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Robot’s personality neural networks

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Law of Robots_Legal personality

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Robot’s personality neural networks

  1. 1. Robot’s Personality Dr. Irene Kafeza Assistant Professor NALSAR, University of Law Hyderabad, India
  2. 2. Data mining • Data mining is the process of automatically discovering useful information in large data repositories. • It helps to find novel and useful patterns in the data • Predict the outcome of a future observation • For example, with data mining we can predict if a newly arrived customer will spend more than 100USD at a department store • Data mining is based on knowledge discovery in databases IRENE KAFEZA
  3. 3. Approaches in learning algorithms • Classification: takes as input a collection of records (instance, example) and maps each record to a predefined class label Classification model Attribute set x Class label y Input Output • Classification: Predicts a certain outcome based on a given input • Next slide shows what features define a vertebrate as a mammal, reptile, bird, fish or amphibian IRENE KAFEZA
  4. 4. Example IRENE KAFEZA
  5. 5. • Suppose that we are given the following characteristics of a creature called gila monster: • We can use classification based on the data of the previous slide to determine in which class it belongs. IRENE KAFEZA
  6. 6. How to solve a classification problem • Use a learning algorithm to create a model that best fits the relationship between the attribute set and the class label of the input data. • Create a training set consisting of records whose labels are known • Use s test set to measure the accuracy of your model IRENE KAFEZA
  7. 7. Artificial Neural Networks • Inspired by attempts to simulate biological neural systems • The human brain consists of nerve cells called neurons • Neurons are linked together with other neurons via strands of fiber called axons. • Axons are used to transmit nerve impulses from one neuron to another via dendrites which are extensions from the cell body of the neuron. • The contact point between a dendrite and an axon is called synapse. • Neurologists have discovered that the human brain learns by changing the strength of the synaptic connection between neurons upon repeated stimulation by the same impulse. IRENE KAFEZA
  8. 8. • The Perceptron model • An artificial neural network (ANN) is composed of nodes and directed links IRENE KAFEZA
  9. 9. • The Perceptron model • nodes are the neurons and the links represent the strength of synaptic connection between the neurons. • As in a biological neural system training a perceptron model means to adapt the weights of the links until they fit the input output relationships of the underlying data • In the specific example the output is • 1 if 0.3*x1+0.3*x2+0.3*x3-0.4>0 and it is • -1 if 0.3*x1+0.3*x2+0.3*x3-0.4<0 • The weight at the arcs is 0.3 and 0.4 is a bias factor. IRENE KAFEZA
  10. 10. • In this example we can see how the data of the given set are divided in two sets. The line is the decision boundary that was decided by applying the perceptron learning algorithm to the data set. IRENE KAFEZA
  11. 11. IRENE KAFEZA
  12. 12. Perceptron learning algorithm • The algorithm maintains a “guess” at good parameters (weights and bias) as it runs. • It processes one example at a time. • For a given example, it makes a prediction. • It checks to see if this prediction is correct (recall that this is training data, so we have access to true labels). • If the prediction is correct, it does nothing. • Only when the prediction is incorrect does it change its parameters, and it changes them in such a way that it would do better on this example next time around. • It then goes on to the next example. Once it hits the last example in the training set, it loops back around for a specified number of iterations IRENE KAFEZA

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