代做Semester留学生、Python/Java编程设计代写、代做c/c++，Java代写

日期：2019-02-26 09:24

2018-19Spring Semester Assignment #1

How to submit it: Submit your written answers as a pdf file. Submit your code for the last

question as a zip file

Penalties on late papers: 20% off each day (anytime after the due time is considered late by one day)

Problem1. (30%) Consider a 10x10 grid without any obstacles, and a robot with the same

specification as our boundary followingrobot: eight sensors and four actions.

Designa reactive production system tocontrolthe robot to go to one of the four corners,

wherever its initial position is.

Show that it is not possible to have areactive production system to make the robot visit

every cell in the grid.

Designa statemachineto achieve the above task. In addition to having memory about

previous actiontaken and previous sensory input, you can have internal variables

(mentalstates) and actions to change the values of these variables.

Problem2. (15%) Which boolean functiondoes the following TLUimplement? The TLU has five

inputs.Its weight vector is (1.1, 3.1, 1, 2, 0.5), andthe threshold is 1.

Problem3. (20%) Consider the problem of training a TLU to do logical disjunction (logical “or”)

using the error-correction procedure that we talked about in class. Notice that this operator

takes two inputs, but to apply the procedure, you need to add one more input whose value is

always 1, and use 0 as the threshold. Suppose that we start with the initial weightsall equal to 0,

and learning rate c = 1. Find aminimalset of traininginstances that will correctly train the TLU

according to the procedure. Here a training set is minimal if removing any instance in it will not

producea TLU for the logical disjunction. Please show the details of your work inlcuding the

converging sequence of the weights.

Problem4. (Programming) (35%) Design and implement a genetic programming system to

evolve some perceptronsthat match wellwith a given training set. A training set is a collection

of tuples of the form (x1, ...,xn, l),where xi’s arereal numbers and lis either 1 (positive example)

or 0 (negative example). So foryour genetic programming system, a“program”is just a tuple

(w1,...,wn,θ) of numbers (weights and the threshold).Answer the following questions:

1. What’s yourfitnessfunction?

2. What’s yourcrossover operator?

3. What’s yourcopy operator?

4. What’s yourmutation operator, if you use any?

5. What’s the size of the initial generation,and how are programs generated?

6. Whendo you stop theevolution? Evolve it upto a fixed iteration, when it satisfies a

condition on the fitness function, or a combination of the two?

7. What’s the output ofyour system for the training set in thenext page? Thistraining set

will beuploaded to canvas as acsv file.

In addition to answer these questions, please also upload your source code and executable (in any language and platform) as a zip archivefile and name it as YourStudentID.zip on canvas.