Greedy Search Algorithm
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Questions and Answers

What is the major drawback of Greedy search in terms of optimality?

  • It is only applicable for small search spaces.
  • It always finds the optimal solution.
  • It is never memory-efficient.
  • It may get stuck in local optima. (correct)

    • What is the primary advantage of Greedy search in terms of time complexity?

  • It is often computationally slow.
  • It is only suitable for large search spaces.
  • It is often computationally fast, especially for small search spaces. (correct)
  • It is guaranteed to find the optimal solution.

    • What is the key strategy used in Greedy search to guide the search?

  • Depth-first search technique.
  • Uninformed search strategies.
  • Heuristic function evaluation. (correct)
  • Breadth-first search approach.

    • What is the major limitation of Greedy search in terms of completeness?

    <p>It is not guaranteed to find a solution.</p> Signup and view all the answers

    What type of search strategy is Greedy search classified as?

    <p>Informed search strategy.</p> Signup and view all the answers

    What is the primary advantage of Iterative Deepening Search compared to Depth-First Search?

    <p>Retains the memory efficiency of DFS while guaranteeing the completeness of BFS</p> Signup and view all the answers

    What is the main disadvantage of Iterative Deepening Search in very large search spaces?

    <p>Redundant exploration of nodes at lower levels, increasing time complexity</p> Signup and view all the answers

    What is the primary reason why Depth-Limited Search is preferred over Depth-First Search in cyclic search spaces?

    <p>DLS prevents getting stuck in infinite loops</p> Signup and view all the answers

    What is a common limitation of Depth-Limited Search?

    <p>It may miss the solution if the depth limit is too shallow</p> Signup and view all the answers

    Which of the following search strategies is not guaranteed to find the shortest path in all cases?

    <p>Depth-First Search</p> Signup and view all the answers

    What is the primary concern of Uniform Cost Search in terms of path cost?

    <p>Finding the path with the lowest total cost</p> Signup and view all the answers

    What is a key characteristic of uninformed search strategies?

    <p>They use only the information available in the problem definition</p> Signup and view all the answers

    What dimension of evaluation measures the maximum number of nodes in memory?

    <p>Space complexity</p> Signup and view all the answers

    What is the time complexity of Uniform Cost Search?

    <p>High due to the possibility of expanding many nodes with low costs</p> Signup and view all the answers

    Which of the following is a characteristic of a complete search strategy?

    <p>It always finds a solution if one exists</p> Signup and view all the answers

    Which search strategy is optimal in terms of the total cost?

    <p>Uniform Cost Search (UCS)</p> Signup and view all the answers

    What is the primary advantage of using Breadth-First Search (BFS) over other search strategies?

    <p>It guarantees the shortest path to the goal in terms of number of edges</p> Signup and view all the answers

    What is the primary difference between Uniform Cost Search and Depth-First Search?

    <p>UCS uses a priority queue, while DFS uses a LIFO stack</p> Signup and view all the answers

    What is an advantage of Depth-First Search over Breadth-First Search?

    <p>It requires less memory compared to BFS</p> Signup and view all the answers

    What is the primary disadvantage of using Breadth-First Search (BFS) in large search spaces?

    <p>It requires significant memory space to store the frontier nodes</p> Signup and view all the answers

    What is the primary goal of a heuristic function in informed search algorithms?

    <p>To produce a solution in a reasonable time frame that is good enough</p> Signup and view all the answers

    Which of the following is a characteristic of a heuristic function in informed search algorithms?

    <p>It approximates the exact solution</p> Signup and view all the answers

    What is the relationship between the optimal solution cost of a relaxed problem and the optimal solution cost of the real problem?

    <p>The relaxed problem cost is no greater than the real problem cost</p> Signup and view all the answers

    Why are heuristics useful in search algorithms?

    <p>Because they produce a good enough solution in a reasonable time frame</p> Signup and view all the answers

    What is the purpose of using heuristics in conjunction with optimization algorithms?

    <p>To improve the time complexity of the search</p> Signup and view all the answers

    What characteristic of A* search guarantees that it finds the optimal solution if one exists?

    <p>It is both complete and optimal</p> Signup and view all the answers

    What is the primary advantage of using A* search over Greedy search in terms of optimality?

    <p>A* search is guaranteed to find the optimal solution, while Greedy search is not</p> Signup and view all the answers

    Which of the following statements is true about A* search?

    <p>It is a combination of the cost-so-far and the estimated cost-to-go</p> Signup and view all the answers

    What is the role of the heuristic function in A* search?

