides a correct estimate for the path cost to the goal, but it never overestimates the path cost to the goal.

A heuristic is monotonic if in every path from the root to the goal the total estimated path cost does not decrease as the heuristic goes down a node tree. (illustration) Nonmonotonic heuristics can be made monotonic by the pathmax equation, which compares the estimated path cost of a node with the estimated path cost of its parent node. It then uses the higher path cost for estimation. Therefore, if the heuristic's estimated path cost decreases from one node to its child node, the pathmax equation uses the path cost of the parent node so that it is not evaluated as decreasing.

Fig.1 Illustration of a map featuring monoticity with contours at 380, 400 and 420.

A* is complete and optimal on graphs that are locally finite where the heuristics are admissible and monotonic.

A* must be locally finite, because if there exist an infinite amount of nodes where the estimated path cost, f(n), is less than the actual goal path cost then the algorithm could continue to explore these nodes without end, and it will be neither complete nor optimal.

How does monotocity affect A*'s completeness? Because A* is monotonic, the path cost increases as the node gets further from the root. Contours can be drawn to show areas where the estimated path cost, the f(n), for the nodes inside the areas are lower than or equal to the path cost for the outer bounds of the contours. These contours can be drawn as larger and larger areas that increase outwards as the f(n) for the outer bound of these contours increases. The first solution found is optimal since it is the first band where the f(n) for the contour is equal to the path cost for the goal. All the contours outside of this solution will have a higher f cost.

A*'s optimality is proved by contradiction. First, it is assumed that g is an optimal goal state with a path cost of f(g), that s is a suboptimal goal state with a path cost of g(s) > f(g), and that n is a node on an optimal path to g. We are assuming that A* selects s (the suboptimal goal) instead of n (the node on the optimal path) from the open list.

Since h is admissible, (optimistic), f(g) >= f(n). (The actual path cost is greater than or equal to the path cost estimated by the heuristic at n.)

If n is not chosen over s for expansion by A*, f(n) >= f(s). (The heuristic chooses the node with the lowest estimated F path cost.)

Thus, f(g) >= f(s).

Since s is a goal state, h(s) = 0. (The estimation from the current node to the final node must be 0.)
So f(s) = g(s). (f(s) = g(s) + h(s).)

Thus, f(g) >= g(s). This contradicts the statement that S is suboptimal so it must be true that A* never chooses a suboptimal path. Since A* only can have as a solution a node that it has selected for expansion, it is optimal.

 

Depth Limited Search:

Depth Limited Search is other prominent search algorithms that are widely used in various domains. 

6. What's the characteristics of AI powered Search Techniques?

Here are some characteristics of search techniques in artificial intelligence (AI): 

 

Heuristic search

Uses heuristics, which are rules of thumb that may lead to a solution. Heuristic search algorithms can distinguish non-goal states and direct their search towards more promising ones. 

 

Uninformed search

Also known as blind search, this technique solves queries systematically without any guidance or domain-specific knowledge. It relies on the structure of the search space to find a solution. 

 

Informed search

Uses domain-specific knowledge to find solutions faster by exploring fewer nodes. 

 

Local search

Uses algorithms like hill-climbing and simulated annealing to solve problems with discrete states and actions. Local searches are often more efficient and cost effective than global searches. 

 

Breadth-first search (BFS)

Explores all neighboring nodes of the initial node to find the shortest path between nodes in a tree-like data structure. 

 

Depth-first search (DFS)

Investigates a path as thoroughly as possible before turning around. 

 

A search*

Uses an evaluation function to calculate the cost of achieving the objective. It combines the benefits of both DFS and BFS. 

 

Genetic algorithms

Inspired by natural selection, these algorithms use evolutionary concepts to find the best answers to challenging situations. 

 

Deep learning

Mimics the behavior of the brain to handle data for detecting things, translating languages, recognizing speech, and making decisions. 

 

Semantic search

Uses natural language processing features to correct typos, find synonyms, and show similar products. 

 

7. Steps of AI powered search techniques

The steps for an effective search can include: 

 

Defining your search: Understand your topic and clearly define the question or problem you want to answer 

 

Identifying keywords: Consider the keywords related to your topic and pick out the most important words to describe it 

 

Using search techniques: Use Boolean operators, wildcards, or phrase searching to help you find more relevant results: 

 

Boolean operators: Use logic-based words like AND, OR, and NOT to narrow down or broaden your search 

 

Wildcards: Use wildcards to represent one or more other characters and expand your search results 

 

Phrase searching: Put your search terms in quotation marks to treat them as a single phrase 

 

Combining search terms: Use operators to link your search terms and define the relationship between them 

 

Selecting a tool: Choose the appropriate search tool for your needs 

 

Conducting your search: Perform your search 

 

Evaluating your results: Evaluate the search results you receive 

 

Re-searching: If needed, you can re-search your topic 

8. Techniques of AI powered search 

Some of the most common types of search algorithms include:

Depth-First Search (DFS) This algorithm explores as far as possible along each branch before backtracking. ...

Breadth-First Search (BFS) ...

Uniform Cost Search (UCS) ...

Heuristic Search. ...

Pathfinding. ...

Optimization. ...

Game Playing.

AI models are trained using a variety of techniques that enable them to learn from data and improve their performance. These techniques include supervised learning, unsupervised learning, semi-supervised learning, and image annotation. Each technique plays a crucial role in enhancing the capabilities of AI models.

