Uninformed Search Strategies in AI
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Questions and Answers

What is the main purpose of a heuristic function in informed search strategies?

  • To provide an estimate of the cost to reach the goal from a given state (correct)
  • To represent the cost incurred in moving from one state to another
  • To determine the order of node exploration
  • To calculate the total cost of reaching the goal

    • Which of the following statements regarding Uniform Cost Search (UCS) is correct?

  • UCS guarantees the fastest solution in all scenarios
  • UCS uses a heuristic to guide the search towards the goal
  • UCS expands the node with the lowest path cost (correct)
  • UCS always explores the deepest node first

    • In the context of A* Search, what does the function $f(n)$ represent?

  • The maximum depth achieved in the search
  • The estimated total cost from start to goal through state $n$ (correct)
  • The heuristic value of state $n$
  • The cost to reach state $n$

    • What characterizes Greedy Best-First Search (GS)?

    <p>It prioritizes nodes that seem closest to the goal using a heuristic</p> Signup and view all the answers

    What is required for a search algorithm to be considered admissible?

    <p>It must not overestimate the cost to reach the goal from any state</p> Signup and view all the answers

    Which statement about iterative-deepening search is true?

    <p>It combines the benefits of depth-first and breadth-first searches</p> Signup and view all the answers

    What does the term 'consistency' refer to in the context of heuristic functions?

    <p>The difference between heuristic values of two states does not exceed the step cost between them</p> Signup and view all the answers

    Which search strategy evaluates nodes primarily based on their heuristic function value, disregarding path cost?

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

    What is the primary evaluation criterion for Uniform Cost Search (UCS)?

    <p>Path cost from the root node</p> Signup and view all the answers

    In Greedy Best-First Search, which function serves as the basis for node evaluation?

    <p>h(n)</p> Signup and view all the answers

    Which feature is crucial for A* Search to guarantee optimal solutions?

    <p>Admissibility of the heuristic function</p> Signup and view all the answers

    What is the purpose of the heuristic function 'h(s)' in informed search strategies?

    <p>To estimate the cost to reach the goal state</p> Signup and view all the answers

    What does the term 'consistency' represent in the context of heuristic functions?

    <p>The estimated cost does not exceed the estimated cost from a neighboring state plus the actual cost to reach that neighbor</p> Signup and view all the answers

    What distinguishes Iterative-Deepening Search from Depth-First Search?

    <p>It limits the depth of nodes being explored iteratively</p> Signup and view all the answers

    When considering pathfinding, which of the following represents the combined evaluation function used in A* Search?

    <p>f(n) = g(n) + h(n)</p> Signup and view all the answers

    Which search strategy prioritizes nodes based on the lowest estimated cost while ignoring depth considerations?

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

    Study Notes

    Heuristics

    • Heuristic functions estimate the cost of reaching the goal state from a given node.
    • They guide search algorithms toward promising paths.
    • UCS guarantees finding the optimal solution by always expanding the node with the lowest path cost.
    • The function (f(n)) in A* Search represents the estimated total cost of the path from the start node to the goal node, passing through node (n). This is calculated as (f(n) = g(n) + h(n)), where (g(n)) is the actual cost from the start node to (n), and (h(n)) is the estimated cost from (n) to the goal.
    • Greedy Best-First Search (GS) prioritizes expanding the node with the lowest estimated cost to the goal, without considering the cost of reaching that node.

    Admissible Search Algorithms

    • An admissible search algorithm guarantees to find an optimal solution if one exists.
    • This is achieved by ensuring that the heuristic function never overestimates the actual cost to reach the goal.
    • Iterative-deepening search combines the advantages of depth-first and breadth-first search.
    • It systematically increases the depth limit of the search tree until the goal is found.

    Consistent Heuristic Functions

    • A consistent heuristic function satisfies the triangle inequality.
    • This means the estimated cost from a node (n) to the goal (h(n)) is less than or equal to the estimated cost from a successor node (n') plus the cost of reaching (n') from (n): (h(n) \leq h(n') + c(n, n')).
    • Greedy search strategies prioritize expanding nodes with the lowest heuristic function value, giving higher importance to the estimated cost to the goal than the actual cost of reaching that node.

    Uninformed Search Strategies

    • Path Cost Function: Represents the cost of reaching a specific node from the starting node, denoted by g(n).
    • Modified Graph Search Algorithm: Adapted to incorporate the path cost function g(n) in the evaluation of nodes.
    • Uniform Cost Search (UCS): A search strategy that expands nodes in increasing order of their path cost, guaranteeing the shortest path if all edge costs are positive.

    Informed Search Strategies

    • Heuristic Function: Estimates the cost of reaching the goal from a given state, denoted by h(s).
    • Greedy Best-First Search (GS): Uses the heuristic function h(s) to guide the search, expanding the node with the lowest estimated cost to the goal, but not guaranteeing optimality.
    • A Search:* Combines the path cost function g(n) and heuristic function h(s) into a single evaluation function f(n) = g(n) + h(n), expanding nodes in increasing order of their estimated total cost.
    • Admissibility: A heuristic is admissible if it never overestimates the actual cost to reach the goal.
    • Consistency: A heuristic is consistent if its estimate for a node is less than or equal to the cost of moving to a neighboring node plus the estimated cost of reaching the goal from the neighbor. Consistent heuristics ensure that A* finds an optimal solution.

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    Description

    Explore the fundamentals of uninformed search strategies, including Depth-First Search, Breadth-First Search, Iterative-Deepening Search, and Uniform-Cost Search. Understand how each strategy explores the search space and the significance of the path cost function in graph search algorithms.

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