Chapter 10 Artificial Intelligence

2 Chapter 10: Artificial Intelligence 10.1 Intelligence and Machines 10.2 Understanding Images 10.3 Reasoning 10.4 Artificial Neural Networks 10.5 Genetic Algorithms 10.6 Other Areas of Research 10.7 Considering the Consequences

3 Intelligent agents Agent = “ device ” that responds to stimuli from its environment Sensors — receive data from environment --microphone, camera Actuators — response to the stimuli --legs, wings, hands, The goal of artificial intelligence is to build agents that behave intelligently

4 Levels of intelligence in behavior Reflex: actions are predetermined responses to the input data Intelligent response: actions affected by knowledge of the environment Goal seeking Learning

5 Artificial intelligence research approaches Agent ’ s intelligence is judged by observing its input-response patterns Performance oriented: Researcher tries to maximize the performance of the agents. Simulation oriented: Researcher tries to understand how the agents produce responses.

6 Turing test Proposed by Alan Turing in 1950 Benchmark for progress in artificial intelligence Test setup: Human interrogator communicates with test subject by typewriter. Test: Can the human interrogator distinguish whether the test subject is human or machine?

7 Figure 10.1 The eight-puzzle in its solved configuration

8 Figure 10.2 Our puzzle-solving machine

9 Techniques for understanding images The first intelligent behavior required by the puzzle-solving machine is the extraction of information through a visual medium. Unlike photographing an image, the problem is to understand the image (Computer Vision) – the ability to perceive. Since the possible images are finite, the machine can merely compare the different sections of the picture to prerecorded templates pixel by pixel, and reveal the condition of the puzzle.

10 Techniques for understanding images Optical readers apply the similar method for image recognition (hand-writing). A certain degree of uniformity(style, size, orientation, non-overlapping, … ) is required. The alternative is to first extract the geometric features(digit 1: a single vertical line) and make comparison in terms of these features

11 Techniques for understanding images Template matching Image processing: identify the characteristics of the image. Edge enhancement to clarify the boundary (taking a derivative), Region(with common properties: color, … ) finding for identifying objects, Smoothing(removing flaws/noises in image),

12 Techniques for understanding images Image analysis: identify the meaning of these characteristics. -- It is to recognize partially obstructed objects from different perspectives. -- First, assumption of what the image might be is made. (clue) -- Then, associate the image components with the objects conjectured to exist.

13 Reasoning After deciphering the positions of tiles from visual image, the remaining task is to move the tiles to reach the final state from the current state. The eight-puzzle has many configurations such that explicitly hard-coded each case for problem solving is not generally feasible. Some algorithm is necessary to resolve the problem in a systematic way. The machine will then ably make decisions, draw conclusions, and perform elementary reasoning activities.

14 Components of production systems A production system classifies the common characteristics shared by a class of reasoning problems, and has the following components: 1. Collection of states Start or initial state Goal state 2. Collection of productions: rules or moves Each production may have preconditions 3. Control system: decides which production to apply next

15 Data processing for production systems State graph = states, productions, and preconditions A graph consists of nodes and arcs(arrows) connecting nodes.A state graph has nodes representing states and arrows representing rules. The arc linking two nodes signifies two states can be shift to each other using the rule; the absence of arcs implicitly indicates the preconditions are not met. The problem magnitude may be too large for explicitly showing the entire state graph. Partial representation of the state graph will help understand the problem.

