Search Algorithms in Artificial Intelligence

Search algorithms in AI are the algorithms that are created to aid the searchers in getting the right solution. The search issue contains search space, first start and end point. Now, by performing simulations of scenarios and alternatives, searching algorithms help AI agents find the optimal state for the task.

Logic used in algorithms processes the initial state and tries to get the expected state as the solution. Because of this, AI machines and applications just functioning using search engines and solutions that come from these algorithms can only be as effective as the algorithms.

AI agents can make the AI interfaces usable without any software literacy. The agents that carry out such activities do so with the aim of reaching an end goal and developing action plans that, in the end, will bring the mission to an end. Completion of the action is gained after the steps of these different actions. The AI agents find the best way through the process by evaluating all the alternatives that are present. Search systems are a common task in artificial intelligence by which you are going to find the optimum solution for the AI agents.

Problem-solving Agents:

In Artificial Intelligence, Search techniques are universal problem-solving methods. Rational agents or Problem-solving agents in AI mostly use these search strategies or algorithms to solve a specific problem and provide the best result. Problem-solving agents are the goal-based agents and use atomic representation. In this topic, we will learn various problem-solving search algorithms.

Properties of Search Algorithms:

Following are the four essential properties of search algorithms to compare the efficiency of these algorithms:

• Completeness: A search algorithm is said to be complete if it guarantees to return a solution if at least any solution exists for any random input.
• Optimality: If a solution found for an algorithm is guaranteed to be the best solution (lowest path cost) among all other solutions, then such a solution is said to be an optimal solution.
• Time Complexity: Time complexity is a measure of time for an algorithm to complete its task.
• Space Complexity: It is the maximum storage space required at any point during the search, as the complexity of the problem.

Importance of Search Algorithms in Artificial Intelligence

Here are some important factors of the role of search algorithms used by AI as follows.

1. Solving problems:

"Workflow" logical search methods like describing the issue, getting the necessary steps together and specifying an area to search help AI search algorithms get better at solving problems. Take, for instance, the development of AI search algorithms that support applications like Google Maps by finding the fastest way or shortest route between given destinations. These programs basically search various options to find the best solution possible.

2. Search programming:

Many AI functions can be designed as search oscillations, which thus specify what to look for in formulating the solution to the given problem.

3. Goal-based agents:

Instead, the goal-directed and high-performance systems use a wide range of search algorithms to improve the efficiency of AI. Though they are not robots, these agents look for the ideal route for action dispersion so as to avoid the most impacting steps that can be used to solve a problem. It is their main aims to come up with an optimal solution which takes into account all possible factors.

4. Support production systems:

AI Algorithms in search engines for systems manufacturing help them run faster. These programmable systems assist AI applications with applying rules and methods, thus making an effective implementation possible. Production systems involve learning of artificial intelligence systems and their search for canned rules that lead to the wanted action.

5. Neural network systems:

Beyond this, employing neural network algorithms is also of importance to neural network systems. The systems are composed of these structures: a hidden layer, an input layer, an output layer, and interconnected nodes. One of the most important functions offered by neural networks is to address the challenges of AI within any given scenario. AI is somehow able to navigate the search space to find the connection weights that will be required in the mapping of inputs to outputs. This is made better by search algorithms in AI.

Search Algorithm Terminologies

• Search: Searching is a step-by-step procedure to solve a search problem in a given search space. A search problem can have three main factors:
  • Search Space: Search space represents a set of possible solutions that a system may have.
  • Start State: It is a state from where the agent begins the search.
  • Goal test: It is a function that observes the current state and returns whether the goal state is achieved or not.
• Search Tree: A tree representation of a search problem is called a Search tree. The root of the search tree is the root node, which corresponds to the initial state.
• Actions: It describes all the available actions to the agent.
• Transition Model: A description of what each action does can be represented as a transition model.
• Path Cost: It is a function that assigns a numeric cost to each path.
• Solution: It is an action sequence that leads from the start node to the goal node.
• Optimal Solution: If a solution has the lowest cost among all solutions.

Types of Search Algorithms

Based on the search problems we can classify the search algorithms into uninformed (Blind search) search and informed search (Heuristic search) algorithms.

Uninformed/Blind Search

The uninformed search does not contain any domain knowledge, such as closeness or the location of the goal. It operates in a brute-force way as it only includes information about how to traverse the tree and how to identify leaf and goal nodes. Uninformed search applies a way in which a search tree is searched without any information about the search space, like initial state operators and tests for the goal, so it is also called blind search. It examines each node of the tree until it achieves the goal node.

It can be divided into six main types:

• Breadth-first search
• Uniform cost search
• Depth-first search
• Depth limited search
• Iterative deepening depth-first search
• Bidirectional Search

Informed Search

Informed search algorithms use domain knowledge. In an informed search, problem information is available, which can guide the search. Informed search strategies can find a solution more efficiently than an uninformed search strategy. An informed search is also called a Heuristic search.

A heuristic is a way that might not always be guaranteed for the best solutions but guaranteed to find a good solution in a reasonable time.

Informed search can solve many complex problems that could not be solved in another way.

An example of informed search algorithms is a travelling salesman problem.

• Greedy Search
• A* Search

Conclusion

Search algorithms in Artificial Intelligence (AI) are crucial tools designed to solve complex search problems by navigating through a defined search space that includes a start state and a goal state. These algorithms play a vital role in addressing various AI challenges and also enhance the performance of related systems such as neural networks and manufacturing processes. A typical search algorithm first defines the problems, which involve elements like the initial state, target state, state space, and path cost. It then executes systematic operations to identify the optimal solution.

In AI, search algorithms are broadly categorized into informed and uninformed types. Uninformed algorithms-such as Breadth-First Search, Depth-First Search, and Uniform-Cost Search, explore the search space without any prior knowledge of the goal. In contrast, informed algorithms, including Greedy Search, A* Tree Search, and A* Graph Search, use heuristic information to guide the search more efficiently. These algorithms are widely applied in real-world domains such as vehicle routing, nurse scheduling, document retrieval, and various industrial processes, making them foundational components of intelligent systems.