COSC-574: Automated Reasoning

Project 3
Fall 2019

Due: M 12/9 @ 11:59 PM
10 points

For this project, you will implement routines for exact and approximate inference for Bayesian networks. Begin by implementing routines for efficiently manipulating factors based on Koller and Friedman's product-factor algorithm. You will then use these routines to implement Russell and Norvig's variable elimination algorithm.

You must implement the following classes and methods:

Note that you will need to implement additional classes and methods, which you can infer from Russell and Norvig's and from Koller and Friedman's algorithms.

Evaluate your implementation three problems:

  1. Russell and Norvig's burglary network,
  2. two problems of your choosing from reputable sources with three or more nodes
Implement these three problems as three separate executables: Example1, Example2, and Example3, respectively. The main function for these classes can construct the network through direct assignment. For the burglary network, the main function should instantiate that Mary called and that John called, compute the probability of the alarm sounding, and print its distribution to the console. For the examples that you choose, you are free to select the query and evidence variables. In comments, describe the reasoning problem, the network, its source, and the query and evidence variables. For output, it is sufficient to print the probability distribution of the query variable. Include with your submission a transcript of your program's output on the three problems.

Extra Credit

Implement these routines as methods of the Network class and demonstrate their use on the burglary problem by implementing the classes ExtraCredit1 and ExtraCredit2. Include runs of these routines in the the transcript you submit with your project.

Instructions for Electronic Submission

In a file named HONOR, provide the following information:
Name
NetID

In accordance with the class policies and Georgetown's Honor Code,
I certify that, with the exceptions of the course materials and those
items noted below, I have neither given nor received any assistance
on this project.

When you are ready to submit your program for grading, create a zip file named submit.zip containing your source files, honor statement, and transcript by typing

$ zip submit.zip *.java transcript HONOR
Do not include these files in a subdirectory. Upload submit.zip to Autolab. You can submit your assignment to the compile check p2c seven times. You can submit your assignment p2 three times, and you will see the autograder's output for the drug-study problem that we discussed in class. After the deadline, I will use a different autograder to conduct a comprehensive evaluation.

Plan B

If Autolab is down, upload your zip file to Canvas.

Copyright © 2019 Mark Maloof. All Rights Reserved. This material may not be published, broadcast, rewritten, or redistributed.