18 Questions
What data structure is used to store the list in Breadth First Search?
Queue
Which search technique guarantees to find a solution and keeps every node in memory?
Breadth First Search
What property must hold true for every node in Uniform Cost Search to find the cheapest solution?
$g(successor(n)) \geq g(n)$
Which search algorithm ensures the shortest path to the goal in terms of cost?
Uniform Cost Search
Which search algorithm expands one of the nodes at the deepest level of the tree?
Depth-First Search
What is the evaluation function used in A* search algorithm?
f(n) = h(n) + g(n)
In A* search, what does the function g(n) represent?
Cost of path from initial state to node n
In which search technique are successors put at the end of a queue for expansion?
Breadth First Search
Which property of A* search ensures that it guarantees reaching the goal state?
Completeness
What is the worst-case time and space complexity of heuristic algorithms like A* search?
Exponential
Based on its properties, is A* search algorithm optimal?
Yes, if the heuristic function is admissible
Which memory-related issue arises when using A* search algorithm?
Keeps all generated nodes in memory
What data structure can be used to implement Depth-first search?
Stack
Which type of spaces does Depth-first search fail in?
Infinite-depth spaces, spaces with loops
What would improve search efficiency greatly according to the text?
Ordering choices based on heuristics
What does the heuristic function h(n) estimate in A* search?
The cost of the path from the start state to node n
How does A* search differ from Best First Search?
A* search uses a heuristic to estimate the cost of the path, Best First Search doesn't
What property makes Depth-first search incomplete and not optimal?
May fail in infinite-depth spaces
Study Notes
Search Strategies
- Informed search strategies use domain knowledge to order choices and explore the most promising paths first.
- Best First Search:
- Uses a heuristic function, h(n), to estimate the goodness of a node.
- Estimates the cost of the path from the current state to a goal state.
- Expands all nodes on a given level of the search tree before moving to the next level.
- Implemented using a queue data structure.
- Properties: takes space, optimal, and complete.
- Uniform Cost Search:
- Modifies Best First Search to find the shortest path to the goal in terms of cost.
- Expands the least-cost unexpanded node.
- Implemented using a priority queue ordered by path cost.
- Properties: finds the cheapest solution, takes space, and has a cost that never decreases along the path.
Depth-First Search
- Expands one of the nodes at the deepest level of the tree.
- Implemented using a stack data structure.
- Properties: incomplete, not optimal, fails in infinite depth spaces, and spaces with loops.
A* Search Algorithm
- Considers both the estimated cost of getting from the current node to the goal node (h(n)) and the cost of getting from the initial node to the current node (g(n)).
- Evaluation function: f(n) = h(n) + g(n).
- Implemented by expanding the node with the lowest evaluation function.
- Properties: complete, optimal, and optimally efficient for any given admissible heuristic function.
- Takes memory and has exponential time and space complexity in the worst case.
- The quality of the heuristic function affects the time and space complexity.
Learn about the A*-search algorithm in Artificial Intelligence, which considers the estimated cost of reaching the goal node and the cost of reaching the current node from the initial state. Discover how to apply three functions to every node in the search process.
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