AI Fundamentals - Practice MCQs for CCAT

Correct Answer: A - Human intelligence by machines

AI involves creating systems that can perform tasks that typically require human intelligence.

Correct Answer: B - Whether a machine can exhibit intelligent behavior indistinguishable from human

The Turing Test, proposed by Alan Turing, tests if a machine's responses are indistinguishable from a human's.

Correct Answer: B - Spam email filter

Narrow AI (Weak AI) is designed for specific tasks like spam filtering, chess playing, or voice assistants.

Correct Answer: B - AI with human-level intelligence across all domains

Artificial General Intelligence (AGI) would have human-level intelligence and ability to learn any intellectual task.

Correct Answer: B - Knowledge base and inference rules

Expert systems use knowledge bases and inference engines to emulate decision-making of human experts.

Correct Answer: B - Problem-solving approach using practical methods

Heuristics are practical problem-solving approaches that may not be optimal but are sufficient for immediate goals.

Correct Answer: D - Finding solutions in problem spaces

Search algorithms explore problem spaces to find solutions, paths, or optimal states.

Correct Answer: B - Optimal pathfinding using heuristics

A* is a best-first search algorithm that finds optimal paths using heuristics to guide the search.

Correct Answer: C - Encoding information for reasoning

Knowledge representation encodes information in a form that AI systems can use for reasoning and inference.

Correct Answer: D - Perceive environment and take actions to achieve goals

Intelligent agents perceive their environment through sensors and act upon it through actuators to achieve goals.

Correct Answer: C - Performance, Environment, Actuators, Sensors

PEAS describes an agent's Performance measure, Environment, Actuators, and Sensors.

Correct Answer: C - Degrees of truth between 0 and 1

Fuzzy logic deals with degrees of truth rather than strict true/false, handling uncertainty and vagueness.

Correct Answer: A - Biological evolution

Genetic algorithms use concepts from natural evolution: selection, crossover, and mutation to solve optimization problems.

Correct Answer: D - Whether computers can truly understand

Searle's Chinese Room argues that symbol manipulation (like programs) doesn't constitute genuine understanding.

Correct Answer: B - Generating sequence of actions to achieve goals

AI planning generates sequences of actions that transform an initial state into a desired goal state.

Correct Answer: A - Assigning values to variables satisfying all constraints

CSPs find assignments to variables that satisfy all constraints, used in scheduling, puzzles, etc.

Correct Answer: A - All nodes at current depth before deeper levels

BFS explores all nodes at the current depth level before moving to the next depth level.

Correct Answer: C - Stack data structure

DFS uses a stack (or recursion) to explore as far as possible along each branch before backtracking.

Correct Answer: C - Game playing (adversarial search)

Minimax is used in two-player games to minimize the maximum possible loss (optimal adversarial play).

Correct Answer: C - Eliminating branches that won't affect the outcome

Alpha-beta pruning eliminates branches that cannot influence the final decision, reducing search space.

Correct Answer: D - Narrow AI

Narrow AI (also called Weak AI) is designed to perform a specific task. Most current AI systems, such as Siri, Alexa, and chess engines, are examples of Narrow AI.

Correct Answer: A - 1950

Alan Turing proposed the Turing Test in 1950 in his paper 'Computing Machinery and Intelligence' to determine whether a machine can exhibit intelligent behavior indistinguishable from a human.

Correct Answer: B - Breadth-First Search

Breadth-First Search (BFS) explores all neighbor nodes at the present depth before moving on to nodes at the next depth level, using a queue data structure.

Correct Answer: D - Knowledge Base and Inference Engine

An expert system has two main components: a Knowledge Base (storing domain-specific facts and rules) and an Inference Engine (applying logical rules to the knowledge base to derive conclusions).

Correct Answer: B - Determinism

Intelligent agents are characterized by autonomy, reactivity, proactiveness, and social ability. Determinism is not a required characteristic; agents can operate in non-deterministic environments.

Correct Answer: A - Estimate the cost to reach the goal from a given state

A heuristic function estimates the cost from a given state to the goal state. It guides search algorithms like A* to find solutions more efficiently, though it may not always guarantee the optimal path.

Correct Answer: A - Connectionism

Connectionism (neural network-based approach) attempts to mimic the structure of the human brain using interconnected nodes (artificial neurons) to process information and learn from data.

Correct Answer: B - f(n) = g(n) + h(n)

A* uses f(n) = g(n) + h(n) where g(n) is the actual cost from the start to node n, and h(n) is the heuristic estimate of the cost from n to the goal. This combines actual and estimated costs.

