What is an Informed Search in AI?

 algorithms have information on the goal state which helps in more efficient searching. This information is obtained by a function that estimates how close a state is to the goal state. Informed search in AI is a type of search algorithm that uses additional information to guide the search process, allowing for more efficient problem-solving compared to uninformed search algorithms. This information can be in the form of heuristics, estimates of cost, or other relevant data to prioritize which states to expand and explore. Examples of informed search algorithms include A* search, Best-First search, and Greedy search. Example: Greedy Search and Graph Search.

Here are some key features of informed search algorithms in AI:

• Use of Heuristics – informed search algorithms use heuristics, or additional information, to guide the search process and prioritize which nodes to expand.
• More efficient – informed search algorithms are designed to be more efficient than uninformed search algorithms, such as breadth-first search or depth-first search, by avoiding the exploration of unlikely paths and focusing on more promising ones.
• Goal-directed – informed search algorithms are goal-directed, meaning that they are designed to find a solution to a specific problem.
• Cost-based – informed search algorithms often use cost-based estimates to evaluate nodes, such as the estimated cost to reach the goal or the cost of a particular path.
• Prioritization – informed search algorithms prioritize which nodes to expand based on the additional information available, often leading to more efficient problem-solving.
• Optimality – informed search algorithms may guarantee an optimal solution if the heuristics used are admissible (never overestimating the actual cost) and consistent (the estimated cost is a lower bound on the actual cost).