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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