Description : What are Semantic Networks? a) A way of representing knowledge b) Data Structure c) Data Type d) None of the mentioned
Last Answer : a) A way of representing knowledge
Description : There exists two way to infer using semantic networks in which knowledge is represented as Frames. 1) Intersection Search 2) Inheritance Search a) True b) False
Last Answer : 1) Intersection Search
Description : Like semantic networks, frames can be queried using spreading activation. a) True b) False
Last Answer : a) True
Description : Most semantic networks are not cognitive based. a) True b) False
Last Answer : b) False
Description : Semantic Network is also known as Frame networks. a) True b) False
Description : What are the limitations of the semantic networks? a) Intractability b) Lack in expressing some of the properties c) Incomplete d) Has memory constraints
Last Answer : b) Lack in expressing some of the properties
Description : Which of the following is an extension of the semantic network? a) Expert Systems b) Rule Based Expert Systems c) Decision Tree Based networks d) Partitioned Networks
Last Answer : d) Partitioned Networks
Description : What is Hyponymy relation? a) A is part of B b) B has A as a part of itself c) A is subordinate of B d) A is superordinate of B
Last Answer : c) A is subordinate of B
Description : What is Holonymy relation? a) A is part of B b) B has A as a part of itself c) A is a kind of B d) A is superordinate of B
Last Answer : b) B has A as a part of itself
Description : What is Meronymy relation? a) A is part of B b) B has A as a part of itself c) A is a kind of B d) A is superordinate of B
Last Answer : a) A is part of B
Description : Many words have more than one meaning; we have to select the meaning which makes the most sense in context. This can be resolved by ____________ a) Fuzzy Logic b) Word Sense Disambiguation c) Shallow Semantic Analysis d) All of the mentioned
Last Answer : b) Word Sense Disambiguation
Description : Where does the dependance of experience is reflected in prior probability sentences? a) Syntactic distinction b) Semantic distinction c) Both Syntactic & Semantic distinction d) None of the mentioned
Last Answer : a) Syntactic distinction
Description : What kind of interpretation is done by adding context-dependant information? a) Semantic b) Syntactic c) Pragmatic d) None of the mentioned
Last Answer : c) Pragmatic
Description : What can’t be done in the semantic interpretation? a) Logical term b) Complete logical sentence c) Both Logical term & Complete logical sentence d) None of the mentioned
Last Answer : c) Both Logical term & Complete logical sentence
Description : What is the extraction of the meaning of utterance? a) Syntactic b) Semantic c) Pragmatic d) None of the mentioned
Last Answer : b) Semantic
Description : What is not represented by using propositional logic? a) Objects b) Relations c) Both Objects & Relations d) None of the mentioned
Last Answer : c) Both Objects & Relations
Description : Which is true for neural networks? a) It has set of nodes and connections b) Each node computes it’s weighted input c) Node could be in excited state or non-excited state d) All of the mentioned
Last Answer : d) All of the mentioned
Description : Which of the following is true? Single layer associative neural networks do not have the ability to: (i) perform pattern recognition (ii) find the parity of a picture (iii)determine whether two or more shapes in ... ) are true b) (ii) is true c) All of the mentioned d) None of the mentioned
Last Answer : a) (ii) and (iii) are true
Description : What are the advantages of neural networks over conventional computers? (i) They have the ability to learn by example (ii) They are more fault tolerant (iii)They are more suited for real time operation due to their high ... true b) (i) and (iii) are true c) Only (i) d) All of the mentioned
Description : Which of the following is true? (i) On average, neural networks have higher computational rates than conventional computers. (ii) Neural networks learn by example. (iii) Neural networks mimic the way the human brain works. ... ) are true c) (i), (ii) and (iii) are true d) None of the mentioned
Last Answer : a) All of the mentioned are true
Description : Which of the following is the model used for learning? a) Decision trees b) Neural networks c) Propositional and FOL rules d) All of the mentioned
Description : Which of the following is true? Single layer associative neural networks do not have the ability to: (i) perform pattern recognition (ii) find the parity of a picture (iii)determine whether two or more shapes in a ... ) are true b) (ii) is true c) All of the mentioned d) None of the mentioned
Description : Which of the following is true for neural networks? (i) The training time depends on the size of the network. (ii) Neural networks can be simulated on a conventional computer. (iii) Artificial neurons are identical in ... b) (ii) is true c) (i) and (ii) are true d) None of the mentioned
Last Answer : c) (i) and (ii) are true
