Answers are in blue.

Computer Science Illuminated, Seventh Edition Nell Dale, PhD; John Lewis, PhD

CHAPTER 13 EXERCISES AND ANSWERS

 

For Exercises 1–5, match the type of ambiguity with an example.

1. Lexical词汇歧义
2. Referential指代歧义
3. Syntactic句法
   1. “Stand up for your flag.”
      1. Does “stand up” mean support or rise to your feet?
   2. “Go down the street on the left.”

C. Does this mean to go left down the street or go down the street that is on the left?

1. 1. “He drove the car over the lawn mower, but it wasn’t hurt.”

B. What wasn’t hurt, the car or the lawn mower?

1. 1. “I saw the movie flying to Houston.” C
   2. “Mary and Kay were playing until she came inside.” B

For Exercises 6–21, mark the answers true and false as follows:

1. 1. 1. True
      2. False
   2. A computer does some tasks much better than a human being.

A

1. 1. A human being does some tasks much better than a computer.

A

1. 1. A computer system that can pass the Turing test is consid- ered to be intelligent.

A

1. 1. Some AI researchers don’t think we can achieve true artificial intelligence until a computer processes information in the same way the human mind does.

A

1. 1. A semantic network is used to model relationships. A
   2. 语义网络用于建模关系。
   3. If information is stored in a semantic network, it is easy to answer questions about it.

B (it depends on how the network is structured)

如果信息存储在语义网络中，回答相关问题会很容易。

B（这取决于网络的结构）

1. 1. A computer has never beaten a human at chess in master- level play.

B

 

1. 1. An inference engine is part of a rule-based expert system. A
   2. A biological neuron accepts a single input signal and pro- duces multiple output signals.

B

生物神经元多个输入一个输出

1. 1. Each element in an artificial neural net is affected by a numeric weight.

A

1. 1. Voice synthesis is the most difficult part of natural language processing.

B语音合成不是最苦难的

1. 1. Each human has a unique voiceprint that can be used to train voice recognition systems.

A

1. 1. The word “light” can be interpreted in many ways by a computer.

A

1. 1. Syntactic ambiguity is no longer a problem for natural lan- guage comprehension.

B

1. 1. A robot may follow the sense–plan–act approach to control its movements.

A

1. 1. Isaac Asimov created three fundamental laws of robotics. A
   2. 6. 计算机在某些任务上比人类做得更好。
   3. A
   4. 7. 人类在某些任务上比计算机做得更好。
   5. A
   6. 8. 能通过图灵测试的计算机系统被认为是智能的。
   7. A
   8. 9. 一些人工智能研究人员认为，只有当计算机以与人类思维相同的方式处理信息时，我们才能实现真正的人工智能。
   9. A
   10. 10. 语义网络用于建模关系。
   11. A
   12. 11. 如果信息存储在语义网络中，回答相关问题会很容易。
   13. B（这取决于网络的结构）
   14. 12. 计算机在大师级别的国际象棋比赛中从未战胜过人类。
   15. B
   17. 13. 推理引擎是基于规则的专家系统的一部分。
   18. A
   19. 14. 生物神经元接受单一输入信号并产生多个输出信号。
   20. B
   21. 15. 人工神经网络中的每个元素都受到数值权重的影响。
   22. A
   23. 16. 语音合成是自然语言处理中最困难的部分。
   24. B
   25. 17. 每个人都有一个独特的声纹，可用于训练语音识别系统。
   26. A
   27. 18. 计算机可以以多种方式解释单词“light”。
   28. A
   29. 19. 对于自然语言理解，句法歧义不再是问题。
   30. B
   31. 20. 机器人可能遵循感知-计划-执行方法来控制其动作。
   32. A
   33. 21. 艾萨克·阿西莫夫创立了机器人学的三个基本定律。
   34. A

For Exercises 22–30, match the task with which (human or computer) can solve it more easily.

