Cegielski + Test bank Tech Guide 4 Intelligent Systems 3e - Test Bank | Introduction to Info Systems 4th Canadian Edition by Rainer and Sanchez by Rainer Prince, Splettstoesser Hogeterp, Sanchez Rodriguez. DOCX document preview.
Tech Guide 4
Intelligent Systems
Question Type: True/False
1) Artificial intelligence is perishable, whereas natural intelligence is permanent from an organizational point of view.
Learning Objective: Differentiate between artificial intelligence and human intelligence.
Learning Objective 1: TG 4.1 Introduction to Intelligent Systems
Difficulty: Easy
2) It is difficult to document the knowledge of artificial intelligence, but it is easy to document the knowledge of natural intelligence.
Learning Objective: Differentiate between artificial intelligence and human intelligence.
Learning Objective 1: TG 4.1 Introduction to Intelligent Systems
Difficulty: Easy
3) Expertise refers to the extensive, task-specific knowledge acquired from training, reading, and experience.
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: TG 4.2 Expert Systems
Difficulty: Easy
4) Expert systems attempt to mimic human experts by applying expertise in a specific domain.
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: TG 4.2 Expert Systems
Difficulty: Easy
5) An expert system has a knowledge base and an inference engine.
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: TG 4.2 Expert Systems
Difficulty: Easy
6) One problem with expert systems is they decrease quality.
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: TG 4.2 Expert Systems
Difficulty: Easy
7) A neural network has two layers of interconnected nodes.
Learning Objective: Define neural networks, and provide examples of their use.
Learning Objective 1: TG 4.3 Neural Networks
Difficulty: Easy
8) Fuzzy logic defines subjective concepts.
Learning Objective: Define fuzzy logic, and provide examples of its use.
Learning Objective 1: TG 4.4 Fuzzy Logic
Difficulty: Easy
9) A genetic algorithm finds the combination of inputs that produces the best outputs.
Learning Objective: Define genetic algorithms, and provide examples of their use.
Learning Objective 1: TG 4.5 Genetic Algorithms
Difficulty: Easy
10) Intelligent agents use expert systems and fuzzy logic to create their behavior.
Learning Objective: Define intelligent agents, and provide examples of their use.
Learning Objective 1: TG 4.6 Intelligent Agents
Difficulty: Easy
11) The goal of artificial intelligence is to completely replace human intelligence.
Learning Objective: Differentiate between artificial intelligence and human intelligence.
Learning Objective 1: Introduction to Intelligent Systems
Difficulty: Easy
12) The explanation subsystem is used to justify recommendations.
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: Expert Systems
Difficulty: Easy
13) Neural networks simulate the underlying concepts of the biological brain.
Learning Objective: Define neural networks, and provide examples of their use.
Learning Objective 1: Neural Networks
Difficulty: Easy
14) Neural networks require complete inputs to be effective.
Learning Objective: Define neural networks, and provide examples of their use.
Learning Objective 1: Neural Networks
Difficulty: Easy
15) Fuzzy logic can only address problems that are black and white.
Learning Objective: Define fuzzy logic, and provide examples of its use.
Learning Objective 1: Fuzzy Logic
Difficulty: Easy
16) Google uses fuzzy logic.
Learning Objective: Define fuzzy logic, and provide examples of its use.
Learning Objective 1: Fuzzy Logic
Difficulty: Medium
17) A genetic algorithm is an optimizing method that finds the combination of outputs that produces the best inputs.
Learning Objective: Define genetic algorithms, and provide examples of their use.
Learning Objective 1: Genetic Algorithms
Difficulty: Easy
18) A shopping bot is also called a personal agent.
Learning Objective: Define intelligent agents, and provide examples of their use.
Learning Objective 1: Intelligent Agents
Difficulty: Easy
19) A user agent is also called a personal agent.
Learning Objective: Define intelligent agents, and provide examples of their use.
