Center for Analysis and Design of Intelligent Agents

In this session we will look at some basic Bayesian Networks and you will solve two problems using a Bayesian Network simulator.

The following is a Java applet for Bayesian Network simulations:

• http://www.cs.cmu.edu/~javabayes/

You are writing a program to control a non-player character (NPC) in a game. The NPC is in a building full of doors. Behind each door, there is either a reward (e.g. health-points) or a monster which the NPC must fight with (losing health-points). Once the NPC opens a door, he must fight the monster behind it if any. However, before opening a door the NPC can stick its nose in the keyhole (it cannot look through it) and smell the air inside the room. The air will smell either bad or not. In summary:

• The NPC should seek reward but avoid monsters;
• The NPC doesn't know what's behind a door in advance, but…
• it can check whether the room smells bad or not and use that information as an indicator.

1. Design a very simple Bayesian network usable by the NPC to decide when in front of a door, whether he should open it or not. Start by inventing reasonable probabilities for the relation between the contents of the room and its smell.
2. Argue how, instead of using made-up probabilities, the NPC can learn as he opens doors and dynamically update the Bayesian network becoming smarter.

The details of the second problem will be given after you finish the first one, as it gives out hints about the solution of problem 1 :)