1. PART IV:
The Potential of
Algorithmic Machines

2. Artificial Intelligence.
Theory of Computation.

3. Ch. 10 Artificial Intelligence
Some philosophical issues.
Image analysis.
Reasoning.
Control system activities.
Using Heuristics.
Artificial neural networks.
Applications of AI.

4. Some Philosophical Issues
Machines Vs. humans.
Performance Vs. simulation.
Intelligence as an interior characteristic - Turing test and program DOCTOR (ELIZA).
How to create an intelligent machine?

5. 10.1 Intelligence and Machines
Turing Test : 1950, Alan Turing proposed a test to evaluate the intelligent behavior of a machine.

6. Figure 10.1: Our puzzle-solving machine

7. An Intelligent puzzle-solving machine
This machine takes the form of a metal box equipped with a gripper, a video camera, and a finger with a rubber end so that it does not slip when pushing something.
Actions: Turn on the machine Place the puzzle The finger pushes the tiles back to the original order Turn off the machine.

8. 10.2 Image Analysis
The first intelligent behavior required by the puzzle-solving machine is the extraction of information through a visual medium.
Perceive ability - determine the current status of the puzzle.
Optical character readers.
Character recognition based on matching the geometric characteristics.

9. Figure 10.2: The eight-puzzle in its solved configuration

10. Figure 10.3: A small portion of the eight-puzzle’s state graph

11. 10.3 Reasoning
Is possible to develop proper programs targeted to all possible initial configurations (in total 181,440 of them)?
Develop a program which can solve the problem itself - the ability to make decisions, draw conclusions, and in short, perform elementary reasoning activities.

12. Reasoning A production system consists of three main components:
1. A collection of states - start/goal states.
2. A collection of productions (rules).
3. A control system - which consists of the logic that solves the problem of moving from the start state to the goal state.
State graph - conceptualizing all states, rules, and preconditions in a production system.

13. Reasoning Start state Goal state Socrates is a man.
All men are humans.
All humans are mortal.
Start state
Socrates is a man.
All men are humans.
All humans are mortal.
Socrates is a human.
Goal state
Socrates is a man.
All men are humans.
All humans are mortal.
Socrates is a human.
Socrates is mortal.

14. Figure 10.4: Deductive reasoning in the context of a production system

15. Control System Activities
A state-graph traversal problem.
Search tree.
How to build a search tree?
It is impractical to develop a full search tree for a complex problem.
Using depth-first construction instead of breadth-first manner.
Avoiding redundancy.

16. Figure 10.5: An unsolved eight-puzzle

17. Figure 10.6: A sample search tree (continued)

18. Figure 10.6: A sample search tree (continued)

19. Figure 10.6: A sample search tree (continued)

20. Figure 10.6: A sample search tree

21. Figure 10.7: Productions stacked for later execution

22. Figure 10.8: An unsolved eight-puzzle

23. Figure 10.9: An algorithm for a control system using heuristics

24. Using Heuristics
Heuristics - the use of intuition, a rule of thumb which may lead to a correct direction but offer no assurance on it.
How to develop a heuristic - first develop a quantitative measure by which a program can determine which of several states is considered closest to the goal (cost function).

25. Figure 10.10: The beginning of our heuristic search

26. Figure 10.11: The search tree after two passes

27. Figure 10.12: The search tree after three passes

28. Figure 10.13: The complete search tree formed by our heuristic system

29. 10.4 Artificial Neural Networks
Neural networks - model networks of neurons in living biological systems.
Compute effective
inputs
Threshold
value
Output
0 or 1
I1W1+…+InWn

30. Figure 10.14: A neuron in a living biological system

31. Figure 10.15: The activities within a processing unit

32. Figure 10.16: Representation of a processing unit

33. Figure 10.17: A neural network with two different programs (continued)

34. Figure 10.17: A neural network with two different programs

35. Figure 10.18: Uppercase C and uppercase T

36. Figure 10.19: Various orientations of the letters C and T (continued)

37. Figure 10.20: The structure of the character recognition system

38. Figure 10.21: The letter C in the field of view

39. Figure 10.22: The letter T in the field of view

40. Figure 10. 23: An artificial neural network
Figure 10.23: An artificial neural network implementing an associative memory

41. Figure 10.24: The steps leading to a stable configuration (continued)

42. Figure 10.24: The steps leading to a stable configuration

43. Figure 10.25: Crossing two poker-playing strategies

44. Figure 10. 26: Coding the topology of an artificial
Figure 10.26: Coding the topology of an artificial neural network (continued)

45. Figure 10.26: Coding the topology of an artificial neural network

46. Figure 10.27: A semantic net

47. 10.6 Applications of Artificial Intelligence
Language processing.
Robotics.
Database systems.
Expert systems.