Correct Answer: A - A hypothetical machine with human-level intelligence

Strong AI (Artificial General Intelligence) refers to a hypothetical system that possesses human-level intelligence and can perform any intellectual task a human can. No strong AI system exists yet.

Correct Answer: B - The algorithm will find a solution if one exists

Completeness means the algorithm is guaranteed to find a solution if one exists. For example, BFS is complete for finite graphs, while DFS may not be complete for infinite graphs.

Correct Answer: D - Semantic Networks

Semantic Networks represent knowledge as a graph of nodes (concepts) connected by edges (relationships). They are used in AI to model relationships between objects and concepts.

Correct Answer: A - Stack

DFS uses a stack (LIFO) data structure to keep track of nodes to visit. It can also be implemented using recursion, which implicitly uses the call stack.

Correct Answer: B - Model-Based Reflex Agent

A Model-Based Reflex Agent maintains an internal model of the world to handle partially observable environments. It updates the model based on percepts and uses it to make decisions.

Correct Answer: A - Decision making in adversarial games

The minimax algorithm is used for decision making in adversarial (two-player) games. It minimizes the possible loss for a worst-case scenario by considering all possible moves of both players.

Correct Answer: B - Minimax Algorithm

Alpha-beta pruning is an optimization technique for the minimax algorithm. It reduces the number of nodes evaluated in the game tree by pruning branches that cannot influence the final decision.

Correct Answer: A - AI that matches human cognitive abilities across all domains

Artificial General Intelligence (AGI) refers to AI that can understand, learn, and apply knowledge across a wide range of tasks at a level comparable to human intelligence, unlike narrow AI which is task-specific.

Correct Answer: C - Productions

In a production system, rules are called productions. They are condition-action pairs (IF-THEN rules) that specify what action to take when certain conditions are met in the working memory.

Correct Answer: C - Uniform Cost Search

Uniform Cost Search (UCS) expands the node with the lowest cumulative path cost from the start node. It uses a priority queue and guarantees finding the optimal solution when all step costs are non-negative.

Correct Answer: D - John Searle

John Searle proposed the Chinese Room argument in 1980 to challenge the idea that a computer running a program can truly 'understand' language or have a 'mind' in the way humans do.

Correct Answer: D - It can get stuck at local maxima

Hill Climbing is a local search algorithm that continuously moves in the direction of increasing value. Its main limitation is that it can get stuck at local maxima, plateaus, or ridges.

Correct Answer: D - Super AI

Super AI (also called Artificial Superintelligence or ASI) is a hypothetical form of AI that surpasses human intelligence in all aspects including creativity, problem-solving, and social intelligence.

Correct Answer: C - Known facts and derives new facts

Forward chaining is a data-driven approach that starts from known facts and applies inference rules to derive new facts until the goal is reached. It is the opposite of backward chaining.

Correct Answer: D - Recursive

AI environments are classified as deterministic/stochastic, episodic/sequential, static/dynamic, discrete/continuous, and fully/partially observable. 'Recursive' is not a standard environment classification.

Correct Answer: A - Lotfi Zadeh

Lotfi Zadeh introduced Fuzzy Logic in 1965. Unlike classical Boolean logic (true/false), fuzzy logic deals with degrees of truth, allowing reasoning with imprecise or uncertain information.

Correct Answer: C - Iterative Deepening Depth-First Search

Iterative Deepening DFS (IDDFS) combines BFS's completeness and optimality with DFS's space efficiency. It performs DFS repeatedly with increasing depth limits.

Correct Answer: C - Performance, Environment, Actuators, Sensors

PEAS stands for Performance measure, Environment, Actuators, and Sensors. It is a framework used to describe the task environment of an intelligent agent.

Correct Answer: C - Map coloring

Map coloring is a classic Constraint Satisfaction Problem (CSP) where the goal is to assign colors to regions such that no two adjacent regions have the same color, subject to defined constraints.

Correct Answer: A - Dartmouth Conference 1956

The term 'Artificial Intelligence' was coined at the Dartmouth Conference in 1956, organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon.

Correct Answer: D - The goal and works backward to find supporting facts

Backward chaining is a goal-driven approach that starts from the goal and works backward to find the facts that support it. It is commonly used in expert systems for diagnosis.

Correct Answer: D - O(b^d)

The time complexity of BFS is O(b^d) where b is the branching factor and d is the depth of the shallowest goal. BFS must explore all nodes at each level before moving deeper.