Propositional and first-order logic, inference, Bayesian networks, Markov models and decision theory.
About this set
40 basic cards for College Artificial Intelligence. It opens with "In propositional logic, what does the conjunction operator (AND) return true if" and "What is the truth value of 'P OR NOT P' in classical propositional logic?". Study it alongside the other College sets below.
40 cards · basic cards · AI-written, checked twice. Edit anything.
- In propositional logic, what does the conjunction operator (AND) return true if and only if?
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Both propositions are true.- What is the truth value of 'P OR NOT P' in classical propositional logic?
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Always true (it is a tautology).- What logical inference rule allows you to conclude Q from 'P implies Q' and P?
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Modus Ponens.- In first-order logic, what is a predicate?
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A function that returns true or false for one or more arguments.- What does the universal quantifier (for all X) allow you to express?
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A statement that applies to every object in the domain.- What does the existential quantifier (exists X) allow you to express?
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A statement that applies to at least one object in the domain.- What is unification in first-order logic?
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Finding a substitution of terms for variables that makes two expressions identical.- What is Conjunctive Normal Form (CNF)?
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A logical formula written as a conjunction (AND) of disjunctions (OR clauses).- In inference, what does the Resolution principle allow you to derive?
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A new clause by resolving two clauses that contain complementary literals.- What is forward chaining in inference?
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Starting from known facts and repeatedly applying inference rules to derive new facts until a goal is reached.- What is backward chaining in inference?
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Starting from a goal and working backward to find supporting facts, stopping when known facts are reached.- What is a Horn clause?
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A clause with at most one positive literal, written as P1 AND P2 AND ... AND Pn implies Q.- What is a Bayesian network?
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A directed acyclic graph where nodes represent random variables and edges represent conditional dependencies.- What is a Conditional Probability Table (CPT) in a Bayesian network?
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A table specifying the probability distribution of a variable given each combination of its parent variables.- What does d-separation (or d-separation criterion) determine in a Bayesian network?
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Whether two variables are conditionally independent given a third variable by checking paths in the DAG.
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