Learning Objective 1: Intelligent Agents
Difficulty: Easy
20) Which of the following is not a characteristic of natural intelligence?
a) It is perishable from an organizational point of view.
b) It is easy, fast, and inexpensive.
c) It is erratic and inconsistent.
d) It is highly creative.
e) It makes use of a wide context of experiences.
Learning Objective: Differentiate between artificial intelligence and human intelligence.
Learning Objective 1: TG 4.1 Introduction to Intelligent Systems
Difficulty: Medium
21) Which of the following is not a characteristic of artificial intelligence?
a) It is permanent.
b) It is easy, fast, and inexpensive.
c) It is highly creative.
d) It tends to be effective only in narrow domains.
e) It is consistent and thorough.
Learning Objective: Differentiate between artificial intelligence and human intelligence.
Learning Objective 1: TG 4.1 Introduction to Intelligent Systems
Difficulty: Medium
22) Expert systems:
a) solve problems that are too difficult for human experts
b) are based on procedural computer programming languages
c) work in specific domains
d) can apply to any business problem
e) share characteristics with mainframe computing
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: TG 4.2 Expert Systems
Difficulty: Medium
23) Which of the following statements is false?
a) Expert systems cannot replace decision makers.
b) Expert systems apply expertise in a specific domain.
c) Expert systems capture the expertise from a domain expert (a person).
d) Expert systems can be embedded in larger systems.
e) Expert systems follow a logical path towards a recommendation.
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: TG 4.2 Expert Systems
Difficulty: Medium
24) Which of the following is not an expert system activity?
a) Knowledge acquisition
b) Knowledge domain
c) Knowledge inferencing
d) Knowledge representation
e) Knowledge transfer
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: TG 4.2 Expert Systems
Difficulty: Medium
25) An inference engine is:
a) a data mining strategy used by intelligent agents
b) the programming environment of an expert system
c) a method of organizing expert system knowledge into chunks
d) a methodology used to search through the rule base of an expert system
e) the user interface of an expert system
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: TG 4.2 Expert Systems
Difficulty: Medium
26) Which of the following statements concerning expert systems is false?
a) The knowledge base contains facts.
b) The knowledge base contains rules.
c) An expert system can explain its recommendation.
d) The blackboard displays the recommendation.
e) Expert systems cannot learn from their own mistakes.
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: TG 4.2 Expert Systems
Difficulty: Medium
27) Which of the following is not a benefit of expert systems?
a) Increased output and productivity
b) Capture and dissemination of scarce expertise
c) Increased decision-making time
d) Reliability
e) Works with incomplete, uncertain information
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: TG 4.2 Expert Systems
Difficulty: Medium
28) Which of the following is not a limitation of expert systems?
a) Expert systems cannot work with incomplete or uncertain data.ans
b) A process might contain too many rules to work as an expert system.
c) A process might be too vague to work as an expert system.
d) Decisions made by expert systems might be a potential liability.
e) Expert systems need to learn from their own mistakes.
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: TG 4.2 Expert Systems
Difficulty: Medium
29) _____ refer(s) to computer reasoning that deals with uncertainties by simulating the process of human reasoning.
a) Expert systems
b) Artificial neural networks
c) Speech understanding systems
d) Fuzzy logic
e) Computer vision systems
Learning Objective: Define fuzzy logic, and provide examples of its use.
Learning Objective 1: TG 4.4 Fuzzy Logic
Difficulty: Medium
30) Fuzzy logic could be used to define which of the following terms:
a) Gender
b) Moderate income
c) Age
d) Address
e) Income
Learning Objective: Define fuzzy logic, and provide examples of its use.
Learning Objective 1: TG 4.4 Fuzzy Logic
Difficulty: Medium
31) Which of the following is not a characteristic of genetic algorithms?
a) Selection
b) Crossover
c) Transformation
d) Mutation
Learning Objective: Define genetic algorithms, and provide examples of their use.
