Podcast
Questions and Answers
A neighbor state is the state to either side of the current ______.
A neighbor state is the state to either side of the current ______.
state
Hill climbing is a type of a local search ______.
Hill climbing is a type of a local search ______.
algorithm
A local maximum is a state that has a higher value than its neighboring ______.
A local maximum is a state that has a higher value than its neighboring ______.
states
The problem with hill climbing algorithms is that they may end up in local ______.
The problem with hill climbing algorithms is that they may end up in local ______.
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Steepest-ascent hill climbing chooses the highest-valued ______.
Steepest-ascent hill climbing chooses the highest-valued ______.
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Simulated Annealing allows the algorithm to 'dislodge' itself if it gets stuck in a local ______.
Simulated Annealing allows the algorithm to 'dislodge' itself if it gets stuck in a local ______.
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Random-restart hill climbing conducts hill climbing multiple ______.
Random-restart hill climbing conducts hill climbing multiple ______.
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Local Beam Search chooses the k highest-valued ______.
Local Beam Search chooses the k highest-valued ______.
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To get the probability of an event, we divide the number of worlds in which it occurs by the number of total possible ______.
To get the probability of an event, we divide the number of worlds in which it occurs by the number of total possible ______.
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P(12) = 1/36, meaning the probability of rolling two dice and getting two numbers whose sum is ______ is 1/36.
P(12) = 1/36, meaning the probability of rolling two dice and getting two numbers whose sum is ______ is 1/36.
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The sum 7 occurs in ______ worlds when rolling two dice.
The sum 7 occurs in ______ worlds when rolling two dice.
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Unconditional Probability is the degree of belief in a proposition in the absence of any other ______.
Unconditional Probability is the degree of belief in a proposition in the absence of any other ______.
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Node consistency is when all the values in a variable’s domain satisfy the variable’s ______ constraints.
Node consistency is when all the values in a variable’s domain satisfy the variable’s ______ constraints.
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Independence means that the occurrence of one event does not affect the probability of the ______ event.
Independence means that the occurrence of one event does not affect the probability of the ______ event.
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Negation in probability is represented as P(¬a) = 1 - P(______).
Negation in probability is represented as P(¬a) = 1 - P(______).
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The algorithm called ______ is used to make the whole problem arc-consistent.
The algorithm called ______ is used to make the whole problem arc-consistent.
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In a constraint satisfaction problem, the initial state is an ______ assignment.
In a constraint satisfaction problem, the initial state is an ______ assignment.
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Inclusion-Exclusion is summarized by the formula P(a ∨ b) = P(a) + P(b) - P(a ∧ ______).
Inclusion-Exclusion is summarized by the formula P(a ∨ b) = P(a) + P(b) - P(a ∧ ______).
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Conditioning is expressed as P(a) = P(a | b)P(b) + P(a | ¬______)P(¬b).
Conditioning is expressed as P(a) = P(a | b)P(b) + P(a | ¬______)P(¬b).
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The ______ model shows how adding the assignment changes the assignment.
The ______ model shows how adding the assignment changes the assignment.
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A ______ search is a type of search algorithm that takes into account the structure of a constraint satisfaction search problem.
A ______ search is a type of search algorithm that takes into account the structure of a constraint satisfaction search problem.
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In constraint satisfaction problems, the goal test checks if all variables are assigned a ______ and all constraints are satisfied.
In constraint satisfaction problems, the goal test checks if all variables are assigned a ______ and all constraints are satisfied.
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The path cost function indicates that all paths have the same ______.
The path cost function indicates that all paths have the same ______.
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This mechanism allows the algorithm to change its state to a neighbor that’s worse than the current state, which is how it can escape from local ______.
This mechanism allows the algorithm to change its state to a neighbor that’s worse than the current state, which is how it can escape from local ______.
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In the traveling salesman problem, the task is to connect all points while choosing the shortest possible ______.
In the traveling salesman problem, the task is to connect all points while choosing the shortest possible ______.
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Linear programming is a family of problems that optimize a ______ equation.
Linear programming is a family of problems that optimize a ______ equation.
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Each x₋ in linear programming is a variable associated with some ______.
Each x₋ in linear programming is a variable associated with some ______.
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A constraint expressed as a sum of variables that is either less than or equal to a value is represented as ______.
A constraint expressed as a sum of variables that is either less than or equal to a value is represented as ______.
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A Hard Constraint is a constraint that must be ______ in a correct solution.
A Hard Constraint is a constraint that must be ______ in a correct solution.
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A Unary Constraint involves only ______ variable.
A Unary Constraint involves only ______ variable.
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A Binary Constraint is a constraint that involves ______ variables.
A Binary Constraint is a constraint that involves ______ variables.
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The function Max-Value is part of the ______ algorithm.
The function Max-Value is part of the ______ algorithm.
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In Max-Value, if the state is ______, the function returns the utility of that state.
In Max-Value, if the state is ______, the function returns the utility of that state.
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Alpha-Beta pruning helps optimize the ______ algorithm by skipping unfavorable computations.
Alpha-Beta pruning helps optimize the ______ algorithm by skipping unfavorable computations.
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The ______ model describes what state results from performing an applicable action in any state.
The ______ model describes what state results from performing an applicable action in any state.
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A knowledge base is a set of ______ known by a knowledge-based agent.
A knowledge base is a set of ______ known by a knowledge-based agent.
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In propositional logic, both statements can be true using the ______ connective.
In propositional logic, both statements can be true using the ______ connective.
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Implication in logic is represented as ______, meaning if A is true, then B is also true.
Implication in logic is represented as ______, meaning if A is true, then B is also true.