Generative AI utilizes deep learning, neural networks, and machine learning techniques to enable computers to produce content that closely resembles human-created output autonomously. These algorithms learn from patterns, trends, and relationships within the training data to generate coherent and meaningful content.

9. Advantages of AI powered search techniques

Search techniques have many advantages, including: 

 

Speed

Some search techniques can be faster than others: 

 

Linear search: Can perform fast searches on small to medium lists, and doesn't require7 the list to be sorted. 

 

Binary search: Can be much quicker than a serial search because it halves the data being searched with each step. It has a logarithmic time complexity, which makes it significantly faster than linear search. 

 

Space saving

Some search techniques don't require any extra space: 

 

Binary search: Operates directly on the input data. 

 

Readability

Some search techniques are easy to understand: 

 

Binary search: Is easy to understand. 

 

Identifying relevant studies

A well-designed search strategy can ensure that all relevant studies are identified. 

 

Reducing bias

A well-designed search strategy can reduce bias towards certain types of research papers or outcomes. 

 

Increasing reproducibility and transparency

A well-designed search strategy can increase the reproducibility and transparency of a manuscript. 

 

10. Limitations of AI powered search techniques

There are several limitations to search techniques, including: 

 

Search algorithms

Search algorithms can have performance issues when the search space is too large or complex. They may also not always find the best solution in dynamic or incomplete search spaces. 

 

Search engines

Search engines have several limitations, including: 

 

Rankings: Search engines rank websites based on concepts like intrinsic authority, which can be flawed and manipulated. 

 

Filtering: Search engines filter results based on the information a user provides, which may not accurately reflect their interests. 

 

Keyword stuffing: Search engine rankings can be affected by keyword stuffing, which is when web pages include an unnaturally large number of search terms. 

 

Commercial interests: Search engine companies' commercial interests, such as their reliance on paid advertising and promotion of their own products, can affect rankings. 

 

Peer review

There are several limitations to peer review of search strategies, including: 

 

No consensus on search tactics 

 

No standards for reporting search strategies 

 

Search strategies may not have been reported with peer review in mind 

 

11. Strategies for optimising search techniques

Here are some strategies for optimizing search techniques: 

 

Content: Create high-quality, relevant content that includes your keyword phrase multiple times. Use headings to help users and search engines understand the structure of your content. 

 

Organization: Organize your site with descriptive URLs and group similar pages into directories. 

 

Links: Build high-quality backlinks from relevant websites, and avoid irrelevant or low-quality backlinks. 

 

Images: Add high-quality images with descriptive alt text near relevant text. 

 

Mobile optimization: Ensure your website is mobile-friendly and loads quickly. 

 

User experience: Avoid intrusive pop-ups, excessive ads, and broken links. 

 

Local SEO: Use location-specific keywords and phrases in your content, meta descriptions, and titles. 

 

Competitor analysis: Spy on your competitors' website traffic to identify common keywords and competitors. 

 

Zombie pages: Delete pages that don't bring in traffic. 

Here are some tips for making FAQs SEO-friendly: 

 

Use structured data: Implementing structured data can make your FAQ page more visually appealing in search results. It can also increase your click-through rate and help you stand out from competitors. 

 

Organize questions: Group similar questions together by topic or theme. For example, if you have an e-commerce site, you might group questions about shipping, returns, and product information. 

 

Use keywords: Use keywords and phrases that your target audience is searching for. You can incorporate these into your questions and answers to increase the chances of your content appearing in search results. 

 

Add a search option: If your FAQ page has more than a few questions, consider adding a search option to help visitors find information quickly. 

 

Write in simple language: Use a consistent tone of voice and write short, quick answers. 

 

Add internal links: Include internal links in your FAQ page. 

 

Update regularly: Keep your FAQ page up to date. 

 

12. Conclusions

All the searches we discuss have their niche in the body of problems we ask computer to solve-this is evidenced by their continued use and continued efforts to optimize them. There is no "ideal" search algorithm for every situation, or even a large number of situations. This, in part, is because questions which are answered by search are far from homogenous in nature. Two player games cannot be solved in the same way one would find an ideal route for a map; and even among such categories, the same search may not be as useful for one game (say, chess) as another (say, backgammon, which includes chance) .
A more interesting optimization is that of the heuristic devices common to intelligent search. One search may be slightly more efficient than another in a certain scenario, but designing a better heuristic will always speed things up-and even make things possible that otherwise were not. What can we do with better heuristics? Everything from smarter traffic lights to smarter video games.

In a field as broad and complex as artificial intelligence, it is often hard to have a good place to establish foundation for a basic understanding. These searches, though at the core of the artificial intelligence's most advanced theories and most notable achievements, are nonetheless comprehensible to relatively inexperienced computer scientists. We hope that from this basic understanding, knowledge of the deepest parts of this vast area can begin to grow.

13. FAQs

What are the 5 basic information search techniques?

Ans.

Effective Search Techniques are

• Keyword Searching. Use a keyword search to search all parts of a source for the words you enter in the search box. ...
• Boolean Searching. ...
• Subject Searching. ...
• Limiters. ...
• Phrase Searching. ...
• Using References/Works Cited Lists.

Q. Give examples of Broad and Narrow Search 

Ans. 

Examples of broad and narrow searches

Broad search: 

(Feeding and eating disorders OR anorexia nervosa OR bulimia nervosa OR binge-eating disorder) AND (family-based treatment OR family treatment OR parent-therapist alliance OR parent-focused treatment OR parent-child relations OR parents OR home treatment)

Narrow search: 

Anorexia nervosa AND Family-based treatment AND Adolescents

References