ArtIntel(Midterm1stHandout)
AI Search Techniques Quiz
Test your knowledge on the fundamentals of artificial intelligence search techniques with this comprehensive quiz designed for students and enthusiasts alike. Covering a range of topics from uninformed and informed searches to game theory applications in AI, this quiz provides an engaging learning experience.
Key Features:
- 36 carefully crafted questions
- Covers a variety of AI search topics
- Multiple choice answers to test your understanding
The process of searching in artificial intelligence (AI) may involve observable, deterministic, static, known environments where the solution is a sequence of actions.Game playing exhibits different aspects of intelligence, particularly the ability to plan at an immediate tactical level and/or for long-term strategic level and ability to learn.
First statement is true.
Second statement is true.
Both statement is true.
Both statement is false.
The following types of problems utilize heuristic searches. Which is not?
Problems for which no exact algorithms are known, but approximation can be applied.
Problems for which exact solutions are known, but the computations cannot be practically produced.
Extensive amount of domain-specific knowledge is infrequently needed because of the limited rules of games.
In a nondeterministic environment, an agent will need conditional plans and carry out different actions depending on its observations. Only one algorithm may be utilized in order to perform searching.
First statement is true.
Second statement is true.
Both statement is true.
Both statement is false.
Uninformed search involves algorithms that do not have any domain-specific knowledge. Searching in AI is defined as a sequence of steps that creates a path between the initial state and the goal state.
First statement is true.
Second statement is true.
Both statement is true.
Both statement is false.
A plausible move generator: If the number of permissible moves is too high, then it is impossible to perform a full-width search to a depth adequate enough to have a good game. A static evaluation function generator: This is the most important component of a game playing program. It is used to evaluate every move that is being made.
First statement is true.
Second statement is true.
Both statement is true.
Both statement is false.
Possible game states/conditions can be represented using a directed graph, commonly called a game tree. The todes of a game tree represent the states of the game and the arcs represent the possible moves by the players.
First statement is true.
Second statement is true.
Both statement is true.
Both statement is false.
Heuristic search utilizes heuristics as an analyze function. Heuristics are approximates that are used like guides pointing in a general direction, but may miss certain paths
First statement is true.
Second statement is true.
Both statement is true.
Both statement is false.
This takes a problem as an input and returns a solution or an indication of failure.
Search algorithm
Search tree
Root
Search data structure
Nodes
Edges
This is commonly used to illustrate paths between states, towards a particular goal.
Search algorithm
Search tree
Root
Search data structure
Nodes
Edges
In a search tree, this corresponds to the initial state of the problem.
Search algorithm
Search tree
Root
Search data structure
Nodes
Edges
In a search tree, these correspond to the states within the state space of the problem.
Search algorithm
Search tree
Root
Search data structure
Nodes
Edges
In a search tree, these correspond to the available actions relative to the problem.
Search algorithm
Search tree
Root
Search data structure
Nodes
Edges
This is required to keep track of the search tree.
Search algorithm
Search tree
Root
Search data structure
Nodes
Edges
Each node in the tree may be represented by a data structure with four (4) usual components except one.
Node.PATH-COST
Node.STATE
Node.PARENT
Node.ACTION
Node.CHILD
This search proceeds in a systematic way of exploring nodes in a predetermined order
Uninformed search
Informed search
Uniform-cost Search
Bidirectional Search
Also known as heuristic search, involves domain-specific knowledge and allows access to additional information, such as pattern databases with solution costs, that improves the search process.
Uninformed search
Informed search
Uniform-cost Search
Bidirectional Search
This search process checks all the nodes per level, starting at the left working towards the right side of the tree, before searching the tree one level deeper.
Breadth-first Search
Depth-first Search
Iterative Deepening Search
Bidirectional Search
Uniform-cost Search
It is one of the main search processes used in AI. The process starts at the root of the tree and down to the left branch until it gets to the bottom node.
Breadth-first Search
Depth-first Search
Iterative Deepening Search
Bidirectional Search
Uniform-cost Search
This search process combines many advantages of the depth-first and breadth-first search.
Breadth-first Search
Depth-first Search
Iterative Deepening Search
Bidirectional Search
Uniform-cost Search
It encompasses a distinctive process that simultaneously searches forward from the initial state and backwards from the goal state(s), with the anticipation that the two (2) searches will meet.
Breadth-first Search
Depth-first Search
Iterative Deepening Search
Bidirectional Search
Uniform-cost Search
This search process can be considered as a variant of the Dijkstra’s algorithm. The complexity of this search can be characterized through the cost of the optimal solution.
Breadth-first Search
Depth-first Search
Iterative Deepening Search
Bidirectional Search
Uniform-cost Search
It is a general strategy used in searching. On each iteration of this search algorithm, the node with the minimum evaluation function value is selected.
Best-first Search
Hill Climbing Search
A* Search
Beam Search
Constraint Satisfaction
It is a search technique for finding the maximum or the minimum of an evaluation function.
Best-first Search
Hill Climbing Search
A* Search
Beam Search
Constraint Satisfaction
This is the most common informed search technique that uses the evaluation function: f(n) = g(n) + h(n).
Best-first Search
Hill Climbing Search
A* Search
Beam Search
Constraint Satisfaction
This is a search technique that utilizes heuristics to trim the state space and turn it into a small number of nearly optimal alternatives.
Best-first Search
Hill Climbing Search
A* Search
Beam Search
Constraint Satisfaction
It is a search strategy that deals with constraints that are the same as the ones that can be encountered in the real world.
Best-first Search
Hill Climbing Search
A* Search
Beam Search
Constraint Satisfaction
An environment that involves a set of circumstances that can produce a particular result dependent on the actions of two (2) or more players.
Game
Players
Strategy
Payoff
Information Set
Notion of Equilibrium
These are the strategic decision-makers within the game.
Game
Players
Strategy
Payoff
Information Set
Notion of Equilibrium
It is a complete plan of action of a player that considers the set of circumstances that may occur within the game.
Game
Players
Strategy
Payoff
Information Set
Notion of Equilibrium
It is something quantifiable that a player can receive in arriving at a particular outcome.
Game
Players
Strategy
Payoff
Information Set
Notion of Equilibrium
This encompasses the available information at a particular point in a game, which is commonly utilized in games that has sequential components
Game
Players
Strategy
Payoff
Information Set
Notion of Equilibrium
It is a point in a game where both players have made their decisions or moves and a particular outcome is reached.
Game
Players
Strategy
Payoff
Information Set
Notion of Equilibrium
There are at least three (3) viewpoints that can be derived in an environment with multiple agents, such as games. Below are the viewpoints except one.
If the total number of agents is very large, then it is possible to consider the agents as an aggregate – an economy.
Considering the argumentative – adversarial agents as part of the environment, even if it makes the environment nondeterministic.False
Explicitly model adversarial agents with a specific search strategy or technique
Extensive amount of domain-specific knowledge is infrequently needed because of the limited rules of games.
.It represents the lower bound value that a maximizing node may be assigned.
Alpha
Beta
Maximizer
Minimizer
T represents the upper bound value that a minimizing node may be assigned.
Alpha
Beta
Maximizer
Minimizer
The main objective of this strategy is for a player to minimize loss and maximize benefit.
Alpha
Beta
Maximizer
Minimax strategy
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