Learning Objective 1: TG 4.5 Genetic Algorithms
Difficulty: Medium
32) Which of the following statements is false?
a) In genetic algorithms, crossover means combining portions of good outcomes.
b) Generic algorithms are best suited for decision making where there are thousands of solutions.
c) Users have to tell the generic algorithm what constitutes a “good” solution.
d) In genetic algorithms, mutation means randomly trying combinations and evaluating the outcome.
e) All of these statements are true.
Learning Objective: Define genetic algorithms, and provide examples of their use.
Learning Objective 1: TG 4.5 Genetic Algorithms
Difficulty: Medium
33) Which of the following statements is false?
a) An intelligent agent is a software program.
b) Intelligent agents are also called bots.
c) Intelligent agents are always helpful.
d) Intelligent agents use export systems and fuzzy logic.
e) Intelligent agents perform repetitive computer-related tasks.
Learning Objective: Define intelligent agents, and provide examples of their use.
Learning Objective 1: TG 4.6 Intelligent Agents
Difficulty: Easy
34) Which of the following statements is false?
a) Information agents search for information and store it for the user.
b) Information agents are used by Google to surf the Web sites in Google’s index.
c) Monitoring-and-surveillance agents are also called predictive agents.
d) Personal agents take action on behalf of the user.
e) User agents automatically fill out forms on the Web from stored information.
Learning Objective: Define intelligent agents, and provide examples of their use.
Learning Objective 1: TG 4.6 Intelligent Agents
Difficulty: Easy
35) Hyo runs an ice cream shop with her family. They need to train someone to close the store at the end of day. This process is an example of which type of intelligent system?
a) Expert systems
b) Neural network
c) Fuzzy logic
d) Genetic algorithms
e) Intelligent agent
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: TG 4.2 Expert Systems
Difficulty: Medium
36) Hyo runs an ice cream shop with her family. They have configured their computer to put any e-mail that contains the word “order” into a folder called Possible Orders. This process is an example of which type of intelligent system?
a) Expert systems
b) Neural network
c) Fuzzy logic
d) Genetic algorithms
e) Intelligent agent
Learning Objective: Define intelligent agents, and provide examples of their use.
Learning Objective 1: TG 4.6 Intelligent Agents
Difficulty: Medium
37) Hyo runs an ice cream shop with her family. They just started letting customers fax in their ice cream orders. Sometimes the writing is hard to read, and Hyo’s family has to guess what the customers have ordered based on what flavors the shop has. This process is an example of which type of intelligent system?
a) Expert systems
b) Neural network
c) Fuzzy logic.
d) Genetic algorithms
e) Intelligent agent.
Learning Objective: Define intelligent agents, and provide examples of their use.
Learning Objective 1: TG 4.6 Intelligent Agents
Difficulty: Medium
38) Which of the following is a characteristic of artificial intelligence?
a) It is difficult to document.
b) It makes use of a wide context of experiences.
c) It can be erratic, inconsistent, and incomplete at times.
d) It is a permanent preservation of knowledge.
Learning Objective: Differentiate between artificial intelligence and human intelligence.
Learning Objective 1: Introduction to Intelligent Systems
Difficulty: Easy
39) What is the primary purpose of the Turing test?
a) Determine whether computers can exhibit intelligent behavior.
b) Support or replace decision-makers.
c) Explains recommendations provided by the computer.
d) Recognize patterns within complex data.
Learning Objective: Differentiate between artificial intelligence and human intelligence.
Learning Objective 1: Introduction to Intelligent Systems
Difficulty: Medium
40) Which of the following is NOT an intelligent system?
a) ujam
b) IBM’s Watson
c) Blackboard
d) Norfolk Southern’s PLASMA
Learning Objective: Differentiate between artificial intelligence and human intelligence.
