** Answer: ** The easiest way to construct an example is to first think
of a selectional restriction. E.g. The subject of "eat" must be animate.
Then for the anaphoric ambiguity, construct a text in which "it" is
the subject of "eat" and it could refer either to an animate or an
inaminate precedent. E.g. "Yesterday, I saw a pigeon sitting on the grass.
It was eating a piece of bread." Here the anaphoric ambiguity of whether
"it" refers to the pigeon or the grass is resolved by the selectional
restriction that the subject of "it" must be animate. There are many
other possible answers, of course.

- A. Show how to use state space search to solve this problem. In
particular, you should specify (i) What is a state? (ii) What is an
operator? (iii) What is the start state? (iv) What is the objective
of the search?
**Answer:**i. A state is a tower.

ii. An operator is adding a block to the top of the tower.

iii. The start state is the empty tower.

iv. The objective of the search is to find the state

*furthest*from the start state. In that respect, it differs from the various state space searches we discussed in class. - B. What search technique would you use to solve this problem?
Give an upper bound on the required time and space.
**Answer:**Since we are looking for the deepest solution, the search technique to use is a depth-first search. The time is proportional to the number of states in the space. Now, there is one state at the root; N states at the 1st level; at most N(N-1) states at the second level at most N(N-1)(N-2) states at the third level ... at most N! states at the Nth level. Hence the number of states is bounded by

N!/(N-1)! + N!/(N-2)! + N!/(N-3)! + ... + N!/1! + N!/0! < e N! which is O(N!). The space required is proportional to the depth of the search space, which is N.

** Answer:** Y can be pruned if X <= 5.

**Answer:** (i) It must be (i) zero-sum; (ii) perfect knowledge;
(iii) deterministic; (iv) two-player; (v) discrete.

** Answer:**

Program: p(X) :- p(X). p(a). Query: ?- p(a).

- a. Activation levels are propagated from the inputs through the hidden layers to the outputs.
- b. Activation levels are propagated from the outputs through the hidden layers to the inputs.
- c. Weights on the links are modified based on messages propagated from input to output.
- d. Weights on the links are modified based on messages propagated from output to input.
- e. Connections in the network are modified, gradually shortening the path from input to output.
- f. Weights at the input level are compared to the weights at the output level, and modified to reduce the discrepancy.

** Answer:**
Task execution is carried out through process (a). Learning is
carried out through process (d).

** Answer:** When you wish to evaluate a learning program using
a fixed corpus of examples, it is common to divide the corpus into
a training set and a test set. The program is first
trained by running it on the training set. Then the learning module
is turned off, and the quality of the current state of the execution module
is tested by running it on the test set. In this way you guard against
the possibility of overlearning, in which the program simply learns
the examples in the corpus, rather than the underlying concept.

** Answer: ** If H in S gives a false positive, then it is deleted
from S. If it gives a false negative on example E, then H is replaced
by all minimal generalizations of H that include E.

** Answer.** (i) An abrupt change in the form of the texture can signal
a change of surface. (ii) Variance in the size of textural elements
are assumed to correspond to a change in the distance of the object.
(iii) A change in a textural element that compresses it in one dimension,
such as a change from a circle to an ellipse or from a square to a
rectangle or parallelogram, can correspond to a change in the orientation
of the surface from a head-on view to a sidelong view.