Breadth First Search (BFS): BFS expands the leaf node with the lowest path cost so far, and keeps going until a goal node is generated. If the path cost simply equals the number of links, we can implement this as a simple queue (“first in, first out”). his is guaranteed to find an optimal […]
Depth First Search (DFS): DFS expands the leaf node with the highest path cost so far, and keeps going until a goal node is generated. If the path cost simply equals the number of links, we can implement this as a simple stack (“last in, first out”). This is not guaranteed to find any path […]
Depth Limited Search (DLS): DLS is a variation of DFS. If we put a limit l on how deep a depth first search can go, we can guarantee that the search will terminate (either in success or failure). If there is at least one goal state at a depth less than l, this algorithm is guaranteed […]
Depth First Iterative Deepening Search (DFIDS): DFIDS is a variation of DLS. If the lowest depth of a goal state is not known, we can always find the best limit l for DLS by trying all possible depths l = 0, 1, 2, 3, … in turn, and stopping once we have achieved a goal state. […]
Suppose that, for each node n in a search tree, an evaluation function f(n) is defined as the sum of the cost g(n) to reach that node from the start state, plus an estimated cost h(n) to get from that state to the goal state. That f(n) is then the estimated cost of the cheapest […]
The basic idea of hill climbing is simple: at each current state we select a transition, evaluate the resulting state, and if the resulting state is an improvement we move there, otherwise we try a new transition from where we are. We repeat this until we reach a goal state, or have no more transitions […]
The DFS and BFS strategies for OR trees and graphs can be adapted for And-Or trees The main difference lies in the way termination conditions are determined, since all goals following an And node must be realized, whereas a single goal node following an Or node will do A more general optimal strategy is AO* (O […]
The AO* algorithm is a search algorithm used primarily for problem-solving in directed acyclic graphs (DAGs). It is particularly useful in scenarios where the search space can be represented as a graph with nodes representing states and edges representing actions or transitions between those states. AO* is an extension of the A* algorithm, designed to […]
Uninformed search algorithms, also known as blind search algorithms, explore the search space without any domain-specific knowledge. They are fundamental in artificial intelligence for solving problems where the solution path is not known. Here’s a comparison of some common uninformed search algorithms: 1. Breadth-First Search (BFS) Description: Explores all nodes at the present depth level […]
Best-First Branch-and-Bound is an optimization algorithm used to solve combinatorial search problems, where the goal is to find the optimal solution among many candidates. It combines the principles of branch-and-bound with best-first search. Components: Branching: The problem is broken down into smaller subproblems (branches) that can be more easily solved or evaluated. Bounding: A bound […]
