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
What distinguishes deductive reasoning from inductive reasoning in AI?
Answer: Deductive reasoning draws specific conclusions that are guaranteed from general premises; inductive reasoning generalizes from specific observations
Deductive reasoning moves from general rules to specific conclusions that are logically certain, while inductive reasoning infers general rules from specific examples, producing conclusions that are probable but not guaranteed.
In forward chaining, an AI system:
Answer: Starts from available facts and applies rules to derive new conclusions until the goal is reached
Forward chaining (data-driven reasoning) begins with known facts and fires applicable rules to generate new facts, continuing until the target goal is derived or no more rules can fire.
Backward chaining is best described as:
Answer: Starting from a goal hypothesis and working backward to find facts that support it
Backward chaining (goal-driven reasoning) begins with the goal and recursively identifies sub-goals or facts needed to prove it, making it efficient when the goal is known in advance.
Fuzzy logic is used in AI primarily to:
Answer: Represent and reason about degrees of truth in situations involving uncertainty or vagueness
Fuzzy logic extends classical binary logic by allowing truth values between 0 and 1, making it suitable for reasoning about imprecise or vague concepts like 'tall', 'warm', or 'fast'.
Which of the following best describes a production rule system?
Answer: A rule-based system using IF-THEN rules to represent knowledge and trigger actions
A production rule system represents knowledge as a collection of IF (condition) THEN (action) rules; the inference engine matches conditions against working memory and fires matching rules.
What is the Closed World Assumption (CWA) in knowledge representation?
Answer: Any fact not known to be true is assumed to be false
Under the CWA, if a fact cannot be proven true from the knowledge base, it is assumed to be false — this is typical in databases and logic programming (e.g., Prolog).
The Open World Assumption (OWA), used in ontologies like OWL, means that:
Answer: A fact not currently known may still be true — lack of information does not imply falsehood
Under the OWA, the absence of a fact from the knowledge base does not mean the fact is false — it may simply be unknown, which is appropriate for open-ended, evolving domains like the Semantic Web.