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Entailment occurs when, in every model where sentence α is true, sentence ______ is also true.
Entailment occurs when, in every model where sentence α is true, sentence ______ is also true.
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The Markov assumption states that the current state depends on only a finite fixed number of previous ______.
The Markov assumption states that the current state depends on only a finite fixed number of previous ______.
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A Markov chain is a sequence of random variables where the distribution of each variable follows the ______ assumption.
A Markov chain is a sequence of random variables where the distribution of each variable follows the ______ assumption.
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A hidden Markov model is a type of a Markov model for a system with ______ states that generate some observed event.
A hidden Markov model is a type of a Markov model for a system with ______ states that generate some observed event.
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Filtering calculates the probability distribution for the current state based on observations from ______ until now.
Filtering calculates the probability distribution for the current state based on observations from ______ until now.
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An Objective Function is a function that we use to ______ the value of the solution.
An Objective Function is a function that we use to ______ the value of the solution.
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A Cost Function is a function that we use to ______ the cost of the solution.
A Cost Function is a function that we use to ______ the cost of the solution.
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Local search is a search algorithm that maintains a single ______ and searches by moving to a neighboring node.
Local search is a search algorithm that maintains a single ______ and searches by moving to a neighboring node.
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A Current State is the state that is currently being ______ by the function.
A Current State is the state that is currently being ______ by the function.
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Study Notes
Introduction to Artificial Intelligence - Search
- Agent: An entity that perceives its environment and acts within it.
- State: A configuration of an agent within its environment.
- Initial State: The starting state for the search algorithm.
- Actions: Choices that can be made in a state.
- Goal Test: The condition determining if a given state is a goal state.
- Path Cost: A numerical cost associated with a given path.
- Solution: A sequence of actions from the initial state to the goal state.
- Optimal Solution: A solution with the lowest path cost among all possible solutions.
Uninformed Search Algorithms
-
Depth-First Search (DFS):
- Explores one branch as deeply as possible before backtracking.
- Uses a stack data structure ("Last-In, First-Out").
- Pros: Can be fast if the solution is shallow.
- Cons: Can get stuck in infinitely deep paths; not guaranteed to find the optimal path.
-
Breadth-First Search (BFS):
- Explores all neighbors at the current depth before moving to deeper levels.
- Uses a queue data structure ("First-In, First-Out").
- Pros: Guaranteed to find the optimal solution if all paths have equal costs.
- Cons: Can be slow for problems with shallow solutions, as it explores many unnecessary paths.
Heuristic Search Algorithms
-
Greedy Best-First Search:
- Selects the node closest to the goal, based on a heuristic function (h(n)).
- Pros: Can be computationally efficient if the heuristic is good.
- Cons: Not guaranteed to find the optimal solution; can get trapped in local optima.
-
A Search:*
- A combination of uniform cost search and greedy search that estimates the total cost to the goal.
- Uses a heuristic function (h(n)) and a cost function (g(n)) to estimate the cost.
- Pros: Guaranteed to find optimal solution if the heuristic is admissible (never overestimates) and consistent (monotonic).
- Cons: Computationally more expensive than simpler uninformed methods.
Adversarial Search
- Minimax: A search algorithm used in games with two players of opposing interests (e.g., chess, tic-tac-toe).
Knowledge Representation
- Transition model: Describes the result of applying an action in a given state.
- State space: The set of all reachable states from the initial state.
- Knowledge-based agents: Agents that reason using internal knowledge representations.
Propositional Logic (Sentences / Statements)
- Propositional Logic: Describes facts that can be either true or false.
-
Connectives (logic operators):
and,or,implies(implication), andbiconditional. - Inference: The process of deriving new statements from existing ones.
- Modus ponens: A rule of inference in logic.
Inference Rules & Algorithms
- Resolution: A rule of inference used to draw conclusions from a set of premises, especially in propositional and predicate logic.
- Clause: A disjunction (OR) of literals.
- Conjunctive Normal Form (CNF): A way to express logical formulas in a standard form suitable for resolution.
Search Problems
- Initial state: The starting configuration of a problem.
- Actions: Possible steps that can be taken.
- Transition model: A description of how actions change the state of the problem.
- Goal test: A test to determine whether a given state is a goal state.
- Path cost function: The cost of reaching a goal state through a path.
Optimization
- Local search: A search algorithm that operates on a single node, explores neighbors, and attempts to improve the solution.
- Hill climbing: A local search algorithm that repeatedly moves to a better neighbor until a local maximum or minimum is reached.
- Simulated annealing: A local search algorithm that allows for occasional moves to worse neighbors to escape local optima.
Constraint Satisfaction Problems
- Variables: Entities that need to be assigned values.
- Domains: Possible values for each variable.
- Constraints: Rules that limit the combinations of variable-value assignments.
- Backtracking search: A search algorithm that attempts to find a solution by assigning values to variables while checking constraints, and if a constraint failed revisits previous assignments.
Other Concepts
- Bayesian Networks: Models representing probabilistic relationships between variables using directed acyclic graphs.
- Random Variables: Variables that have numerical values with a probability distribution..
- Uncertainty and Probability
Additional Notes
- Traveling Salesperson Problem: Finding the shortest route that visits all cities exactly once and returns to the starting city.
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Description
This quiz covers key concepts in artificial intelligence search methods, including agents, states, and various uninformed search algorithms like Depth-First Search (DFS) and Breadth-First Search (BFS). Test your understanding of terms such as goal tests, path costs, and optimal solutions. Challenge yourself to grasp the fundamentals of how search algorithms operate within AI applications.
