Lecture 3 Flashcards
Informed Search Algorithms
What is an informed search algorithm?
An algorithm that uses problem-specific knowledge (heuristics) to search more intelligently for a goal state compared to uninformed methods.
What are the four performance measures for comparing search algorithms?
Completeness, Optimality, Time Complexity, Space Complexity.
How does Breadth-First Search (BFS) work, and what are its properties?
Explores all nodes level by level; it’s complete and optimal for uniform step costs but has high space complexity.
What is the main advantage of Iterative Deepening Search (IDS) over BFS and DFS?
Combines DFS’s low memory usage with BFS’s optimality.
What is the key difference between Greedy Best-First Search and A* Search?
Greedy uses h(n) (heuristic only), while A* uses f(n) = g(n) + h(n) (cost so far + heuristic).
What is a heuristic function?
A rule or measure used to estimate how close a node is to the goal.
What does it mean for a heuristic to be admissible?
It never overestimates the true cost to reach the goal, ensuring optimality.
Give two examples of heuristic functions.
Straight-Line Distance (SLD) and Manhattan Distance.
What issue can occur with Greedy Best-First Search, and why?
It may get stuck in loops or fail to find a solution because it doesn’t consider the cost so far (g(n)).
What is the difference between uninformed and informed search algorithms?
Uninformed search algorithms do not use additional problem-specific knowledge, while informed search algorithms use heuristics to guide the search process more effectively.
What is the fringe in a search algorithm?
The fringe is a data structure that stores nodes that are generated but not yet expanded during the search process.
When would you use a goal-driven approach over a data-driven approach?
Use a goal-driven approach when the goal is well-defined and the branching factor is smaller in the backward direction.
What is the main limitation of Depth-First Search (DFS)?
DFS may fail to find a solution if the search space is infinite, and it is not guaranteed to find the optimal solution.
How does Iterative Deepening Search handle memory differently compared to Breadth-First Search?
Iterative Deepening Search uses less memory by discarding nodes after each depth-limited iteration, while Breadth-First Search retains all nodes in memory.
Why is Greedy Best-First Search incomplete and not optimal?
It focuses only on the heuristic value (h(n)) without considering the cost so far (g(n)), which can lead to loops or dead ends.
Why is it important for a heuristic to be monotonic?
Monotonicity ensures that the estimated cost (heuristic) does not decrease as nodes are expanded, maintaining local admissibility and preventing suboptimal paths.
How can the choice of heuristic impact the performance of A* search?
A good heuristic can significantly reduce the number of nodes expanded, improving efficiency, while a bad heuristic can increase computational cost or lead to incorrect results.
What is the main advantage of A* Search over Greedy Best-First Search?
A* considers both the cost to reach a node (g(n)) and the estimated cost to reach the goal (h(n)), ensuring both completeness and optimality.
How does Beam Search sacrifice solution quality for efficiency?
Beam Search limits the number of nodes stored and expanded by only considering the k best nodes at each level, which may miss the optimal solution.
Which algorithm guarantees the least memory usage, and why?
Depth-First Search guarantees the least memory usage because it only stores nodes along the current path.
How does stochastic beam search differ from traditional beam search?
Stochastic beam search introduces randomness when selecting nodes, which reduces the risk of getting stuck in local maxima.
What is an evaluation function in informed search algorithms?
An evaluation function is a mathematical expression used to rank nodes during search, typically combining g(n) and h(n).
What does it mean for a heuristic to be admissible?
It means the heuristic never overestimates the true cost to reach the goal, ensuring the solution is optimal.
What is monotonicity in the context of heuristics?
Monotonicity ensures that for any node, the estimated cost to the goal is not less than the cost of reaching a successor plus the successor’s cost to the goal.
What is Straight-Line Distance (SLD) and how is it used in search algorithms?
Straight-Line Distance (SLD) is the shortest distance between two points, used as an admissible heuristic in search algorithms.
What is Manhattan Distance and when is it used as a heuristic?
Manhattan Distance measures the total number of horizontal and vertical steps required to reach the goal, commonly used in grid-based searches.
What does it mean for an algorithm to be complete?
Completeness means the algorithm will always find a solution if one exists.
What does it mean for an algorithm to be optimal?
Optimality means the algorithm guarantees finding the best (least-cost) solution.
