Chapter 10 Artificial Intelligence Chapter 10 Artificial Intelligence

Chapter 10 Artificial Intelligence

Chapter 10: Artificial Intelligence n n n n 10. 1 10. 2 10. 3 10. 4 10. 5 10. 6 10. 7 Intelligence and Machines Understanding Images Reasoning Artificial Neural Networks Genetic Algorithms Other Areas of Research Considering the Consequences 2

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

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

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

Turing test n n 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? 6

Figure 10. 1 The eight-puzzle in its solved configuration 7

Figure 10. 2 Our puzzle-solving machine 8

Techniques for understanding images n n n 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. �� 9

Techniques for understanding images n n n 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 10

Techniques for understanding images n n n 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), �� 11

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. n 12

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. 13

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 n n Start or initial state Goal state 2. Collection of productions: rules or moves n Each production may have preconditions 3. Control system: decides which production to apply next 14

Data processing for production systems n n 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. 15

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

Figure 10. 3 A small portion of the eightpuzzle’s state graph 17

Figure 10. 4 Deductive reasoning in the context of a production system 18

Figure 10. 5 An unsolved eight-puzzle 19

Figure 10. 6 A sample search tree 20

Figure 10. 7 Productions stacked for later execution 21

Figure 10. 8 An unsolved eight-puzzle 22

Heuristic strategies n n 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)�� 23

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

Figure 10. 9 An algorithm for a control system using heuristics 25

Figure 10. 10 The beginnings 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

Neural networks n n CPU is not capable of perceive and reasoning Artificial neuron n 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. 30

Figure 10. 14 A neuron in a living biological system 31

Neural networks n n 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. 32

Figure 10. 15 The activities within a processing unit 33

Figure 10. 16 Representation of a processing unit 34

Figure 10. 17 A neural network with two different programs: (a) o=1 when 2 inputs diff (b)o=1 when both I=1 35

Neural networks n n 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. 36

character recognition: n 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. 37

Figure 10. 18 Uppercase C and uppercase T 38

Figure 10. 19 Various orientations of the letters C and T 39

Neural networks The system contains two levels of units. �� The first level has many units, one for each 3 x 3 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. 40

Neural networks n n 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. 41

Figure 10. 20 The structure of the character recognition system 42

Figure 10. 21 The letter C in the field of view 43

Figure 10. 22 The letter T in the field of view 44

Associative memory n n 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. 45

Figure 10. 23 An artificial neural network implementing an associative memory 1. 2. 3. 4. 5. 6. 7. 8. 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 46

Figure 10. 24 The steps leading to a stable configuration 1 -1 Two stable states 1. Perimeter stable state 2. (later stable state) 3. When we initialize the 4. Network with a least four 5. Adjacent units on the 6. Perimeter in their excited 7. states 47

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 48

Genetic algorithms n Simulate genetic processes to evolve algorithms n n 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. 49

Figure 10. 25 Crossing two poker-playing strategies 50

Figure 10. 26 Coding the topology of an artificial neural network 51

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

Figure 10. 27 A semantic net 53

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

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

Some issues raised by artificial intelligence n n n 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? 56