### Solving Problems by Searching

An important Application of Artificial intelligence is Problem solving. Problem Solving Agents are goal-based agents and use atomic representation.
Steps involved in problem solving:

Define Problem Statements

Generating the solution by keeping different condition in mind.

Searching is the most commonly used technique of problem solving in artificial intelligence.

#### Steps Performed by Problem Solving agent:

Goal Formation: Goal formulation Based on the current Situation and the agent’s performance measure. It organizes steps required to achieve that goal.

Problem Formulation: Problem formulation is the process of deciding what actions should be taken to achieve the formulated goal.

Components involved in problem formulation: There are several components are involved in problem solution these are given below:

• • Initial State
• • Actions
• • Transition model
• • Goal test
• • Path cost

#### A Problem can be defined formally by five components.

• 1. The initial state that the agent starts in.
• 2. A description of possible actions available to the agent.
• 3. A description what each action does; the name for this is the transition model.
• 4. The goal test, which determines whatever a given state is a goal state. Sometimes there is an explicit set of possible goal states, and the test simply whether the given state is one of them.
• 5. A path Cost function that assigns a numeric cost function that reflects its own performance measure.

In Artificial Intelligence, Search techniques are universal problem-solving methods. Rational agents or Problem-solving agents in AI mostly used these search strategies or algorithms to solve a specific problem and provide the best result.

#### 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:

• 1. Search Space: Search space represents a set of possible solutions, which a system may have.
• 2. Start State: It is a state from where agent begins the search.
• 3. Goal test: It is a function which observe the current state and returns whether the goal state is achieved or not.

• Search tree: A tree representation of search problem is called Search tree. The root of the search tree is the root node which is corresponding to the initial state.

• Actions: It gives the description of all the available actions to the agent.

• Transition model: A description of what each action do, can be represented as a transition model.

• Path Cost: It is a function which assigns a numeric cost to each path.

• Solution: It is an action sequence which leads from the start node to the goal node.

• Optimal Solution: If a solution has the lowest cost among all solutions.

#### Properties of Search Algorithms:

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

• 1. 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.
• 2. 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 for is said to be an optimal solution.
• 3. Time Complexity: Time complexity is a measure of time for an algorithm to complete its task.
• 4. Space Complexity: It is the maximum storage space required at any point during the search, as the complexity of the problem.

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