1. 1. 1. Computer
      2. Human
   2. Identify a dog in a picture. B
   3. Add a column of 100 four-digit numbers. A
   4. Interpret a poem. B
   5. Match a finger print. A
   6. Paint a landscape. B
   7. Carry on an intelligent conversation. B

对于练习22-30，将任务与更容易解决它的一方（人类或计算机）进行匹配。

A. 计算机

B. 人类

56. 识别图片中的狗。 B

57. 添加100个四位数的列。 A

58. 解释一首诗。 B

59. 匹配指纹。 A

60. 绘制一幅风景画。 B

61. 进行智能对话。 B

1. 1. Learn to speak. B
   2. Judge guilt or innocence. B
   3. Give affection. B

Exercises 31–76 are problems or short-answer questions.

1. 1. What is the Turing test?

The Turing test is a test devised by Alan Turing to answer the question “How can we know we’ve succeeded in creating a machine that can think?” The test is based on whether a computer could fool a human into believing that the com- puter is another human being.

灵测试是由艾伦·图灵设计的一种测试，旨在回答“我们如何知道我们成功地创建了一台能够思考的机器？”的问题。该测试基于计算机是否能够欺骗人类，使其相信计算机是另一名人类。

1. 1. How is the Turing test organized and administered?

A human interrogator sits in a room and uses a computer terminal to communicate with two respondents. The interro- gator knows that one respondent is human and the other is a computer. After conversing with both the human and the computer, the interrogator must decide which respondent is the computer. If the computer could fool enough interroga- tors, then it must be considered intelligent.

一位人类询问者坐在一个房间里，使用计算机终端与两个被询问者进行通信。询问者知道其中一个被询问者是人类，另一个是计算机。在与人类和计算机都进行对话之后，询问者必须决定哪个被询问者是计算机。如果计算机能够欺骗足够多的询问者，那么它就被认为是具有智能的。

1. 1. What is weak equivalence and how does it apply to the Turing test?

Weak equivalence is the equality of two systems based on their results. The Turing test shows weak equivalence.

弱等价性是基于两个系统的结果的相等性。图灵测试展示了弱等价性。

1. 1. What is strong equivalence?

Strong equivalence is the quality of two systems based on their results and the process by which they arrive at those results.

强等价性是基于两个系统的结果以及它们达到这些结果的过程的质量

1. 1. What is the Loebner prize?

The Loebner prize is the first formal instantiation of the Tur- ing test. It has been held annually since 1991.

洛布纳奖是图灵测试的第一个正式实例。自1991年以来，它每年都会举行

1. 1. Name and describe briefly five issues in the world of AI cov- ered in this chapter.

Knowledge representation: The techniques used to rep- resent knowledge so that a computer system can use it in problem solving.

Expert systems: Computer systems that embody the knowl- edge of human experts.

Neural networks: Computer systems that mimic the process- ing of the human brain.

Natural-language processing: Computer systems that pro- cess the language that humans use to communicate.

Robotics: The study of mobile robots that use AI techniques to interact with their environments.

知识表示：用于表示知识以便计算机系统在问题解决中使用的技术。

专家系统：具有人类专家知识的计算机系统。

神经网络：模拟人脑处理过程的计算机系统。

自然语言处理：处理人类用于交流的语言的计算机系统。

机器人学：研究使用人工智能技术与环境互动的移动机器人。

1. 1. Name and define two knowledge representation techniques. Semantic networks: A technique that represents the relation- ships among objects.

Search trees: A structure that represents alternatives in adversarial situations such as games.

语义网络：一种表示对象之间关系的技术。

搜索树：表示在对抗性情境中的替代方案的结构。

 

1. 3. What data structure defined in Chapter 8 is used to represent a semantic network?

A graph is used to represent a semantic network. The nodes in the graph represent objects and the arrows (arcs) repre- sent relationships.

1. 1. Create a semantic network for the relationships among your family members. List five questions that your semantic net could easily be used to answer and five questions that would be more of a challenge to answer.

 

Easy questions to answer given this organization: Who are John’s children?

What is the gender of Kayla? How old are Sharon’s children?

How many female children does John have?

Does Sharon have any children older than 5 years of age?

