Here is an example that provides a comparison between influence diagrams and decision trees.

a. Suppose you are planning a party, and your objective is to have an enjoyable party for all the guests. An outdoor barbecue would be the best, but only if the sun shines; rain would ruin the barbecue. On the other hand, you could plan an indoor party. This would be a good party, not as nice as an outdoor barbecue in the sunshine but better than a barbecue in the rain. Of course, it is always possible to forego the party altogether! Construct an influence diagram and a decision tree using PrecisionTree for this problem.

b. You will, naturally, consult the weather forecast, which will tell you that the weather will be either “sunny” or “rainy.” The forecast is not perfect, however. If the forecast is “sunny,” then sunshine is more likely than rain, but there still is a small chance that it will rain. A forecast of “rainy” implies that rain is likely, but the sunmay still shine. Now draw an influence diagram using PrecisionTree for the decision, including the weather forecast. (There should befour nodes in your diagram, including one for the forecast, which will be available at the time you decide what kind of party to have, and one for the actual weather. Which direction should the arrow point between these two nodes? Why?) Now draw a decision tree for this problem. Recall that the events and decisions in a decision tree should be in chronological order.