A decision tree represents complex rules as a series of simple conditions with sub-trees (branches) for further conditions depending on the outcome to the inital condition. Decision trees can be hand crafted (e.g. decison trees in IVR), but most commonly encountered in AI as the output of machine learning algorithms such as Quinlan's ID3 or C4.5, or as part of a random forest.
Used in Chap. 5: pages 67, 72; Chap. 6: pages 84, 85; Chap. 8: page 110; Chap. 9: pages 117, 118, 121, 122, 124; Chap. 10: page 135; Chap. 16: pages 247, 248; Chap. 18: pages 282, 283; Chap. 19: page 298; Chap. 21: pages 332, 334, 335, 336, 339, 340; Chap. 24: page 376
Also known as decision trees
A simple decision tree