Question#1
What does the state space represent in AI problem-solving?
a) All possible actions
b) All possible states ✅
c) The goal state
d) The initial state

Question#2
In the water jug problem, what constitutes the action space?
a) The water levels in the jugs
b) Filling, emptying, or pouring water ✅
c) The size of the jugs
d) The target amount of water

Question#3
What is a key characteristic of the brute-force approach?
a) Efficiency
b) Systematic exploration of actions ✅
c) Guaranteed optimal solution
d) Complex calculations

Question#4
In the 3 and 5-liter jug example, what is the goal?
a) To fill both jugs completely
b) To measure 4 liters of water ✅
c) To empty both jugs
d) To have equal amounts in each jug

Question#5
What is a production system in AI?
a) A set of random actions
b) A set of rules to carry out a task ✅
c) A database of facts
d) A type of machine learning algorithm

Question#6
What are the two main parts of a production rule?
a) Input and output
b) Condition and action ✅
c) Cause and effect
d) Premise and conclusion

Question#7
Which component of a production system makes decisions?
a) Knowledge base
b) Working memory
c) Inference engine ✅
d) Control strategy

Question#8
What does knowledge representation aim to enable machines to do?
a) Generate random numbers
b) Reason, understand, and make informed decisions ✅
c) Process images quickly
d) Translate languages fluently

Question#9
What are ontologies?
a) Random data sets
b) Formal representations of knowledge ✅
c) Types of computer hardware
d) Statistical models

Question#10
What do semantic networks use to represent concepts?
a) Algorithms
b) Nodes ✅
c) Matrices
d) Functions

Question#11
Rule-based systems use what to represent knowledge?
a) Decision trees
b) Production rules ✅
c) Neural networks
d) Graphs

Question#12
What is the primary goal of reasoning in AI?
a) To mimic human thought
b) To derive logical conclusions ✅
c) To store large amounts of data
d) To generate random outputs

Question#13
Deductive reasoning is also known as?
a) Bottom-up reasoning
b) Top-down reasoning ✅
c) Cause-effect reasoning
d) Common sense reasoning

Question#14
What type of reasoning uses generalization to reach a conclusion?
a) Deductive reasoning
b) Abductive reasoning
c) Inductive reasoning ✅
d) Monotonic reasoning

Question#15
Which reasoning starts with observations and seeks the most plausible explanation?
a) Deductive reasoning
b) Inductive reasoning
c) Abductive reasoning ✅
d) Monotonic reasoning

Question#16
What is a characteristic of monotonic reasoning?
a) Conclusions can be refuted
b) Conclusions remain valid with new information ✅
c) Deals with uncertain models
d) Relies on heuristic knowledge

Question#17
In non-monotonic reasoning, what happens when new information is added?
a) Old conclusions can be negated ✅
b) Old conclusions are always valid
c) No change in conclusions
d) System crashes

Question#18
Which AI technique enables computers to understand human language?
a) Computer vision
b) Machine learning
c) Natural language processing ✅
d) Reinforcement learning

Question#19
What is the focus of computer vision in AI?
a) Understanding visual data ✅
b) Generating text
c) Making sequential decisions
d) Mimicking human expertise

Question#20
What do Expert systems use to make decisions?
a) Random numbers
b) Knowledge from human experts ✅
c) Complex algorithms
d) Trial and error