Learning Objective 1: Introduction to Intelligent Systems
Difficulty: Hard
41) ______________ attempt to mimic human experts by applying expertise in a specific domain.
a) Neural networks
b) Expert systems
c) Genetic algorithms
d) Intelligent agents
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: Expert Systems
Difficulty: Easy
42) Which of the following is the correct order of the activities to transfer expertise from the expert to a computer?
a) Acquisition, Representation, Inferencing, Transfer
b) Transfer, Acquisition, Representation, Inferencing
c) Acquisition, Inferencing, Representation, Transfer
d) Transfer, Acquisition, Inferencing, Representation
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: Expert Systems
Difficulty: Easy
43) In this stage of transferring expertise from the expert to a computer, acquired knowledge is organized as rules or frames and stored electronically in a knowledge base.
a) Acquisition
b) Inferencing
c) Representation
d) Transfer
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: Expert Systems
Difficulty: Easy
44) Neural networks are NOT used for _____________.
a) combatting fraud
b) preventing money-laundering
c) airline security
d) medical expertise
Learning Objective: Define neural networks, and provide examples of their use.
Learning Objective 1: Neural Networks
Difficulty: Medium
45) Neural networks are _________________.
a) a system of programs and data structures
b) used to approximate the operation of the human brain
c) particularly good at recognizing subtle, hidden, and newly emerging patterns in complex data
d) All of the above describe neural networks
Learning Objective: Define neural networks, and provide examples of their use.
Learning Objective 1: Neural Networks
Difficulty: Easy
46) _______________ is a mathematical method of handling imprecise or subjective information.
a) An expert system
b) Fuzzy logic
c) A neural network
d) An intelligent agent
Learning Objective: Define fuzzy logic, and provide examples of its use.
Learning Objective 1: Fuzzy Logic
Difficulty: Easy
47) Fuzzy logic handles _______________ concepts.
a) Black and white
b) Objective
c) Subjective
d) Well-defined
Learning Objective: Define fuzzy logic, and provide examples of its use.
Learning Objective 1: Fuzzy Logic
Difficulty: Easy
48) ______________ gives preference to better and better outcomes.
a) Crossover
b) Evolution
c) Mutation
d) Selection
Learning Objective: Define genetic algorithms, and provide examples of their use.
Learning Objective 1: Genetic Algorithms
Difficulty: Easy
49) Which of the following is NOT true of genetic algorithms?
a) They work best when there are only a few possible solutions.
b) They can be used to find and evaluate solutions intelligently.
c) They can process many more possibilities more thoroughly and faster than humans.
d) They mimic the evolutionary “survival of the fittest” process.
Learning Objective: Define genetic algorithms, and provide examples of their use.
Learning Objective 1: Genetic Algorithms
Difficulty: Medium
50) Randomly trying combinations and evaluating the success (or failure) of an outcome is _________________.
a) crossover
b) evolution
c) mutation
d) selection
Learning Objective: Define genetic algorithms, and provide examples of their use.
Learning Objective 1: Genetic Algorithms
Difficulty: Easy
51) Combining portions of good outcomes in the hope of creating an even better outcome is _________________.
a) crossover
b) mutation
c) selection
d) testing
Learning Objective: Define genetic algorithms, and provide examples of their use.
Learning Objective 1: Genetic Algorithms
Difficulty: Easy
52) Intelligent agents often use ________________ behind the scenes.
a) expert systems and fuzzy logic
b) expert systems and neural networks
c) fuzzy logic and neural networks
d) fuzzy logic and genetic algorithms
Learning Objective: Define intelligent agents, and provide examples of their use.
Learning Objective 1: Intelligent Agents
Difficulty: Medium
53) ____________ agents help customers find the products and services they need on a web site.
a) Information
b) Monitoring-and-surveillance
c) Buyer
d) User
Learning Objective: Define intelligent agents, and provide examples of their use.
Learning Objective 1: Intelligent Agents
Difficulty: Easy
54) A _____________ agent is software that will search several retailer websites and provide a comparison of each retailer’s offerings including price and availability.
a) Buyer
b) Personal
c) User
d) Information
Learning Objective: Define intelligent agents, and provide examples of their use.
