PART IV: The Potential of Algorithmic Machines

Artificial Intelligence. Theory of Computation.

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

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?

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

Figure 10.1: Our puzzle-solving machine

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: 1. Turn on the machine. 2. Place the puzzle. 3. The finger pushes the tiles back to the original order. 4. Turn off the machine.

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.

Figure 10.2: The eight-puzzle in its solved configuration

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

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.

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.

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.

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

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.

Figure 10.5: An unsolved eight-puzzle

Figure 10.6: A sample search tree (continued)

Figure 10.6: A sample search tree (continued)

Figure 10.6: A sample search tree (continued)

Figure 10.6: A sample search tree

Figure 10.7: Productions stacked for later execution

Figure 10.8: An unsolved eight-puzzle

Figure 10.9: An algorithm for a control system using heuristics

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).

Figure 10.10: The beginning of our heuristic search

Figure 10.11: The search tree after two passes

Figure 10.12: The search tree after three passes

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

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

Figure 10.14: A neuron in a living biological system

Figure 10.15: The activities within a processing unit

Figure 10.16: Representation of a processing unit

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

Figure 10.17: A neural network with two different programs

Figure 10.18: Uppercase C and uppercase T

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

Figure 10.20: The structure of the character recognition system

Figure 10.21: The letter C in the field of view

Figure 10.22: The letter T in the field of view

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

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

Figure 10.24: The steps leading to a stable configuration

Figure 10.25: Crossing two poker-playing strategies

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

Figure 10.26: Coding the topology of an artificial neural network

Figure 10.27: A semantic net

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