    <p>It guides the search by biasing it towards nodes that appear to be more promising</p> Signup and view all the answers

    What is the main difference between A* search and Greedy search in terms of decision-making?

    <p>A* search considers the entire search space, while Greedy search considers only local information</p> Signup and view all the answers

    What is the primary reason why A* search is considered optimal?

    <p>The heuristic function never overestimates the true cost</p> Signup and view all the answers

    What is the consequence of using an inconsistent heuristic function in A* search?

    <p>The search becomes non-optimal</p> Signup and view all the answers

    How does the heuristic function influence the search process in A* search?

    <p>It guides the search towards states that appear more promising</p> Signup and view all the answers

    What is a necessary condition for A* search to be complete?

    <p>The search space is finite</p> Signup and view all the answers

    How does the complexity of the heuristic function affect the memory and computation resources required by A* search?

    <p>It increases the required resources</p> Signup and view all the answers

    What is the primary advantage of A* search in terms of optimality?

    <p>It always finds the optimal solution with the minimum total cost</p> Signup and view all the answers

    What is the role of the heuristic function in A* search?

    <p>It estimates the cost or utility of reaching a goal state from a given state</p> Signup and view all the answers

    What is the consequence of using an inadmissible heuristic function in A* search?

    <p>The search becomes non-optimal</p> Signup and view all the answers

    How does the efficiency of A* search compare to greedy search?

    <p>A* search combines the efficiency of greedy search with the optimality of uniform-cost search</p> Signup and view all the answers

    What is the primary advantage of A* search in terms of time complexity?

    <p>It combines the efficiency of greedy search with the optimality of uniform-cost search</p> Signup and view all the answers

    Study Notes

    Strategies and Dimensions

    • Strategies are evaluated along the dimensions of completeness, time complexity, space complexity, and optimality
    • Time and space complexity are measured in terms of b (maximum branching factor of the search tree), d (depth of the least-cost solution), and m (maximum depth of the state space)

    Uninformed Strategies

    Breadth-First Search (BFS)

    • Explores all neighbor nodes at the present depth level before moving to the nodes at the next level
    • Uses a FIFO (First-In-First-Out) queue to store the frontier nodes
    • Guarantees the shortest path to the goal in terms of the number of edges
    • Requires significant memory space to store the frontier nodes, especially for large search spaces

    Uniform-Cost Search (UCS)

    • Expands the node with the lowest path cost (i.e., cumulative cost from the initial state) rather than just the shallowest node
    • Uses a priority queue ordered by the cumulative path cost to store the frontier nodes
    • Finds the optimal solution in terms of the least total cost
    • May expand many nodes with low costs before finding the optimal solution, leading to high time complexity

    Depth-First Search (DFS)

    • Explores as far as possible along each branch before backtracking
    • Uses a LIFO (Last-In-First-Out) stack to store the frontier nodes
    • Requires less memory compared to BFS as it only needs to store nodes along the current path
    • Not guaranteed to find the shortest path, can get stuck in infinite loops if the search space contains cycles

    Depth-Limited Search (DLS)

    • Begins at the base node and recursively examines every node at its current level before heading to the next level
    • Terminates the search when it reaches a specified depth limit, preventing infinite loops
    • Prevents DFS from getting stuck in infinite loops in cyclic search spaces
    • May miss the solution if the depth limit is too shallow

    Iterative Deepening Search (IDS)

    • Combines DFS and DLS
    • Repeatedly performs DFS with increasing depth limits until the goal state is found
    • Retains the memory efficiency of DFS while guaranteeing the completeness of BFS
    • Redundant exploration of nodes at lower levels can increase time complexity, especially in large search spaces
    • Looks for the fastest/slowest growth/descent towards the goal node before the expansion
    • Selects the node that appears to be the most promising at each step
    • Prioritizes nodes that are closest to the goal state according to a heuristic function
    • Does not guarantee optimality or completeness
    • May get stuck in local optima or fail to find the optimal solution if the heuristic function is not admissible or leads the search down the wrong path
    • Combines the efficiency of greedy search with the optimality of uniform-cost search
    • Uses a heuristic function that estimates the cost or distance from a given state to the goal state
    • Selects the node with the lowest combination of the cost-so-far (g-value) and the estimated cost-to-go (h-value) at each step
    • Prioritizes nodes that have a lower total estimated cost (f-value = g-value + h-value), aiming to minimize the total cost to reach the goal state
    • Is complete and optimal, guaranteeing to find the optimal solution with the minimum total cost if the heuristic function is admissible and consistent

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