CSE458 Bayesian Networks

Assignment 1, 2006

www.csse.monash.edu.au/courseware/cse458/Ass1.html

Due date: Monday 15th May, 12noon

This assignment is worth 30% of your final mark for this subject. I expect each student to spend no more than 10 hrs completing this assignment, though be warned that it is easy to spend more time playing around with your networks. The work you submit should be done on your own.

Bayesian Network Software

This assignment involves using the Netica (http://www.norsys.com) Bayesian network package to develop models for uncertain reasoning.

This package can be downloaded and installed from the Internet. Note that we have a Monash Educational Site License for Netica. When you first download and run a copy of Netica, you will be asked for a license password to run it in more than Demo mode. (Demo mode does not allow you to save files of more than 15 nodes). The password is available from the lecturer.

Note: All questions in the assignment sections below that require an answer, should be answered in a single separate README file.

PART A: Fred's LISP dilemma (10 marks)

Fred is debugging a LISP program. He just typed an expression to the LISP interpreter and now it will not respond to any further typing. He can't see the visual prompt that usually indicates the interpreter is waiting for further input. As far as Fred knows, there are only two situations that could cause the LISP interpreter to stop running: (1) there are problems with the computer hardware; (2) there is a bug in Fred's code. Fred is also running an editor in which he is writing and editing his LISP code; if the hardware is functioning properly, then the text editor should still be running. And if the editor is running, the editor's cursor should be flashing. Additional information is that the hardware is pretty reliable, and is OK about 99% of the time, whereas Fred's LISP code is often buggy, say 40\% of the time}
  1. Construct a Belief Network to represent and draw inferences about Fred's dilemma.
  2. Show the belief of each variable before adding any evidence, i.e., about the LISP visual prompt not being displayed (Save in A1). Which nodes in your network are d-separated when there is no evidence added?
  3. Add the evidence about the LISP visual prompt not being displayed. After doing belief updating on the network, what is Fred's belief that he has a bug in his code? (Save in A2)
  4. Suppose that Fred checks the screen and the editor's cursor is still flashing. What effect does this have on his belief that the LISP interpreter is misbehaving because of a bug in his code? (Save in A3) Explain the change in terms of diagnostic and predictive reasoning.
  5. With these two pieces of evidence in your network, which pairs of nodes are d-separated?

PART B: Model your own problem (10 marks)

Think of your own problem involving reasoning with evidence and uncertainty.
  1. Write down an English description of the problem (in README file), then model it using a Bayesian Network. Your network should have between 8 and 15 nodes, and it should be multiply connected (i.e. not a tree or a polytree).
  2. Show the beliefs for each node in the network before any evidence is added. (Save in B1)
  3. Which nodes are d-separated with no evidence added?
  4. Which nodes in your network would be considered evidence (or observation) nodes? Which might be considered the query nodes? (Obviously this depends on the domain and how you might use the network).
  5. For one of the possible query nodes, use Netica's sensitivity analysis function to determine which would be the most informative observation to make next. Cut and paste the Netica output information into your README file, before explaining your answer.
  6. Show how the beliefs change in a form of diagnostic reasoning when evidence about at least one of the domain variables is added (Save in B2). Which nodes are d-separated with this evidence added?
  7. Show how the beliefs change in a form of causal reasoning when evidence about at least one of the domain variables is added (Save in B3).
  8. Show how the beliefs change through "explaining away" when evidence particular combinations of evidence are added. (Save in B4).
  9. Show how the beliefs change when you change the priors of a node without parents (rather than adding evidence). (Save in B5)
  10. What is the complexity of the join-tree for this network?

PART C: Modelling decision making (10 marks)

For one of the problems represented in either Part A or Part B, extend the problem to be a decision problem. This means that you must decide on the possible actions to be presented in the decision node, and decide what the utility node should be a function of.
  1. Describe the extended problem and the new nodes, particularly the utility values, in the README file.
  2. Show the decision outcome (including the expected utilities) for at least 2 different evidence cases. Save networks (showing probabilities and expected utility for the decision node) in files C1 and C2.
  3. Show the decision outcome (including the expected utilities) for the same two evidence cases with a different utility function. cases. Save networks (showing probabilities and expected utility for the decision node) in files C3 and C4.

Submission

Please put your network files and your README file in a directory called CSE4548-Ass1-LoginId. The README file should also include your name and login Id. Submission can be via CD-ROM, floppy or email (tarred and gzipped).

Late Submission

Late submissions will be accepted but subject to a late penalty.