Artificial Intelligence Introduction

Artificial Intelligence Project

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.

**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:

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: 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 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.

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.

Silan Software is one of the India's leading provider of offline & online training for Java, Python, AI (Machine Learning, Deep Learning), Data Science, Software Development & many more emerging Technologies.

We provide Academic Training || Industrial Training || Corporate Training || Internship || Java || Python || AI using Python || Data Science etc