Artificial Intelligence MCQ

John McCarthy is also known as the “Father of Artificial Intelligence”.

Blind Search doesn’t contain any domain information such as closeness and hence it is best used for a Small Search Space.

AI is classified into 3 categories processes: Sensing, Reasoning, and Acting.

Machines that can change their course of action by making their own decisions are said to be Intelligent.

While programming can be done in any language, in today’s world Python has become the go-to language for AI and ML-related tasks due to its vast and diverse library functionalities.

Other than Database Management System, all the other options are viable fields of study in Artificial Intelligence.

true and false are the 2 proposition symbols in AI.

The logical symbols in AI are: Negation, Conjunction, Disjunction, Implication, and Biconditional symbols.

All of the above options are informed search methods used in Artificial Intelligence.

The name of the Artificial Intelligence system developed by Daniel Bobrow was STUDENT developed in the Lisp Language in 1964.

There are a total of 5 AI agents: Simple Reflex Agent, Model-Based Reflex Agent, Goal-based reflex agent, utility-based agent, learning agent.

There are 3 types of recognition in AI: Biometric Identification, Content-Based Image Retrieval, Handwriting Recognition.

Procedural Domain Knowledge in a rule-based system, is in the form of Production Rules.

All the given options are different types of AI approaches used in different scenarios.

The Turing Test was originally called the ‘The Imitation Game’ by its creator.

MISD architecture is also known as systolic arrays.

The Min/Max algorithm is preferred over other search-based algorithms in Game trees to make win/loss decisions optimally.

The only 2 ways to solve a problem of state-space search problem are given in options (A) and (B).

Machines running LISP are also called AI workstations.

An AI agent perceives and acts upon the environment using Sensors and Actuators. With Sensors, it senses the surrounding, and with Actuators, it acts on it.

Task Environments provides an issue and Rational Agents provides the corresponding solution.

There are 2 types of observing environments: Fully and Partial.

There are 2 types of quantification in AI: Universal and Existential.

DFS uses the lesser amount of memory because at a time the path from the node to a root is only stored in the memory stack unlike BFS which will have to store the whole tree within it.

Fuzzy Logic allows machines to mimic human intuition when provided with information that is vague in nature.

A complete algorithm is an algorithm that finds a solution to a problem(if it exists) in finite time.

The performance of an AI agent is improved using the learning agent.

All of the above algorithms given above are valid ML algorithms.

The machine learning methods include Memorization, Analogy, and Deduction.

In unsupervised learning, no external supervision is required so the machine learns on the basis of exploring the datasets themselves.

The 3 main things to be considered in the design of a learning element are: Components, Feedback, and Representation.

The 3 types of machine learning are: Supervised, Unsupervised, and Reinforcement Learning.

A decision tree makes its decision based on the results of a sequence of tests.

Discrete and Continuous variables both act as numerical inputs to a hybrid Bayesian network.

AI agents implement mapping for perceptions to actions.

The performance element is used for external action selection in AI agents.

The brightness of a pixel is directly proportional to the amount of light incident on the screen.

Unification is the process of making 2 logical expressions look identical.

PEAS is an abbreviation for Performance, Environment, Actuators, Sensors.

The representation of knowledge in AI can be done both as Predicate and Propositional Logic.

The 3 main levels for a knowledge-based AI agent are: Knowledge, Logical, and Implementation level.

Machine learning and Deep Learning are 2 of the ways to implement Artificial Intelligence in real life.

Both options A and B are heuristics-based search algorithms.

The syntax to multiply 2 numbers in LISP is correctly shown in option A.

The modes on which inference engines work are forward and backward chaining.

Segmentation is the process of breaking an image into groups based on the similarity of pixels.

All of the above options are valid 3D image processing techniques.

The process of building up a parse tree for an input string is called parsing.

Fifth Generation computers are said to be able to have Artificial Intelligence within them.

All the above options comprise the components of an expert system.

Depth First Search takes the least memory consumption because only the nodes on the current path are stored.

Reactive Machines are not a type of AI based on functionality.

State Space in AI is defined as the collection of all the problem states.