Knowledge Representation and Reasoning Flashcards
7 cards from real Artificial Intelligence practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Knowledge Representation and Reasoning flashcards as text
A Bayesian network is best used in AI for:
Answer: Representing probabilistic dependencies among variables and performing uncertainty reasoning
A Bayesian network is a directed acyclic graph where nodes represent random variables and edges encode conditional probability dependencies, enabling probabilistic inference under uncertainty.
Case-based reasoning (CBR) in AI solves new problems by:
Answer: Retrieving and adapting solutions from similar past cases stored in memory
CBR follows a retrieve-reuse-revise-retain cycle: it finds past cases similar to the current problem, adapts their solutions, evaluates the result, and stores the new case for future use.
Which query language is used to retrieve and manipulate data stored in RDF (Resource Description Framework) knowledge graphs?
Answer: SPARQL
SPARQL (SPARQL Protocol and RDF Query Language) is the W3C standard query language designed specifically for querying and updating RDF-based knowledge graphs and linked data.
Description logics (DLs) are used in AI primarily to:
Answer: Formally represent and reason about concept hierarchies and object properties in ontologies
Description logics are a family of formal knowledge representation languages used to define ontologies; they provide decidable reasoning services like classification, consistency checking, and instance retrieval.
In knowledge representation, what is the 'is-a' (or subsumption) relationship used to express?
Answer: That one class is a subset of or specialization of another class
The 'is-a' relationship expresses class hierarchy: if Dog is-a Animal, then every Dog is an Animal, enabling inheritance of properties from parent to child classes.
Which of the following reasoning types starts from observations and infers the most likely explanation for those observations?
Answer: Abductive reasoning
Abductive reasoning (inference to the best explanation) selects the simplest, most plausible hypothesis that explains the observed data, even if that hypothesis is not guaranteed to be correct.
Non-monotonic reasoning in AI is important because it allows the system to:
Answer: Revise or retract earlier conclusions when new contradictory information arrives
Non-monotonic reasoning supports belief revision: conclusions drawn from incomplete information can be retracted when new facts contradict them, reflecting how real-world knowledge evolves.