A* Search and Heuristic Functions

What does the 'g(n)' function represent in A search?

What is the purpose of the heuristic function in search algorithms?

Which search algorithm is guaranteed to find the shortest path if a solution exists?

What type of search algorithm uses both g(n) and h(n) to find an optimal solution?

What defines an admissible heuristic?

What characteristic defines a consistent heuristic?

What critical advantage does A* search algorithm provide over other search algorithms?

What is the primary purpose of Alpha-Beta pruning in search tree evaluation?

What primary function does the minimax algorithm serve in two-player games?

Which player in a minimax scenario actively tries to increase their score?

In the context of depth-limited minimax, what is the evaluation function used to achieve?

What types of scenarios does adversarial search specifically deal with?

What is the strategic goal of the MIN player in adversarial search?

A* Search

g(n) represents the cost to reach a specific node from the initial state in A* search.
h(n) represents the estimated cost to reach the goal from the current node in A* search.
Breadth-first search guarantees finding the shortest path if a solution exists.
Uniform-cost search expands the node with the lowest total path cost so far.

Heuristic Functions

Heuristic functions estimate the cost from the current node to the goal.
A search* uses both g(n) and h(n) to find an optimal solution.
An admissible heuristic never overestimates the true cost to reach the goal.
A consistent heuristic satisfies the condition: h(n) ≤ h(n') + cost(n, n'), meaning the estimated cost from a node is less than or equal to the estimated cost from a successor node plus the cost to reach that successor node.
The main advantage of A search* is that it guarantees finding the shortest path if the heuristic is admissible and consistent.

Optimization Techniques

Alpha-Beta Pruning reduces the number of nodes evaluated in a minimax algorithm by pruning branches that are guaranteed to be worse than already explored branches.

Minimax Algorithm

Minimax evaluates the optimal strategy in two-player games.
The MAX player aims to maximize their own score.
The MIN player aims to minimize the MAX player’s score.
The evaluation function in depth-limited minimax estimates the utility of a game state when the search is cut off.

Game Search and Adversarial Search

Adversarial search addresses multi-agent competitive environments.
The MAX player aims to maximize their own score in adversarial search.

This quiz covers A* search algorithms and heuristic functions. It delves into key concepts such as the cost functions g(n) and h(n), the significance of admissible and consistent heuristics, and the advantages of the A* search method. Test your understanding of these essential algorithmic topics.