Podcast
Questions and Answers
In the context of search algorithms, what is the primary difference between a 'Uniformed' and an 'Informed' search strategy?
In the context of search algorithms, what is the primary difference between a 'Uniformed' and an 'Informed' search strategy?
- Uniformed searches are always faster than informed searches.
- Informed searches explore all possible paths equally, while uniformed searches prioritize certain paths based on cost.
- Informed searches use domain-specific knowledge to estimate the cost to a goal, while uniformed searches do not. (correct)
- Uniformed searches use heuristics to guide the search, while informed searches do not.
Which of the following search algorithms is considered a 'cost-insensitive' search method?
Which of the following search algorithms is considered a 'cost-insensitive' search method?
- A* Search
- Dijkstra's Algorithm
- Breadth-First Search (correct)
- Uniform Cost Search
What is the main objective of using a heuristic function in search algorithms?
What is the main objective of using a heuristic function in search algorithms?
- To estimate the cost from a given state to the goal state, guiding the search process. (correct)
- To guarantee finding the optimal solution in every search problem.
- To eliminate the need for exploring any paths that do not lead directly to the goal.
- To reduce the memory requirements of the search algorithm by pruning the search tree randomly.
In the context of search algorithms, what does 'admissibility' refer to?
In the context of search algorithms, what does 'admissibility' refer to?
How does the A* algorithm use the heuristic function, h(n), and the cost function, g(n), to evaluate nodes?
How does the A* algorithm use the heuristic function, h(n), and the cost function, g(n), to evaluate nodes?
What could be a potential consequence of using a heuristic function that significantly overestimates the cost to the goal in the A* algorithm?
What could be a potential consequence of using a heuristic function that significantly overestimates the cost to the goal in the A* algorithm?
In Greedy Best-First Search, what factor determines the selection of the next node to expand?
In Greedy Best-First Search, what factor determines the selection of the next node to expand?
What is the key difference between the A* algorithm and Dijkstra's algorithm in pathfinding?
What is the key difference between the A* algorithm and Dijkstra's algorithm in pathfinding?
In the context of search algorithms, what does the term 'local search' generally refer to?
In the context of search algorithms, what does the term 'local search' generally refer to?
What characterizes an admissible heuristic?
What characterizes an admissible heuristic?
What is the main principle behind 'Weighted A*' search?
What is the main principle behind 'Weighted A*' search?
What does the term 'branching factor' refer to in the context of search algorithms?
What does the term 'branching factor' refer to in the context of search algorithms?
How does the 'Manhattan distance' heuristic calculate distance?
How does the 'Manhattan distance' heuristic calculate distance?
What condition defines 'consistency' for a heuristic function h(n)?
What condition defines 'consistency' for a heuristic function h(n)?
In the context of weighted A* search, what is the role of the weight 'W' in the evaluation function (f(n) = g(n) + W*h(n))?
In the context of weighted A* search, what is the role of the weight 'W' in the evaluation function (f(n) = g(n) + W*h(n))?
How does Uniform Cost Search (UCS) differ from Breadth-First Search (BFS)?
How does Uniform Cost Search (UCS) differ from Breadth-First Search (BFS)?
What is a primary limitation of Greedy Best-First Search?
What is a primary limitation of Greedy Best-First Search?
In the context of search algorithms, what is an 'unvisited database'?
In the context of search algorithms, what is an 'unvisited database'?
What is the potential benefit of using an admissible heuristic in an A* search?
What is the potential benefit of using an admissible heuristic in an A* search?
Consider a scenario where you want to find the shortest path through a maze. Which heuristic would be considered admissible?
Consider a scenario where you want to find the shortest path through a maze. Which heuristic would be considered admissible?
What is the primary difference between a 'bounded-cost search' and an 'unbounded-cost search'?
What is the primary difference between a 'bounded-cost search' and an 'unbounded-cost search'?
Suppose you're using A* search to navigate a robot. If the heuristic consistently underestimates the cost to the goal, how might this affect the search?
Suppose you're using A* search to navigate a robot. If the heuristic consistently underestimates the cost to the goal, how might this affect the search?
How does increasing the weight (W) on the heuristic in a weighted A* algorithm affect the search?
How does increasing the weight (W) on the heuristic in a weighted A* algorithm affect the search?
In a search problem, what does it mean for a node to be 'expanded'?
In a search problem, what does it mean for a node to be 'expanded'?
What is the primary role of the 'visited database' (or closed list) in search algorithms like A*?
What is the primary role of the 'visited database' (or closed list) in search algorithms like A*?
Which of the following search algorithms is generally considered complete and optimal in simple environments?
Which of the following search algorithms is generally considered complete and optimal in simple environments?
If you have a limited amount of memory and need to find a solution quickly (but not necessarily the optimal one), which search strategy might be the most appropriate?
If you have a limited amount of memory and need to find a solution quickly (but not necessarily the optimal one), which search strategy might be the most appropriate?
In a scenario where the cost of each step is uniform across the search space, which of the following search algorithms would be most efficient in finding the shortest path?
In a scenario where the cost of each step is uniform across the search space, which of the following search algorithms would be most efficient in finding the shortest path?
What does it mean for a search algorithm to be 'complete'?
What does it mean for a search algorithm to be 'complete'?
In a planning problem, which of the following search strategies would be best suited for minimizing the amount of CPU resources?
