Artificial Intelligence Search Algorithms

These flashcards cover key concepts related to search algorithms and artificial intelligence, focusing on definitions, algorithm characteristics, and comparisons.

What is the primary difference between uninformed search and informed search in AI?

Uninformed search does not use additional information beyond the problem definition, while informed search uses problem-specific knowledge to guide the search towards the goal.

Define Problem Space in the context of search algorithms.

Problem Space is the environment consisting of a set of states and a set of actions that can change those states.

What does Space Complexity refer to in search algorithms?

Space Complexity refers to the maximum number of nodes that are stored in memory during the execution of the search algorithm.

What is the main disadvantage of Breadth-First Search?

The main disadvantage is that it consumes a lot of memory because it keeps all nodes at the current level in memory to create the next level.

Describe Depth-First Search (DFS).

Depth-First Search explores the deepest nodes first, moving down a branch before backtracking, and uses a stack data structure.

What are the characteristics of the A* search algorithm?

The A* search algorithm is a path-finding algorithm that expands the most promising paths first, using both the actual cost from the start node and an estimated cost to the goal.

Explain what a Greedy search algorithm does.

A Greedy search algorithm chooses the next node based on local optimization, picking the node that appears to be closest to the goal state.

What is an Admissible heuristic?

An Admissible heuristic never overestimates the cost of reaching the goal, ensuring that optimal solutions are found.

What is the role of a heuristic function in search algorithms?

A heuristic function estimates the cost from a given node to the goal, guiding the search algorithm in exploring promising paths.

What is the primary use of Dijkstra's algorithm in AI?

Dijkstra's algorithm is used to find the shortest path in weighted graphs, always selecting the node with the smallest cost.