This set of Cognitive Radio Multiple Choice Questions & Answers (MCQs) focuses on “Artificial Intelligence – 1”.
1. Bayesian networks uses ____
a) directed cyclic graph
b) directed acyclic graph
c) undirected cyclic graph
d) undirected acyclic graph
Explanation: A Bayesian network is a probabilistic graphical model where a group of variables and their conditional dependencies are represented using directed acyclic graph. Directed acyclic graph is a directed graph and does not have directed cycles.
2. Bayesian network can match ____ with ____
a) an incident, probable cause
b) a definite cause, probable consequence
c) a incident, similar incident
d) a cause, similar cause
Explanation: A Bayesian network examines an event and determines the probability of a known cause activating that event. For example, a Bayesian network can be used to examine the symptoms of a person and match it with a probable disease.
3. Bayesian network that models speech signals are called ____
a) signal Bayesian network
b) sequential Bayesian network
c) series Bayesian network
d) dynamic Bayesian network
Explanation: Bayesian network that replicate sequence of variables such as speech signals are called as dynamic Bayesian network. It has been applied in robotics, bioinformatics, and digital forensics. It is a generalisation of Hidden Markov Model and Kalman filter.
4. Which among the following is not represented by node in Bayesian network?
a) Latent variables
b) Observable variables
Explanation: The nodes in a Bayesian network represent latent variables, observable variables, unknown parameters, and hypotheses. The conditions are represented using edges. The nodes which are not connected by edges are conditionally independent.
5. The process of computing the ____ of variables when provided with supporting conditions is called ____
a) prior distribution, probabilistic inference
b) posterior distribution, probabilistic inference
c) prior distribution, probabilistic query
d) posterior distribution, probabilistic query
Explanation: The process of computing the posterior distribution of variables when provided with supporting conditions is called probabilistic inference. It is probability of one or more variable taking a particular set of values.
6. Expert system is a computer system capable of resolving _____ problems.
b) decision making
Explanation: An expert system is a computer system that performs decision making based on facts and heuristic approach. It can solve difficult issues occurring within a specific domain.
7. Which among the following is used expert systems?
a) If-then rules
b) Arithmetic rules
c) Logical rules
d) Procedure rules
Explanation: Expert systems are enriched with information and use reasoning to solve complex decision making problems. The process is depicted with a set of if-then conditions instead of conventional procedural code.
8. What are the two major units of expert systems?
a) Knowledge base and performance engine
b) Performance engine and inference engine
c) Radio base and performance engine
d) Knowledge base and inference engine
Explanation: Knowledge base is a large storage of facts and rules obtained from experts of a domain. Inference engine uses the knowledge to deduce new solutions and resolve the query presented by the user.
9. Which among the following is considered as disadvantage of expert system?
a) Fast prototyping
b) Development process
d) Knowledge acquisition
Explanation: The process of gathering information for knowledge base is difficult as it required input from highly experienced and valued domain experts. Several tools are being developed to automate the process of collecting relevant information, designing, and maintenance of rules.
10. DENDRAL is an example for expert system.
Explanation: DENDRAL is a successful implementation of expert system in organic chemistry. It was developed in 1965. It is applied in chemical analysis to predict molecular structure.
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