Description : Which of the following is true? (i) On average, neural networks have higher computational rates than conventional computers. (ii) Neural networks learn by example. (iii) Neural networks mimic the way the human brain works ... ) are true c) (i), (ii) and (iii) are true d) None of the mentioned
Description : Semantic grammars are _____________ a) Encode semantic information into a syntactic grammar b) Decode semantic information into a syntactic grammar c) Encode syntactic information into a semantic grammar d) Decode syntactic information into a semantic grammar
Last Answer : a) Encode semantic information into a syntactic grammar
Description : The “Turing Machine” showed that you could use a/an _____ system to program any algorithmic task. a) binary b) electro-chemical c) recursive d) semantic
Last Answer : a) binary
Description : What is the process of associating a FOL expression with a phrase? a) Interpretation b) Augmented reality c) Semantic interpretation d) Augmented interpretation
Last Answer : c) Semantic interpretation
Description : The basic inference mechanism in semantic network in which knowledge is represented as Frames is to follow the links between the nodes. a) True b) False
Description : Frames in artificial intelligence is derived from semantic nets. a) True b) False
Description : A semantic network is used when one has knowledge that is best understood as a set of concepts that are related to one another. a) True b) False
Description : The basic inference mechanism in semantic network is to follow the links between the nodes. a) True b) False
Description : Graph used to represent semantic network is _____________ a) Undirected graph b) Directed graph c) Directed Acyclic graph (DAG) d) Directed complete graph
Last Answer : b) Directed graph
Description : Define Semantic Interpretation.
Last Answer : The process of extracting the meaning of an utterance an expression in some representation language.
Description : Slots and facets are used in (A) Semantic Networks (B) Frames (C) Rules (D) All of these
Last Answer : (B) Frames
Description : What are the 3 types of symbol which is used to indicate objects, relations and functions?
Last Answer : i) Constant symbols for objects ii) Predicate symbols for relations iii) Function symbols for functions
Description : With an example, show objects, properties functions and relations.
Last Answer : Example “EVIL KING JOHN BROTHER OF RICHARD RULED ENGLAND IN 1200” Objects : John, Richard, England, 1200 Relation : Ruled Properties : Evil, King Functions : BROTHER OF
Description : A _________ is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. a) Decision tree b) Graphs c) Trees d) Neural Networks
Last Answer : a) Decision tree
Description : Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions
Last Answer : a) Linear Functions
Description : Why is the XOR problem exceptionally interesting to neural network researchers? a) Because it can be expressed in a way that allows you to use a neural network b) Because it is complex ... solved by a single layer perceptron d) Because it is the simplest linearly inseparable problem that exists.
Last Answer : d) Because it is the simplest linearly inseparable problem that exists.
Description : Neural Networks are complex ______________with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions
Last Answer : b) Nonlinear Functions
Description : Core of soft Computing is A. Fuzzy Computing, Neural Computing, Genetic Algorithms B. Fuzzy Networks and Artificial Intelligence C. Artificial Intelligence and Neural Science D. Neural Science and Genetic Science
Last Answer : D. Neural Science and Genetic Science
Description : Perceptron is A. General class of approaches to a problem. B. Performing several computations simultaneously C. Structures in a database those are statistically relevant D. Simple forerunner of modern neural networks, without hidden layers
Last Answer : D. Simple forerunner of modern neural networks, without hidden layers
Description : Neurons or artificial neurons have the capability to model networks of original neurons as found in brain A. True B. False
Last Answer : A. True
Description : Neural Networks are complex ———————–with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions e) Power Functions
Description : This set of Artificial Intelligence MCQs focuses on Neural Networks - 2 . 1. Why is the XOR problem exceptionally interesting to neural network researchers? a) Because it can be expressed in ... by a single layer perceptron d) Because it is the simplest linearly inseparable problem that exists.
Description : What is multilayer feed forward neural networks?
Last Answer : It consists of many hidden units. Each hidden unit act as a perceptron that represents a soft threshold functions in the input space. Output unit act as a soft threshold linear combination of several such functions.
Description : What are the two functions in Neural network’s Activation functions?
Last Answer : i. Threshold function ii. Sigmoid function