1. 计算机科学概述 Introduction to Computer Science 陆嘉恒 中国人民大学 信息学院 www.jiahenglu.net

2. Artificial Intelligence （人工智能）

3. Objectives In this class, you will learn about What is artificial intelligence Knowledge representation Recognition tasks Reasoning tasks Robotics

4. Introduction to Artificial Intelligence What is intelligence? –The capacity to acquire and apply knowledge. –The faculty of thought and reason. –The ability to learn or understand or to deal with new or trying situations.

5. Major Subdivisions of AI Understanding Thinking Acting

6. AI: Understanding Computer Vision – understanding what you see

7. AI: Thinking Capturing Structure and Reaching Goals –Machine Learning –Planning –Clustering

8. AI: Acting Robotics

9. Consider AI use in one company

10. Search

11. Sponsered Links

12. Google News

13. Google maps

14. Introduction Turing test –A test for intelligent behavior of machines –Allows a human being to interrogate two entities, both hidden from the interrogator A human being A machine (a computer)

15. The Turing Test

16. Introduction (continued) Turing test (continued) –If the interrogator is unable to determine which entity is the human being and which is the computer, the computer has passed the test Artificial intelligence can be thought of as constructing computer models of human intelligence

17. A Division of Labor Categories of tasks –Computational tasks –Recognition tasks –Reasoning tasks Computational tasks –Tasks for which algorithmic solutions exist –Computers are better (faster and more accurate) than human beings

18. A Division of Labor (continued) Recognition tasks –Sensory/recognition/motor-skills tasks –Human beings are better than computers Reasoning tasks –Require a large amount of knowledge –Human beings are far better than computers

19. Figure 14.2 Human and Computer Capabilities

20. Knowledge Representation Knowledge: A body of facts or truths For a computer to make use of knowledge, it must be stored within the computer in some form

21. Knowledge Representation (continued) Knowledge representation schemes –Natural language –Formal language –Pictorial –Graphical

22. Knowledge Representation (continued) Required characteristics of a knowledge representation scheme –Adequacy –Efficiency –Extendability –Appropriateness

23. Recognition Tasks A neuron is a cell in the brain capable of –Receiving stimuli from other neurons through its dendrites –Sending stimuli to other neurons through its axon

24. Figure 14.4 A Neuron

25. Recognition Tasks (continued) If the sum of activating and inhibiting stimuli received by a neuron equals or exceeds its threshold value, the neuron sends out its own signal Each neuron can be thought of as an extremely simple computational device with a single on/off output

26. Recognition Tasks (continued) Human brain: A connectionist architecture –A large number of simple “processors” with multiple interconnections Von Neumann architecture –A small number (maybe only one) of very powerful processors with a limited number of interconnections between them

27. Recognition Tasks (continued) Artificial neural networks (neural networks) –Simulate individual neurons in hardware –Connect them in a massively parallel network of simple devices that act somewhat like biological neurons The effect of a neural network may be simulated in software on a sequential- processing computer

28. Recognition Tasks (continued) Neural network –Each neuron has a threshold value –Incoming lines carry weights that represent stimuli –The neuron fires when the sum of the incoming weights equals or exceeds its threshold value A neural network can be built to represent the exclusive OR, or XOR, operation

29. Figure 14.5 One Neuron with Three Inputs

30. Figure 14.8 The Truth Table for XOR

31. Recognition Tasks (continued) Neural network –Both the knowledge representation and “programming” are stored as weights of the connections and thresholds of the neurons –The network can learn from experience by modifying the weights on its connections

32. Reasoning Tasks Human reasoning requires the ability to draw on a large body of facts and past experience to come to a conclusion Artificial intelligence specialists try to get computers to emulate this characteristic

33. Intelligent Searching State-space graph –After any one node has been searched, there are a huge number of next choices to try –There is no algorithm to dictate the next choice State-space search –Finds a solution path through a state-space graph

34. Figure 14.12 A State-Space Graph with Exponential Growth

35. Intelligent Searching (continued) Each node represents a problem state Goal state: The state we are trying to reach Intelligent searching applies some heuristic (or an educated guess) to –Evaluate the differences between the present state and the goal state –Move to a new state that minimizes those differences

36. Swarm Intelligence Swarm intelligence –Models the behavior of a colony of ants Swarm intelligence model –Uses simple agents that Operate independently Can sense certain aspects of their environment Can change their environment May “evolve” and acquire additional capabilities over time

37. Intelligent Agents An intelligent agent: Software that interacts collaboratively with a user Initially an intelligent agent simply follows user commands

38. Intelligent Agents (continued) Over time –Agent initiates communication, takes action, and performs tasks on its own using its knowledge of the user’s needs and preferences

39. Expert Systems Rule-based systems –Also called expert systems or knowledge- based systems –Attempt to mimic the human ability to engage pertinent facts and combine them in a logical way to reach some conclusion

40. Expert Systems (continued) A rule-based system must contain –A knowledge base: Set of facts about subject matter –An inference engine: Mechanism for selecting relevant facts and for reasoning from them in a logical way Many rule-based systems also contain –An explanation facility: Allows user to see assertions and rules used in arriving at a conclusion

41. Expert Systems (continued) A fact can be –A simple assertion –A rule: A statement of the form if... then... Modus ponens (method of assertion) –The reasoning process used by the inference engine

42. Expert Systems (continued) Inference engines can proceed through –Forward chaining –Backward chaining Forward chaining –Begins with assertions and tries to match those assertions to “if” clauses of rules, thereby generating new assertions

43. Expert Systems (continued) Backward chaining –Begins with a proposed conclusion Tries to match it with the “then” clauses of rules –Then looks at the corresponding “if” clauses Tries to match those with assertions or with the “then” clauses of other rules

44. Expert Systems (continued) A rule-based system is built through a process called knowledge engineering –Builder of system acquires information for knowledge base from experts in the domain

45. Robotics Robot: Device that can gather sensory information autonomously Many uses for robots (auto manufacturing, bomb disposal, exploration, microsurgery) Deliberative strategy: Robot has an internal representation of its environment Reactive strategy: Uses heuristic algorithms to allow robot to respond directly to environment

46. Summary Artificial intelligence explores techniques for incorporating aspects of intelligence into computer systems Categories of tasks: Computational tasks, recognition tasks, reasoning tasks Neural networks simulate individual neurons in hardware and connect them in a massively parallel network

47. Summary (continued) Swarm intelligence models the behavior of a colony of ants Intelligent agent interacts with a user Rule-based systems attempt to mimic the human ability to engage pertinent facts and combine them in a logical way to reach some conclusion Robots can perform many useful tasks

48. Conclusions AI is big business Still can't do most things What it can do it does extremely well Major Subdivision of AI –vision and language –robotics –machine learning