2.3 - Give specifics for percepts, actions, goals (and performance measure), and environment (note 2.5 for an example)

__Chapter 3__

For each of the six basic uninformed search strategies, describe a realistic search problem where that strategy would be more appropriate than the other 5, and state why.

__Chapter 4__

4.1 and 4.2 - Give at least one reasonable heuristic for each problem

__Chapter 6__

Review 6.2, do 6.3 (prove some directly using logic rules instead of a truth table) and 6.10

__Chapter 7__

7.2, 7.6 (just first paragraph), 7.10, and 7.14 (give a 1 page discussion)

__Chapter 9__

9.1, 9.4, 9.5, 9.8

__Chapter 11__

11.1, 11.4, 11.7a-c

__Chapter 14__

14.2, 3 (give an exact probability using the normalization technique)

__Chapter 15__

15.1

__Chapter 18__

18.3

Given the training set below and using the ID3 algorithm, show carefully (work out the proper numbers) which attribute would be the first attribute chosen to partition the training set. Use only information gain as the criteria.

Example/Year/GPA/Marital Status/Output

1 Fr Hi M N

2 So Lo S P

3 Se Med M P

4 So Hi M N

5 So Hi M N

6 Fr Med S P

7 Se Hi M N

8 Fr Lo S N

9 Se Lo S P

__Chapter 19__

19.1 (use threshold logic units)

Given a 3-input threshold unit (1 if > 0, else 0), plus a bias input, initial weights of 0, and the following training set, show the learning sequence followed until convergence (or a maximum of 3 epochs). Assume a learning rate of .1. Show your results in a tabular format with the following entries.

Pattern /Target /StartingWeight Vector/ Net/ Output/ Vector of weight changes

a)

1 0 0 -> 0

0 0 0 -> 0

0 1 1 -> 1

1 1 1 -> 1

b)

.2 -.3 .6 -> 1

.3 0 -.2 -> 0

-.5 -.1.1 -> 1

__Chapter 22__

22.7