Cognitive Radio Questions and Answers – Next Generation Wireless Network – Knowledge Representation

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This set of Cognitive Radio Multiple Choice Questions & Answers (MCQs) focuses on “Next Generation Wireless Network – Knowledge Representation”.

1. Which among the following processes is required to extend radio adaption beyond predefined knowledge?
a) Sorting
b) Knowledge acquisition
c) Reasoning
d) Learning
View Answer

Answer: d
Explanation: The reasoning engine is responsible for applying the gathered knowledge in accordance with the current state of the system. However, the reactions will be limited to previously defined knowledge even under new situations. Learning provides the ability to study input, identify patterns, and modify the operation of the radio system as per the demands of the situations.
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2. Which among the following processes under learning action initiates a change in the action selection policy?
a) Observation of state of the radio system
b) Decision making in reasoning engine
c) Analyses of the outcome of the decision selected by the reasoning engine
d) Modification of knowledge base
View Answer

Answer: d
Explanation: The modification of the knowledge base step is generally the final step in a single learning cycle. The knowledge base is modified in accordance with the outcome of the actions implemented by the reasoning engine. The existing information in the knowledge base is altered, or the action selection policy is changed. Otherwise, new knowledge entries are included in the database.

3. What is called declarative knowledge?
a) Declarative knowledge represents a range of values applicable for assigning a variable
b) Declarative knowledge represents assumed statements
c) Declarative knowledge asserts and reasons over descriptive factual knowledge
d) Declarative knowledge represents predicted knowledge
View Answer

Answer: c
Explanation: Declarative knowledge involves asserting and reasoning over descriptive factual knowledge about an object. Declarative knowledge describes the semantic relationships between objects.
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4. Which among the following is concerned with the effect on a knowledge system?
a) Behaviour knowledge
b) Declarative knowledge
c) Narrative knowledge
d) Status and system knowledge
View Answer

Answer: a
Explanation: Behavioural knowledge involves actions and their effect on the knowledge system. Both internal and external effectors altering the system and the environment are included. An event begins with a set of assertions and terminates with a varying set of assertions.

5. Which among the following is a benefit of symbolic knowledge representation?
a) No encryption is required
b) Fast translation
c) Less memory storage
d) Easy human comprehension
View Answer

Answer: d
Explanation: Symbolic knowledge representation typically involves graphical or natural language notations. It is easy for humans to understand. It offers a simplistic means for conveying underlying information for human understanding and interpretation.
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6. Which among the following is not used for symbolic knowledge representation?
a) Interface
b) Objects
c) Semantic nets
d) Frames
View Answer

Answer: a
Explanation: In symbolic representation and reasoning systems, storage is performed using extensible data structures to capture facts, descriptions, and properties related to a concept. Semantic nets, rules, frames, and objects are some of the structures employed to represent knowledge.

7. Which among the following is employed for associating new concepts in the learning process for ontology based systems?
a) Dissimilarities between two consequent entries
b) Similarities between two consequent entries
c) Similarities to existing entries in memory
d) Dissimilarities to existing entries in memory
View Answer

Answer: c
Explanation: Learning in ontology based system involves integrating new knowledge with existing collection of entries in memory. A classifier is used to assess the degree of similarity between a new entry and the existing entries. The classifier accomplishes this by analyzing the properties and values of a new entry.
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8. What does an arc in a decision tree represent?
a) An action
b) A decision
c) Set of all possible choices
d) Set of all possible plans
View Answer

Answer: c
Explanation: A decision tree is a directed graph with a hierarchical set of nodes and arcs. A node represents a choice or a decision. An arc from one decision node to another decision node represents all possible choices associated with that node.

9. What is the parameter of analysis in reinforcement learning?
a) Number of requests during wake cycle
b) Number of processes to achieve final outcome
c) Degree of failure
d) Degree of success
View Answer

Answer: d
Explanation: Reinforcement based learning assigns a weightage to an action on the basis of the degree of success of its outcome. When situations requiring similar action arise, the weightage associated with an action is analyzed for compatibility. The degree of success is computed by measuring the closeness of the obtained outcome with the expected outcome.
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10. Which among the following statements provides the difference between reinforcement-based learning and temporal difference technique?
a) State represented by a directed graph
b) Assignment of weightage to an action on the basis of the degree of success
c) Computation of degree of success
d) Priori model of the sequence of possible states
View Answer

Answer: d
Explanation: The temporal difference algorithm does not a priori model of the sequence of possible states as the temporal difference algorithm constructs the state representation during execution. The states are composed as a value function and are stored on a neural network.

11. Which among the following is not a challenge of employing reasoning and learning stage in the cognitive radio?
a) Computational requirement
b) Quality of Service
c) Edge conditions
d) Predictable behaviour
View Answer

Answer: b
Explanation: Cognitive radio should provide a large amount of computational resources to achieve the results of each operation. Edge conditions refer to the devices which are positioned in a location without regular service and hence cannot benefit from learning techniques. Predictable behaviour refers to the ability to estimate the outcome of each step of the operation but not the final outcome.

12. Which among the following is not a challenge for case based reasoning implementation?
a) Memory
b) Fixed ontology and knowledge representation
c) Pattern matching
d) Accuracy of reasoning
View Answer

Answer: b
Explanation: Case based reasoning requires a large case database. It requires a large amount of computational resources for pattern matching and to modify the solution of a close match to satisfy the current problem. Case based reasoning can be implemented in any system provided it has large memory and processing power regardless of ontology and knowledge representation.

13. Which among the following options should replace the label ‘A’, ‘B’, and ‘C’ in rule based system?
Find the knowledge base from the given diagram
a) Memory, Inference Engine, Knowledge Base
b) Memory, Knowledge Base, Inference Engine
c) Knowledge Base, Memory, Inference Engine
d) Inference Engine, Memory, Knowledge Base
View Answer

Answer: c
Explanation: The knowledge base of the rule based system is composed of rules specified in the form of “if-then” clauses. The memory unit consists of the current state of the system. The inference engine is responsible for pattern matching.

14. Temporal reasoning allows a system to reason about its operational characteristics at discrete points in time.
a) True
b) False
View Answer

Answer: b
Explanation: Temporal reasoning allows a system to reason about its operational characteristics during an interval of time. For example, spectrum sensing over a period can help identify intervals of spectrum underutilization within that period of time.

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Manish Bhojasia - Founder & CTO at Sanfoundry
Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He is Linux Kernel Developer & SAN Architect and is passionate about competency developments in these areas. He lives in Bangalore and delivers focused training sessions to IT professionals in Linux Kernel, Linux Debugging, Linux Device Drivers, Linux Networking, Linux Storage, Advanced C Programming, SAN Storage Technologies, SCSI Internals & Storage Protocols such as iSCSI & Fiber Channel. Stay connected with him @ LinkedIn | Youtube | Instagram | Facebook | Twitter