Description
In this assignment, we’ll focus on Ch. 16 Decision Networks and ML algorithms.
Section 1: DECISION NETWORKS [100 points]
A decision network (Chapter 16, AIMA section 16.5) uses a Directed Acyclic Graph (DAG) to represent
a set of random variables and their conditional dependencies within a probabilistic model, while a
decision network extends the Bayesian network to include decision nodes and utility nodes. As in the
example given in Figure 1, there are three types of nodes: Rectangles represent decision nodes, Ovals
represent chance nodes, and Diamonds represent utility nodes. In this assignment, you will write code
to perform inference in Decision Networks of discrete variables.
You will be given a decision network, which may have several decision nodes, several chance nodes,
and at most one utility node. You will be asked to answer queries using the given network:
Calculate the expected utility of a particular decision, or determine the decision with the maximum
expected utility, given the joint probabilities.
Files provided:
• Assignment Notes for Decision Networks – which gives a deep insight of this particular problem
• Question.py
• TestCases folder which contains the input files.
Submission Checklist:
• Output files for all the test cases
• Decision_network_soln.py
