Search Flashcards
An entity that perceives its environment and acts upon that environment.
Agent
A configuration of an agent in its environment.
State
The state from which the search algorithm starts.
Initial State
Choices that can be made in a state.
Actions
A description of what state results from performing any applicable action in any state.
Transition Model
The set of all states reachable from the initial state by any sequence of actions.
State Space
The condition that determines whether a given state is a goal state.
Goal Test
A numerical cost associated with a given path.
Path Cost
A sequence of actions that leads from the initial state to the goal state.
Solution
A solution that has the lowest path cost among all solutions.
Optimal Solution
contains the following data:
* A state
* Its parent node, through which the current node was generated
* The action that was applied to the state of the parent to get to the current node
* The path cost from the initial state to this node
node
the mechanism that “manages” the nodes
frontier
search algorithm that exhausts each one direction before trying another direction
Depth-First Search
search algorithm where the frontier is managed as a stack data structure
Depth-First Search
Pros and Cons of DFS
Pros:
* At best, this algorithm is the fastest. If it “lucks out” and always chooses the right path to the solution (by chance), then depth-first search takes the least possible time to get to a solution.
Cons:
* It is possible that the found solution is not optimal.
* At worst, this algorithm will explore every possible path before finding the solution, thus taking the longest possible time before reaching the solution.
search algorithm that will follow multiple directions at the same time, taking one step in each possible direction before taking the second step in each direction.
Breadth-First Search
search algorithm where the frontier is managed as a queue data structure
Breadth-First Search
Pros and Cons of BFS
Pros:
* This algorithm is guaranteed to find the optimal solution.
Cons:
* This algorithm is almost guaranteed to take longer than the minimal time to run.
* At worst, this algorithm takes the longest possible time to run.
A type of algorithm that considers additional knowledge to try to improve its performance
Informed Search Algorithm
search algorithm that expands the node that is the closest to the goal, as determined by a heuristic function h(n).
Greedy Best-First Search
ignores walls and counts how many steps up, down, or to the sides it would take to get from one location to the goal location
Manhattan Distance
function that estimates how close to the goal the next node is, but it can be mistaken.
heuristic function
The efficiency of the greedy best-first algorithm depends on
how good a heuristic function is
considers not only h(n), the estimated cost from the current location to the goal, but also g(n), the cost that was accrued until the current location.
A* Search
For a* search to be optimal, the heuristic function has to be:
Admissible & Consistent
In the heuristic function h(n) of an A* search algorithm, it is consistent if
for every node n and successor node n’ with step cost c, n ≤ n’ + c
In the heuristic function h(n) of an A* search algorithm, what does it mean to be admissible?
Never overestimates the actual cost to reach a goal from any node
algorithm that faces an opponent that tries to achieve the opposite goal.
Adversarial Search
represents winning conditions as (-1) for one side and (+1) for the other side.
Minimax
Recursively, the algorithm simulates all possible games that can take place beginning at the current state and until a terminal state is reached. Each terminal state is valued as either (-1), 0, or (+1).
Minimax
an optimization technique for minimax that skips some of the recursive computations that are decidedly unfavorable
Alpha-Beta Pruning
Minimax that considers only a pre-defined number of moves before it stops, without ever getting to a terminal state.
Depth-Limited Minimax
estimates the expected utility of the game from a given state, or, in other words, assigns values to states.
Evaluation function
