1. Chapter 11: Artificial Intelligence

2. Chapter 11: Artificial Intelligence
Intelligence and Machines
Perception
Reasoning
Additional Areas of Research
Artificial Neural Networks
Robotics

3. Artificial intelligence
Artificial intelligence is the field of computer science that seeks to build autonomous machines that can carry out complex tasks without human intervention. AI areas: Psychology, neurology, mathematics, linguistics, and electrical and mechanical engineering.

4. Intelligent Agents
Agent: A “device” that responds to stimuli from its environment
Sensors
Actuators
An interactive video game
A process communicating with other processes over the Internet
Much of the research in AI can be viewed in the context of building agents that behave intelligently

5. Levels of Intelligent Behavior
Reflex: actions are predetermined responses to the input data
More intelligent behavior requires knowledge of the environment and involves such activities as:
Goal searching
Learning

6. The eight-puzzle in its solved configuration

7. Here.

8. Our puzzle-solving machine
1. Perceive in the sense that it must extract the current puzzle state from the image it receives from its camera
2. Develop and implement a plan for obtaining a goal.

9. Approaches to Research in AI
Engineering track
develop systems that exhibit intelligent behavior
natural language processing
Theoretical track
develop a computational understanding of human intelligence
Linguists

10. Turing Test
Test setup: Human communicates with test subject by typewriter.
Test: Can the human distinguish whether the test subject is human or machine?

11. Turing test examples
Internet viruses carry on “intelligent” dialogs with a human victim in order to trick the human into dropping his or her malware guard.
Computer games such as chess-playing programs. These programs select moves merely by applying brute-force techniques, humans competing against the computer often experience the sensation that the machine possesses creativity and even a personality.

12. Techniques for understanding Images
Template matching
Image processing
identifying characteristics of the image
Image analysis
process of understanding what characteristics mean

13. A smartphone AI application

14. Language Processing Syntactic Analysis Semantic Analysis
Parsing. Finding the object, subject, verb of a sentence.
Grammatical roles.
Semantic Analysis
the task of identifying the semantic role of each word
Contextual Analysis
The sentence is brought into the understanding process

15. A semantic net for information extraction

16. Reasoning

17. Components of a Production Systems
1. Collection of states
Start (or initial) state
Goal state (or states)
2. Collection of productions: rules or moves
Each production may have preconditions
3. Control system:
decides which production to apply next

18. Reasoning by searching
State Graph: All states and productions
Search Tree: A record of state transitions explored while searching for a goal state
Breadth-first search
Depth-first search

19. A small portion of the eight-puzzle’s state graph

20. Deductive reasoning

21. Deductive reasoning in the context of a production system

22. An unsolved eight-puzzle

23. A sample search tree

24. Productions stacked for later execution

25. Heuristic Strategies Heuristic: A “rule of thumb” for making decisions
Requirements for good heuristics
Must be easier to compute than a complete solution
Must provide a reasonable estimate of proximity to a goal

26. An unsolved eight-puzzle

27. An algorithm for a control system using heuristics

28. The beginnings of our heuristic search

29. The search tree after two passes

30. The search tree after three passes

31. The complete search tree formed by our heuristic system

32. Handling Real-World Knowledge
Representation and storage
Accessing relevant information
Meta-Reasoning
Closed-World Assumption
Frame problem

33. Learning
Imitation
Supervised Training
Training Set
Reinforcement

34. Genetic Algorithms
Begins by generating a random pool of trial solutions:
Each solution is a chromosome
Each component of a chromosome is a gene
Repeatedly generate new pools
Each new chromosome is an offspring of two parents from the previous pool
Probabilistic preference used to select parents
Each offspring is a combination of the parent’s genes

35. Artificial Neural Networks
Artificial Neuron
Each input is multiplied by a weighting factor.
Output is 1 if sum of weighted inputs exceeds the threshold value; 0 otherwise.
Network is programmed by adjusting weights using feedback from examples.

36. A neuron in a living biological system

37. The activities within a processing unit

38. Representation of a processing unit

39. A neural network with two different programs

40. The structure of ALVINN

41. Associative Memory
Associative memory: The retrieval of information relevant to the information at hand
Build associative memory using neural networks that when given a partial pattern, transition themselves to a completed pattern.

42. An artificial neural network implementing an associative memory

43. The steps leading to a stable configuration

44. Robotics
Truly autonomous robots require progress in perception and reasoning.
Major advances being made in mobility
Plan development versus reactive responses
Evolutionary robotics

45. Issues Raised by AI
When should a computer’s decision be trusted over a human’s?
If a computer can do a job better than a human, when should a human do the job anyway?
What would be the social impact if computer “intelligence” surpasses that of many humans?

46. Isaac Asimov's "Three Laws of Robotics"
A robot may not injure a human being or, through inaction, allow a human being to come to harm.
A robot must obey orders given it by human beings except where such orders would conflict with the First Law.
A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.