Heuristic search techniques are used for problem-solving in AI systems. These techniques help find the most efficient path from a starting point to a goal, making them essential for applications such as navigation systems, game playing, and optimization problems.
- Heuristic search operates within the search space of a problem to find the best or near-optimal solution using systematic algorithms.
- Unlike brute-force methods, which exhaustively evaluate all possible solutions, heuristic search leverages heuristic information to guide the search toward more promising paths.
In this context, heuristics refer to a set of criteria or rules of thumb that provide an estimate of the most viable solution. By balancing exploration (searching new possibilities) and exploitation (refining known solutions), heuristic algorithms efficiently solve complex problems that would otherwise be computationally expensive.
#Components
- State Space: This implies that the totality of all possible states or settings, which is considered to be the solution for the given problem.
- Initial State: The instance in the search tree of the highest level with no null values, serving as the initial state of the problem at hand.
- Goal Test: The exploration phase ensures whether the present state is a terminal or consenting state in which the problem is solved.
- Successor Function: This create a situation where individual states supplant the current state which represent the possible moves or solutions in the problem space.
- Heuristic Function: The function of a heuristic is to estimate the value or distance from a given state to the target state. It helps to focus the process on regions or states that has prospect of achieving the goal.
#Algorithms
- A* Search
- Hill-climbing Search
- Simulated Annealing
- Greedy Best-First Search
- Beam Search
Greedy Best-First Search