In a planning problem, which of the following search strategies would be best suited for minimizing the amount of CPU resources?
What characteristics generally describe a complex environment?
What characteristics generally describe a complex environment?
If an environment is 'partially observed', what challenge does this present to a planning agent?
If an environment is 'partially observed', what challenge does this present to a planning agent?
What is an implication of a 'non-deterministic' environment for problem-solving agents?
What is an implication of a 'non-deterministic' environment for problem-solving agents?
Which search algorithm is best suited for path finding in game development?
Which search algorithm is best suited for path finding in game development?
A* algorithm defines the effect of the heuristic value versus path cost as a
A* algorithm defines the effect of the heuristic value versus path cost as a
Flashcards
Informed Search
Informed Search
A domain-specific search that uses hints about the location of goals to find solutions more efficiently than an uninformed strategy.
Heuristic Function
Heuristic Function
A function h(n) that estimates the cost of the cheapest path from the state at node n to a goal state.
Greedy Best First Search
Greedy Best First Search
A search algorithm that expands the most promising node based on an evaluation function f(n) that includes a heuristic component.
Admissible Heuristic
Admissible Heuristic
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C(n)
C(n)
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Bounded-cost Search
Bounded-cost Search
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Unbounded-cost Search
Unbounded-cost Search
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Manhattan Distance
Manhattan Distance
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Weighted A Search
Weighted A Search
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Path Curvature
Path Curvature
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Study Notes
- Dr. Mustafa Shiple is the author
- Focus is on Informed search algorithms, and planning agents in simple environments
Review of Searching Problems
- Searching problems can occur in simple or complex environments
- Environments can be deterministic or non-deterministic
- Environments can be partially observed
Informed Search Strategies
- The objective is to search problems
- Using Greedy best first search
- Or using the A* search algorithm
What is Planning?
- Planning is the process of determining a sequence of actions and motions, by looking ahead.
Informed Search Strategy Facts
- An informed search strategy uses domain-specific hints about the location of goals
- The heuristics find solutions more efficiently than an uninformed strategy
Heuristic Function Defined
- The heuristic function h(n) estimates the cost of the cheapest path from the state at node n to a goal state.
Searching Algorithms: Greedy Best First Search (GBFs)
- The text describes the Greedy Best-First Search algorithm, starting with initialization
- Initialization starts from an initial state
- Evaluates the path cost g(n)
- Prefers previous state
Greedy Best-First Search (GBFs) Step 1
- Starts from the least H(n), which is node B
- Finds path cost
- Continues to prefer previous state
Greedy Best-First Search (GBFs) Step 2
- Algorithm goes to higher f(n) which is B node
- Continues to prefer previous state
Results Of The Greedy Best First Search
- F(n) = H(n) = 15
- Path cost obtained is 11 (A, B, C)
- Optimum path cost that could have been obtained is 7 (A, D, E, C)
- Crucially a greedy best-first search finds a solution without expanding a node that is not on the solution path
More About Best First Search
- b = # successors (in our case b= 2) maximum branching factor
- d = depth of the optimal solution
- m = maximum depth of the state space
- Complexity = less than O(bm)
Greedy Best-First Search (GBFs) Step 3
- Now moves until its finds the least f(n) which is C
A* Search Initialization:
- Path cost from the initial state to node n is given as C(n)
- Initialization: (start from initial State)
A* Step 1
- Algorithm starts from the least C(x) which is A again
A* Step 2
- Algorithm starts from the least f(n) which is B
A* Step 3
- From here algorithm starts from the least f(n) which is C
Key to A* Working Correctly
- A key property is admissibility
- A heuristic is admissible if it never overestimates the cost to reach a goal.
- H(x, g) ≤ c(x, g)
Underestimating Heuristic
- An underestimating heuristic is critical here
- So in step 1: (start from least f(n) which is A again )
A* Step 2 With Admissible Heuristic
- Now uses least f(n) which is D
A* Step 3 With Admissible Heuristic
- Starts from least f(n) which is E
- C is a goal, so now stop search
Search Tree Algorithms Overview
- Tree splits into uniformed and informed search strategies
- Uniformed branches into both Cost insensitive and Cost sensitive
- Informed is for GBF
- BSF is in Cost insensitive
- Dijkstra and UCS are in Cost sensitive
Nils Nilsson
- Nils utilized an "Admissible heuristic"
- Nils also utilized "Heuristic function h(n)."
- Admissible Heuristic Example:*
Int heuristic(initial_state, goal_state){
## Manhattan distance on a square grid
return abs (inital.x - goal.x) + abs (inital.y - goal.y)}
- Nils published this in 1968
Algorithms: weighted A*
- F(n)=c(n)+W*H(n) represents the curvature of the path
- H(n) needs to "Consider more than c(n)"
- H(n) needs to "Exclude the curved paths quickly."
Weighted A* Search Algorithms
- A search: c(n)+h(n) (W = 1)
- Uniform-cost search: c(n) (W = 0)
- Greedy best-first search: h(n) (W =∞)
- Weighted A search: g(n)+Wh(n) (1 <W < ∞) call weighted A “somewhat-greedy search"
More About Weighted A*
- Bounded-cost Search looks for a solution whose cost is less than some constant C.
- Unbounded-cost Search accepts a solution of any cost, as long as it can be found quickly.
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