More challenging questions to answer given this organization: Who are Kayla’s parents?

Who are Justin’s siblings?

How many female children are there? Who is the mother of John’s children? Does John have any step-children?

1. 1. Create a semantic network that captures the information in a small section of a newspaper article.

This is an activity for which no answer is appropriate.

1. 1. What object-oriented properties do semantic networks borrow?

Semantic networks borrow inheritance and instantiation. The inheritance is expressed in the “is-a” relationship, and instanti- ation is expressed when an object is related to something that describes it.

语义网络借用了继承和实例化。继承表达了“是一个”关系，而实例化是指当一个对象与描述它的东西相关联时。

1. 1. What is a search tree?

A search tree is a structure that represents all possible moves for both players in a two-person game.

搜索树是一种表示两人游戏中双方所有可能走法的结构。

1. 1. What are trees for complex games like chess to large?

A search tree contains all possible moves from the first posi- tion, all possible moves from each of the moves from the first position, , all possible moves from all possible moves at

the level above. Thus the trees are very large for complex games like chess.

对于复杂游戏如国际象棋，树的结构会变得很大。

搜索树包含了从初始位置开始的所有可能移动，以及每个从初始位置开始的移动的所有可能移动，以此类推，一直到上一级的所有可能移动。因此，对于复杂游戏如国际象棋，树的结构会非常庞大。

1. 1. Distinguish between depth-first searching and breadth-first searching.

Depth-first searching begins at the top level (root) and con- tinues going deeper and deeper into the tree until the search has reached a leaf node, at which time the search moves back up one level and starts down again. A breadth-first search begins at the top level, then searches every node on the next lower level, then searches every node at the next lower level, until it has searched every node on every level.

深度优先搜索从顶层（根）开始，不断深入树中，直到搜索到达叶节点，此时搜索会回溯到上一层并重新开始。而广度优先搜索则从顶层开始，然后搜索下一层的每个节点，接着搜索下一层的每个节点，直到搜索完每个层的每个节点。

1. 1. What does it mean to prune a tree?

Pruning a tree means to eliminate some branches from searching.

剪枝是指从搜索中消除一些分支。

1. 1. Distinguish between knowledge-based systems and expert systems.

A knowledge-based system is a software system that uses a specific set of information from which it extracts and pro- cesses particular pieces. An expert system is sometime used as a synonym, but it also carries with it the idea of modeling the expertise of a professional in that particular field.

基于知识系统是一种软件系统，它使用特定的信息集，从中提取和处理特定的信息片段。专家系统有时被用作同义词，但它还带有模拟该领域专业人士的专业知识的概念。

1. 1. Distinguish be rule-based systems and inference engines.

A rule-based system is a software system that uses a set of rules to guide its processing. An inference engine is the soft- ware system that processes the rules.

基于规则系统是一种软件系统，它使用一组规则来引导其处理。推理引擎是处理规则的软件系统。

1. 1. What is an example of a human expert system?

A doctor is an example of a human expert system. The doc- tor asks questions and runs tests based on his knowledge and experience.

医生是人类专家系统的一个例子。医生根据其知识和经验询问问题并进行测试。

1. 1. What do we call a knowledge-based system that models the expertise of professionals in the field?

An expert system.

专家系统被称为基于规则的系统，因为它使用一组规则来引导其处理

1. 1. Why is an expert system called a rule-based system?

An expert system is called a rule-based system because it uses a set of rules to guide its processing.

1. 1. What is the part of the software in an expert system that determines how the rules are followed and what conclusions can be drawn?

An inference engine.

推理引擎。

1. 1. How are the rules expressed in an expert system?

The rules are expressed as selection statements (if statements).

规则被表达为选择语句（if 语句）。

1. 1. What are the advantages of an expert system?

An expert system is goal oriented; it doesn’t focus on abstract or theoretical information. It is efficient; it records previous responses and doesn’t ask irrelevant questions. An expert system can provide useful guidance even if it can’t provide the answer to a specific question.