16 Data processing for production systems State graph = states, productions, and preconditions Search tree = record of state transitions explored while searching for a goal state Breadth-first search Depth-first search

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

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

19 Figure 10.5 An unsolved eight-puzzle

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 Heuristic strategies Generally, a search tree may grow much huger if the nodes ’ fan-outs are large. It becomes more complicate if the goal is very far away. (more generations) Developing a full (exhaustive) search tree[brute-force methods] may be impractical. In contrast to this breadth-first approach (layer by layer), we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner. (vertical)

24 Heuristic strategies Heuristic strategy is to develop a heuristic–a quantitative measure on how close a state is to the goal. Requirements for good heuristics Must be much easier to compute than a complete solution Must provide a reasonable estimate of proximity to a goal

25 Figure 10.9 An algorithm for a control system using heuristics

26 Figure The beginnings of our heuristic search

27 Figure The search tree after two passes

28 Figure The search tree after three passes

29 Figure The complete search tree formed by our heuristic system

30 Neural networks CPU is not capable of perceive and reasoning Artificial neuron Each input is multiplied by a weighting factor. Output is 1 if sum of weighted inputs exceeds a threshold value; 0 otherwise. Network is programmed by adjusting weights using feedback from examples.

31 Figure A neuron in a living biological system

32 Neural networks ANN are multi-processing architectures to model networks of concurrent neurons. Each processing unit in ANN is a simple device to simulate the neuron. The output of the unit may be 0 or 1, (or the fractional numbers in-between), dependent on the whether its effective input exceeds a given threshold value.

33 Figure The activities within a processing unit

34 Figure Representation of a processing unit

35 Figure A neural network with two different programs: (a) o=1 when 2 inputs diff (b)o=1 when both I=1

36 Neural networks Different weights determine different output values.Case (a) will produce 1 if its two inputs differ, while (b) outputs 1 if both inputs are 1’s. A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron.

37 character recognition: A specific application –character recognition: distinguish C and T, regardless of the orientation.The network produces a 0 if the recognized letter is C, or a 1 if the letter is a T.

38 Figure Uppercase C and uppercase T

39 Figure Various orientations of the letters C and T

40 Neural networks The system contains two levels of units. The first level has many units, one for each 3x3 block of pixels. Each unit has nine inputs; the inputs of adjacent units overlap.Threshold =.5, center’s weight = 2, others’weights = -1. The second level only has one unit, with a separate input for each unit in the first level. Threshold =.5, each weight = 1. It outputs a 1 iff at least one input is a 1.

41 Neural networks If “C”is present, all the first level units will produce a 0.All the possible cases can be enumerated. If “T”is present, only the first level’s unit (highlighted below) will output a 1, while others output 0’s.The final output is 1.

42 Figure The structure of the character recognition system

43 Figure The letter C in the field of view

44 Figure The letter T in the field of view

45 Associative memory Associative memory = the retrieval of information relevant to the information at hand One direction of research seeks to build associative memory using neural networks that when given a partial pattern, transition themselves to a completed pattern.

46 Figure An artificial neural network implementing an associative memory 1.The lines connecting circles are two-way connection i.e. output of one unit is connected As input of other unit 2. The number associated with Lines are weights 3. The number inside the circle is threshold

47 Figure The steps leading to a stable configuration Two stable states 1.Perimeter stable state (later stable state) When we initialize the Network with a least four Adjacent units on the Perimeter in their excited states 1

48 The steps leading to a stable configuration Two stable states 2. Center stable state (former stable state) When we initialize the Network with center excited And no more than two of Perimeter in their excited states

49 Genetic algorithms Simulate genetic processes to evolve algorithms Start with an initial population of “ partial solutions. ” Graft together parts of the best performers to form a new population. Periodically make slight modifications to some members of the current population. Repeat until a satisfactory solution is obtained.

50 Figure Crossing two poker-playing strategies

51 Figure Coding the topology of an artificial neural network

52 Language processing Syntactic analysis(subject,verb, noun) Semantic analysis(identify actions) Contextual analysis(understanding) --The bat flew from his hand. Entire database Information retrieval(web searching) Information extraction(template) Semantic net(a large linked data structure)

53 Figure A semantic net

54 Robotics Began as a field within mechanical and electrical engineering Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55 Expert systems Expert system = software package to assist humans in situations where expert knowledge is required Example: medical diagnosis Often similar to a production system Blackboard model: several problem-solving systems share a common data area

56 Some issues raised by artificial intelligence 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?