Informed Search
Afjal H Sarower Lecturer
Daffodil International University
Informed Search: Informed Search 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.
Uninformed Search: Uninformed search algorithms have no additional information on the goal node other than the one provided in the problem definition. The plans to reach the goal state from the start state differ only by the order and length of actions.
Heuristics function: Heuristic is a function which is used in Informed Search, and it finds the most promising path.
Heuristic function estimates how close a state is to the goal. It is represented by h(n), and it calculates the cost of an optimal path between the pair of states. The value of the
heuristic function is always positive.
Heuristic Search Tries to optimize a problem using Heuristic function.
Best-first Search Strategies
• Best-first is a family of search strategies, each with a different evaluation function
• Typically, strategies use estimates of the cost of reaching the goal and try to minimize it
Best-first Search Strategies
• Best-first is a family of search strategies, each with a different evaluation function
• Typically, strategies use estimates of the cost of reaching the goal and try to minimize it
• Greedy Search
• A*
Greedy Best-first Search Strategies
• Greedy best-first search algorithm always selects the path which appears best at that moment. It is the combination of depth-first search and breadth-first search algorithms.
In the best first search algorithm, we expand the node which is closest to the goal node and the closest cost is estimated by
heuristic function