专家系统是目标导向的；它不关注抽象或理论性的信息。它高效；它记录先前的响应并不问无关的问题。即使不能回答特定问题，专家系统仍然可以提供有用的指导。

1. 1. What is a single cell that conducts a chemically based elec- tronic signal?

A neuron.

1. 1. What do a series of connected neurons form? A pathway in the brain.

 

1. 2. Upon what does the signal along a particular pathway depend? The signals depend on the state of the neurons through which the signal passes.
   3. 信号沿特定通路的传播取决于什么？信号取决于沿途神经元的状态。
   4. 生物神经元中的多输入触角是什么？树突。
   6. What are the multiple input tentacles in a biological neuron? Dendrites
   7. 树突
   8. What is the primary output tentacle in a biological neuron? An axon.
   9. 生物神经元中的主要输出触角是什么？轴突。
   10. 轴突
   11. From where do dendrites of one neuron pick up the signals from other neurons to form a network?

The dendrites of one neuron pick up the signals from the axons of other neurons to forma neural network.

一个神经元的树突从其他神经元的轴突接收信号，形成神经网络。

1. 1. What is the gap between an axon and a dendrite? A synapse.
   2. 轴突和树突之间有什么间隙？突触。
   3. What tempers the strength of a synapse?

The chemical composition of a synapse tempers the strength of its input signal.

什么调节了突触的强度？突触的化学成分调节了其输入信号的强度。

1. 1. What is the role of a synapse?

The role of a synapse is to weight the input signal.

突触的作用是加权输入信号。

1. 1. How is a synapse modeled in an artificial neural network?

A synapse is represented by a weight assigned to each input signal.

如何在人工神经网络中建模突触？突触由分配给每个输入信号的权重表示。

1. 1. What is an effective weight in an artificial neuron?

An effective weight is the sum of the weights multiplied by the corresponding input values.

工神经元中的有效权重是什么？有效权重是权重乘以相应输入值的总和。

1. 1. How is the output value from an artificial neuron calculated? Each neuron has a numeric threshold value. If the effective weight is greater than the threshold, a 1 is output; otherwise, a 0 is output.
   2. 人工神经元的输出值如何计算？每个神经元都有一个数值阈值。如果有效权重大于阈值，则输出为1；否则，输出为0。
   3. If the processing element in an artificial neural net accepted five input signals with values of 0, 0, 1, 1, and 0 and corre- sponding weights of 5, -2, 3, 3, and 6, what is the output if the threshold is 5?

1

如果人工神经网络中的处理元素接受了值为0、0、1、1和0的五个输入信号，相应的权重为5、2、3、3和6，如果阈值为7，输出是什么？

0

1. 1. If the processing element in an artificial neural net accepted five input signals with values of 0, 0, 1, 1, and 0 and corre- sponding weights of 5, -2, 3, 3, and 6, what is the output if the threshold is 7?

0

1. 1. What is a phoneme?

A phoneme is a fundamental sound in a language.

什么是音素？

音素是语言中的基本音

1. 1. Describe the two distinct ways that voice synthesis can be accomplished.

In dynamic voice generation, the set of phonemes for a lan- guage are generated. A computer examines the letters that make up a word and produce the sequence of sounds using the language’s phonemes.

In recorded speech, human speech is recorded. A computer chooses the correct word from its file of recorded words.

Dynamic voice generation can make an attempt to pro- nounce any word, but recorded speech can only pronounce words that have been prerecorded.

描述实现语音合成的两种明显方式。

在动态语音生成中，生成语言的音素集。计算机检查组成一个单词的字母，并使用语言的音素生成声音序列。

在记录语音中，记录人类的语音。计算机从其已记录的单词文件中选择正确的单词。

动态语音生成可以尝试发音任何单词，但记录的语音只能发音已经预先记录的单词。

1. 1. Which issues affect the ability to recognize the words spoken by a human voice?

Accents, regional dialects, voice pitch, homonyms, and the clarity of a person’s speech.

口音、地方方言、语调、同音异义词以及个人言语的清晰度。