Que- Like relational databases there does exists fuzzy relational databases.
a. TRUE
b. False
c. Nothing can be said
d. None of the mentioned
Ans- TRUE
Que- ______________ is/are the way/s to represent uncertainty.
a. Fuzzy Logic
b. Probability
c. Entropy
d. All of the mentioned
Ans- All of the mentioned
Que- ____________ are algorithms that learn from their more complex environments (hence eco) to generalize, approximate and simplify solution logi
c.
a. Fuzzy Relational DB
b. Ecorithms
c. Fuzzy Set
d. None of the mentioned
Ans- Fuzzy Set
Que- Which algorithm is used for solving temporal probabilistic reasoning?
a. Hill-climbing search
b. Hidden markov model
c. Depth-first search
d. Breadth-first search
Ans- Hidden markov model
Que- How does the state of the process is described in HMM?
a. Literal
b. Single random variable
c. Single discrete random variable
d. None of the mentioned
Ans- Single discrete random variable
Que- What are the possible values of the variable?
a. Variables
b. Literals
c. Discrete variable
d. Possible states of the world
Ans- Possible states of the world
Que- Where does the additional variables are added in HMM?
a. Temporal model
b. Reality model
c. Probability model
d. All of the mentioned
Ans- Temporal model
Que- Which allows for a simple and matrix implementation of all the basic algorithm?
a. HMM
b. Restricted structure of HMM
c. Temporary model
d. Reality model
Ans- Restricted structure of HMM
Que- Where does the Hidden Markov Model is used?
a. Speech recognition
b. Understanding of real world
c. Both Speech recognition & Understanding of real world
d. None of the mentioned
Ans- Speech recognition
Que- Which variable can give the concrete form to the representation of thetransition model?
a. Single variable
b. Discrete state variable
c. Random variable
d. Both Single & Discrete state variable
Ans- Both Single & Discrete state variable
Que- Which algorithm works by first running the standard forward pass to compute?
a. Smoothing
b. Modified smoothing
c. HMM
d. Depth-first search algorithm
Ans- Modified smoothing
Que- Which reveals an improvement in online smoothing?
a. Matrix formulation
b. Revelation
c. HMM
d. None of the mentioned
Ans- Matrix formulation
Que- Which suggests the existence of efficient recursive algorithm for online smoothing?
a. Matrix
b. Constant space
c. Constant time
d. None of the mentioned
Ans- Constant space
Que- In LISP, the function returns t if <integer> is even and nil otherwise:
a. (evenp <integer>)
b. (even <integer>)
c. (numeven <integer>)
d. (numnevenp <integer>)
Ans- (evenp <integer>)
Que- Which of the following is an advantage of using an expert system development tool?
a. imposed structure
b. knowledge engineering assistance
c. rapid prototyping
d. all of the mentioned
Ans- all of the mentioned
Que- An AI system developed by Daniel Bobrow to read and solve algebra word problems
a. SHRDLU
b. SIMD
c. BACON
d. STUDENT
Ans- STUDENT
Que- 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
Ans- binary
Que- MCC is investigating the improvement of the relationship between people and computers through a technology called:
a. computer-aided design
b. human factors
c. parallel processing
d. all of the mentioned
Ans- human factors
Que- The first widely-used commercial form of Artificial Intelligence (Al) is being used in many popular products like microwave ovens, automobiles and plug in circuit boards for desktop PCs. It allows machines to handle vague information with a deftness that mimics human intuition. What is the name of this Artificial Intelligence?
a. Boolean logic
b. Human logic
c. Fuzzy logic
d. Functional logic
Ans- Fuzzy logic
Que- In his landmark book Cybernetics, Norbert Wiener suggested a way of modeling scientific phenomena using not energy, but:
a. mathematics
b. intelligence
c. information
d. history
Ans- information
Que- Input segments of AI programming contain(s)
a. sound
b. smell
c. touch
d. None of the mentioned
Ans- None of the mentioned
Que- The applications in the Strategic Computing Program include:
a. battle management
b. autonomous systems
c. pilot's associate
d. all of the mentioned
Ans- all of the mentioned
Que- In LISP, the function evaluates <object> and assigns this value to the unevaluated <sconst>.
a. (constant <sconst> <object>)
b. (defconstant <sconst> <object>)
c. (eva <sconst> <object>)
d. (eva <object> <sconst>)
Ans- (defconstant <sconst> <object>)
Que- What will take place as the agent observes its interactions with the world?
a. Learning
b. Hearing
c. Perceiving
d. Speech
Ans- Learning
Que- Which modifies the performance element so that it makes better decision?
a. Performance element
b. Changing element
c. Learning element
d. None of the mentioned
Ans- Learning element
Que- How many things are concerned in design of a learning element?
a. 1
b. 2
c. 3
d. 4
Ans- 3
Que- What is used in determining the nature of the learning problem?
a. Environment
b. Feedback
c. Problem
d. All of the mentioned
Ans- Feedback
Que- How many types are available in machine learning?
a. 1
b. 2
c. 3
d. 4
Ans- 3
Que- Which is used for utility functions in game playing algorithm?
a. Linear polynomial
b. Weighted polynomial
c. Polynomial
d. Linear weighted polynomial
Ans- Linear weighted polynomial
Que- Which is used to choose among multiple consistent hypotheses?
a. Razor
b. Ockham razor
c. Learning element
d. None of the mentioned
Ans- Ockham razor
Que- What will happen if the hypothesis space contains the true function?
a. Realizable
b. Unrealizable
c. Both Realizable & Unrealizable
d. None of the mentioned
Ans- Unrealizable
Que- What takes input as an object described by a set of attributes?
a. Tree
b. Graph
c. Decision graph
d. Decision tree
Ans- Decision tree
Que- How the decision tree reaches its decision?
a. Single test
b. Two test
c. Sequence of test
d. No test
Ans- Sequence of test
Que- Factors which affect the performance of learner system does not include
a. Representation scheme used
b. Training scenario
c. Type of feedback
d. Good data structures
Ans- Good data structures
Que- Different learning method does not include:
a. Memorization
b. Analogy
c. Deduction
d. Introduction
Ans- Introduction
Que- 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
Ans- All of the mentioned
Que- Automated vehicle is an example of ______
a. Supervised learning
b. Unsupervised learning
c. Active learning
d. Reinforcement learning
Ans- Supervised learning
Que- Following is an example of active learning:
a. News Recommender system
b. Dust cleaning machine
c. Automated vehicle
d. None of the mentioned
Ans- News Recommender system
Que- In which of the following learning the teacher returns reward and punishment to learner?
a. Active learning
b. Reinforcement learning
c. Supervised learning
d. Unsupervised learning
Ans- Reinforcement learning
Que- Decision trees are appropriate for the problems where:
a. Attributes are both numeric and nominal
b. Target function takes on a discrete number of values.
c. Data may have errors
d. All of the mentioned
Ans- All of the mentioned