Maze Solver: BFS and DFS - AI Lab CS 39002

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Questions and Answers

In the context of the maze solver assignment, why might the path found by DFS not be the shortest path?

DFS explores paths to their full depth before exploring other branches, it doesn't guarantee finding the shortest path. It reports the valid path found first, not necessarily the path with the fewest steps.

Explain how bi-directional BFS can improve the efficiency of route-finding compared to standard BFS, particularly in the context of Assignment 2.

Bi-directional BFS searches from both the start and end points simultaneously, meeting in the middle. This can significantly reduce the number of nodes explored, as the search space expands exponentially from both ends, leading to faster convergence.

In the treasure-seeking task (Assignment 3), what is the role of the heuristic in Best-First Search, and how does the choice of heuristic influence the search process?

The heuristic estimates the distance or cost from the current cell to the treasure. The choice of heuristic influences the search process by guiding the algorithm towards cells that appear closer to the treasure, affecting both the path taken and the efficiency of the search.

In Uniform Cost Search (Assignment 4), how does it differ from BFS, and when is Uniform Cost Search preferred?

Uniform Cost Search explores paths based on the cumulative cost from the starting node, while BFS explores based on the number of edges. Uniform Cost Search is preferred when the edges have varying costs, as it guarantees finding the lowest-cost path. Signup and view all the answers

In the 8-puzzle problem (Assignment 5), what is the purpose of defining heuristic functions like 'number of misplaced tiles' and 'sum of Manhattan distances', and how do they contribute to the efficiency of the A* search algorithm?

Heuristic functions estimate the remaining cost to reach the goal state, guiding A* to explore more promising states first. 'Number of misplaced tiles' and 'sum of Manhattan distances' heuristics directs the A* search toward the goal, reducing the number of explored nodes and improving efficiency. Signup and view all the answers

Explain how you would adapt the A* search algorithm for a robot path planning problem (Assignment 6) in a 2D grid with obstacles, specifically focusing on how the heuristic function is used and how the algorithm avoids obstacles.

The heuristic function estimates the distance from the current cell to the goal, considering the grid. To avoid obstacles, valid moves in A* consider only unblocked adjacent cells, and paths through blocked cells are not explored. The algorithm maintains a closed list to prevent revisiting already explored grid locations. Signup and view all the answers

How can BFS and DFS be adapted to explore game states in a simple turn-based game like Tic-Tac-Toe (Assignment 7)? Discuss their limitations in this context.

BFS and DFS can be used to explore all possible game states by treating each state as a node in a search tree. BFS guarantees finding the shortest path to a win, while DFS explores deeply but doesn't guarantee optimality. However in practice their performance can be poor because they cannot look at the values of future game states in order to make correct decisions. Signup and view all the answers

In Assignment 8, describe how you would modify the A* search algorithm to handle multiple goals with differing priorities or costs in a navigation problem.

Modify A* by adjusting the cost function to incorporate the priority or cost of each goal, considering the order in which goals must be achieved. This can be implemented by adding the goal's cost to the path cost when that goal is reached, influencing the search to favor paths that achieve higher-priority goals earlier. Signup and view all the answers

How is the A* search algorithm applied in Assignment 9 to optimize task scheduling, and what role does the heuristic play in guiding the search for an optimal schedule?

A* is used to find the optimal task schedule by treating each possible schedule as a state. The heuristic estimates the remaining time to complete all tasks, guiding A* to explore schedules that minimize the total completion time. The algorithm can then choose an optimal schedule by choosing the schedule with the lowest cost (time). Signup and view all the answers

When comparing search algorithms like BFS, DFS, and A* (Assignment 10), what metrics would be most important to consider, and explain why each is significant.

Important metrics include efficiency (nodes explored, time taken, memory usage) and optimality (guarantee of finding the best solution). Efficiency is important for practical applicability, while optimality ensures the solution is the best possible within the given constraints. Signup and view all the answers

In the context of solving AI-based search problems, explain the concept of local optimization techniques like Hill Climbing, and discuss their strengths and limitations.

Hill Climbing iteratively improves a solution by making small changes and accepting those that improve the objective function. Strengths: simple and efficient for some problems. Limitations: can get stuck in local optima, failing to find the global optimum. Signup and view all the answers

Explain how a Genetic Algorithm (GA) can be applied to solve AI-based search problems, detailing the roles of key GA components such as selection, crossover, and mutation.

A Genetic Algorithm uses a population of candidate solutions and evolves them over generations. Selection chooses the best solutions to reproduce, crossover combines the genetic material of two parents to create offspring, and mutation introduces random changes to maintain diversity and avoid premature convergence. Signup and view all the answers

In the Water-Jugs problem (Assignment 13), how does the Minimax algorithm determine the optimal move for the AI player, and what role does the utility function play in this process?

The Minimax algorithm explores all possible moves for both the AI and the opponent. The utility function evaluates the state resulting from each move. The AI aims to maximize its score, assuming the opponent plays optimally to minimize the AI's score. Signup and view all the answers

In the 4-Queens problem (Assignment 14), describe how alpha-beta pruning enhances the efficiency of the Minimax algorithm.

Alpha-beta pruning eliminates branches of the search tree that are guaranteed to be worse than the best already found. It avoids evaluation of branches that could not possibly influence the final decision, significantly speeding up the search. Signup and view all the answers

Explain how transforming the Tower of Hanoi into a competitive two-player game (Assignment 15) allows for the implementation of adversarial search strategies.

By turning the problem into a competitive game, each player aims to move the largest disk to the target rod. It requires each player to consider the other player's possible responses and optimize their moves to win the game. Signup and view all the answers

Flashcards

DFS (Depth-First Search)

A search algorithm that explores all possible paths and reports one valid path, not necessarily the shortest.

BFS (Breadth-First Search)

A search algorithm that finds the shortest path by exploring all neighbors at the present depth prior to moving on to the nodes at the next depth level.

Bi-Directional Search

A search algorithm that minimizes the number of explored nodes by searching from both the initial and goal states simultaneously.

Best-First Search

A search algorithm that finds a treasure in grid, and each cell has a heuristic value representing its "closeness" to the treasure.