Learning Objective 1: Intelligent Agents
Difficulty: Easy
55) ____________ agents constantly observe and report on some item of interest.
a) Information
b) Monitoring-and-surveillance
c) Buyer
d) User
Learning Objective: Define intelligent agents, and provide examples of their use.
Learning Objective 1: Intelligent Agents
Difficulty: Easy
Question Type: Essay
56) Compare and contrast expert systems and intelligent agents.
Learning Objective: Define neural networks, and provide examples of their use.
Learning Objective: Define fuzzy logic, and provide examples of its use.
Learning Objective 1: TG 4.3 Neural Networks
Section Reference: TG 4.4 Fuzzy Logic
Difficulty: Medium
57) How are neural networks useful to businesses?
Learning Objective: Define neural networks, and provide examples of their use.
Learning Objective: Define fuzzy logic, and provide examples of its use.
Learning Objective 1: TG 4.3 Neural Networks
Section Reference: TG 4.4 Fuzzy Logic
Difficulty: Medium
58) How are intelligent agents useful to businesses?
Learning Objective: Define neural networks, and provide examples of their use.
Learning Objective: Define fuzzy logic, and provide examples of its use.
Learning Objective 1: TG 4.3 Neural Networks
Section Reference: TG 4.4 Fuzzy Logic
Difficulty: Medium
59) Compare and contrast fuzzy logic and neural networks.
Learning Objective: Define neural networks, and provide examples of their use.
Learning Objective: Define fuzzy logic, and provide examples of its use.
Learning Objective 1: TG 4.3 Neural Networks
Section Reference: TG 4.4 Fuzzy Logic
Difficulty: Medium
60) What are the advantages and disadvantages of artificial intelligence systems?
Learning Objective: Differentiate between artificial intelligence and human intelligence.
Learning Objective 1: TG 4.1 Introduction to Intelligent Systems
Difficulty: Hard
61) Describe how your university could use an expert system in its admissions process.
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: TG 4.2 Expert Systems
Difficulty: Hard
62) Contrast the three types of intelligent agents and give examples of how they might be used in business.
Learning Objective: Define intelligent agents, and provide examples of their use.
Learning Objective 1: TG 4.6 Intelligent Agents
Difficulty: Hard
Question Type: Fill-in-the-Blank
63) Artificial intelligence is a subfield of computer science concerned with studying the thought processes of ____________.
Learning Objective: Differentiate between artificial intelligence and human intelligence.
Learning Objective 1: Introduction to Intelligent Systems
Difficulty: Easy
64) The _________________ is a computer program that provides a methodology for reasoning and formulating conclusions.
Learning Objective: Define expert systems, and provide examples of their use.
Learning Objective 1: Expert Systems
Difficulty: Easy
65) A _________________ is a system of programs and data structures that approximates the operation of the human brain.
Learning Objective: Define neural networks, and provide examples of their use.
Learning Objective 1: Neural Networks
Difficulty: Easy
66) Fuzzy logic is a mathematical method of handling _______________ information.
Learning Objective: Define fuzzy logic, and provide examples of its use.
Learning Objective 1: Fuzzy Logic
Difficulty: Easy
67) A ____________ mimics the evolutionary “survival-of-the-fittest” process to generate increasingly better solutions to a problem.
Learning Objective: Define genetic algorithms, and provide examples of their use.
Learning Objective 1: Genetic Algorithms
Difficulty: Easy
68) An intelligent agent is a software program that assists you, or acts on your behalf, in performing ______________________ computer-related tasks.
Learning Objective: Define intelligent agents, and provide examples of their use.
Learning Objective 1: Intelligent Agents
Difficulty: Medium
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Test Bank | Introduction to Info Systems 4th Canadian Edition by Rainer and Sanchez
By Rainer Prince, Splettstoesser Hogeterp, Sanchez Rodriguez