1. Aspects of Artificial Intelligence Himadri Barman

2. Defining AI systems that learn new concepts and tasks systems that can reason and draw useful conclusions about the world around us systems that can understand a natural language or perceive and comprehend a visual scene systems that perform other types of feats that require human types of intelligence Artificial Intelligence (AI) is a branch of computer science concerned with the study and creation of computer systems that exhibit some form of intelligence. It means 2

3. Business and AI To store information in an active form as organizational memory, creating an organizational knowledge base that many employees can examine and preserving expertise that might be lost when an acknowledged expert leaves the firm To create a mechanism that is not subject to human feelings, such as fatigue and worry. This may be especially useful when jobs may be environmentally, physically or mentally dangerous to humans. These systems may also be useful advisers in times of crisis 3

4. To eliminate routine and unsatisfying jobs held by people To enhance the organization’s knowledge base by generating solutions to specific problems that are too massive and complex to be analyzed by human beings in a short period of time Attempt to solve problems with no apparent solution 4

5. Fuzzy Sets Classical Sets - binary membership (absolute membership - either yes or no) Fuzzy Sets - allows partial membership - mapping into the interval [0,1] 5

6. Fuzzy Logic A form of knowledge representation suitable for notions that cannot be defined precisely, but which depend upon their contexts. Fuzzy logic generalizes the notion of truth values in classical logic (i.e., true or false) into a matter of degree. A statement in fuzzy logic, thus may be partially true between 0 (false) and 1 (true). I've grayed to a considerable extent, but not entirely grayed may be represented by assigning a value (say 0.6) the logic grayed (Himadri) [Technically grayed will be called a predicate and Himadri will be an argument] 6

7. Fuzzy Logic Example The problem –Change the speed of a heater fan, based on the room temperature and humidity. A temperature control system has four settings –Cold, Cool, Warm, and Hot Humidity can be defined by: –Low, Medium, and High Using this we can define the fuzzy set. 7

8. Biological Neurons A neuron consists of a cell body, one axon, and many dendrites. Dendrites receive inputs from axons of other neurons via excitation or inhibition synapses. Synapses are narrow gaps through which Axons influence dendrites 8

9. Artificial Neural Networks An ANN typically consists of simulated neurons. Each link is associated with a weight. The nature and strength of one node’s influence on the other is determined by the weight Each node combines the separate influences received on its input links into an overall influence using an activation function (most often a threshold function is used corresponding to a threshold value) 9

10. An Artificial Neuron The artificial neuron (perceptron) receives inputs, processes inputs and delivers a single output. 10

11. ANN Example 11 Robert Ronald Romeo John James Jack 0.5 1.5-1.5 Acquaintances Siblings H1 H2 Hidden Node Output Not observable All other links weight is 1 Adapted from Winston, P H, Artificial Intelligence, 3 rd Edition, Pearson ANN recognizing siblings and acquaintances H3 H4

12. ANN Applications Tax form processing to identify tax fraud Enhancing auditing by finding irregularities Bankruptcy prediction Customer credit scoring Loan approvals Credit card approval and fraud detection 12

13. Financial prediction Energy forecasting Computer access security (intrusion detection and classification of attacks) Fraud detection in mobile telecommunication networks 13

14. Benefits of ANNs Usefulness for pattern recognition, classification, generalization, abstraction and interpretation of incomplete and noisy inputs. (e.g. handwriting recognition, image recognition, voice and speech recognition, weather forecasting). 14

15. Providing some human characteristics to problem solving that are difficult to simulate using the logical, analytical techniques of expert systems and standard software technologies. (e.g. financial applications). 15

16. Ability to solve new kinds of problems. ANNs are particularly effective at solving problems whose solutions are difficult, if not impossible, to define. This opened up a new range of decision support applications formerly either difficult or impossible to computerize. 16

17. Robustness. ANNs tend to be more robust than their conventional counterparts. They have the ability to cope with incomplete or fuzzy data. ANNs can be very tolerant of faults if properly implemented. 17

18. Fast processing speed. Because they consist of a large number of massively interconnected processing units, all operating in parallel on the same problem, ANNs can potentially operate at considerable speed (when implemented on parallel processors). 18

19. Flexibility and ease of maintenance. ANNs are very flexible in adapting their behavior to new and changing environments. They are also easier to maintain, with some having the ability to learn from experience to improve their own performance. 19

20. Limitations of ANN ANNs do not produce an explicit model even though new cases can be fed into it and new results obtained. ANNs lack explanation capabilities. Justifications for results is difficult to obtain because the connection weights usually do not have obvious interpretations. 20

21. Genetic Algorithms GAs (also called Adaptive Computations) refer to a variety of problem-solving techniques that are conceptually based on the method that living organisms use to adapt to their environments – the process of evolution. They are programmed to work the way populations solve problems – by changing and reorganizing their component parts using processes such as reproduction, mutation and natural selection. GAs breed programs that solve problems even when no person can fully understand their structure. 21

22. 1. Initialize: Randomly generate a population of N binary strings (chromosomes) where each string represents a possible solution 2. Evaluation: Assign a fitness value to each string which says how that solution is fit for the given problem 3. Selection: Select n strings on the basis of fitness 4.Perform Crossover operation 5.Do Evaluation and Selection 6.Apply Mutation operation in the population of strings 7.Repeat steps 2 to 6 till termination criteria satisfied 22

23. GA Example Objective Function: Y=x 2 – x ; x  (0,1, 2, …. 15) Maximize the objective function Total number of possible solutions or values of x = 16 = 2 4 So, a binary string of length l =4 is enough to represent a solution Initialization Let N = 4, randomly generate a population of 4 binary strings 0011 0101 1001 1000 23

24. GA Example Evaluation Assign a fitness value to each binary string No.String (x)XY=x 2 – xf = y / (∑y i ) 10010220.013 201015200.133 310019720.480 410008560.374 Total1501.000 Selection Higher the fitness values, the more chance of being selected. Most likely string 0010 won’t survive 24

25. GA Example Crossover Single point crossover 01010100 10001001 Double point crossover 11001011 11010011 10010001 10001001 Mutation 01010111 25