## Description

Problem 1 (10 points): Trace the operation of A∗

search (the tree version) applied to the problem of getting to Bucharest

from Lugoj using the straight-line distance heuristic. That is, show the sequence of nodes that the algorithm will consider and the f, g, and h score for each node. You don’t need to draw the graph, just right down a sequence of

(city, f(city), g(city), h(city)) in the order in which the nodes are expanded.

Giurgiu

Urziceni

Hirsova

Eforie

Neamt

Oradea

Zerind

Arad

Timisoara

Lugoj

Mehadia

Drobeta

Craiova

Sibiu Fagaras

Pitesti

Vaslui

Iasi

Rimnicu Vilcea

Bucharest

71

75

118

111

70

75

120

151

140

99

80

97

101

211

138

146 85

90

98

142

92

87

86

Figure 1: A simplified road map of part of Romania indicating distances between different cities.

Section 3.5. Informed (Heuristic) Search Strategies 93

Urziceni

Neamt

Oradea

Zerind

Timisoara

Mehadia

Sibiu

Pitesti

Rimnicu Vilcea

Vaslui

Bucharest

Giurgiu

Hirsova

Eforie

Arad

Lugoj

Drobeta

Craiova

Fagaras

Iasi

0

160

242

161

77

151

366

244

226

176

241

253

329

80

199

380

234

374

100

193

Figure 3.22 Values of hSLD—straight-line distances to Bucharest.

expanding a node that is not on the solution path; hence, its search cost is minimal. It is

not optimal, however: the path via Sibiu and Fagaras to Bucharest is 32 kilometers longer

than the path through Rimnicu Vilcea and Pitesti. This shows why the algorithm is called

“greedy”—at each step it tries to get as close to the goal as it can.

Greedy best-first tree search is also incomplete even in a finite state space, much like

depth-first search. Consider the problem of getting from Iasi to Fagaras. The heuristic suggests that Neamt be expanded first because it is closest to Fagaras, but it is a dead end. The

solution is to go first to Vaslui—a step that is actually farther from the goal according to

the heuristic—and then to continue to Urziceni, Bucharest, and Fagaras. The algorithm will

never find this solution, however, because expanding Neamt puts Iasi back into the frontier,

Iasi is closer to Fagaras than Vaslui is, and so Iasi will be expanded again, leading to an infinite loop. (The graph search version is complete in finite spaces, but not in infinite ones.) The

worst-case time and space complexity for the tree version is O(bm), where m is the maximum

depth of the search space. With a good heuristic function, however, the complexity can be

reduced substantially. The amount of the reduction depends on the particular problem and on

the quality of the heuristic.

3.5.2 A* search: Minimizing the total estimated solution cost

The most widely known form of best-first search is called A∗ A search (pronounced “A-star ∗ SEARCH

search”). It evaluates nodes by combining g(n), the cost to reach the node, and h(n), the cost

to get from the node to the goal:

f(n) = g(n) + h(n) .

Since g(n) gives the path cost from the start node to node n, and h(n) is the estimated cost

of the cheapest path from n to the goal, we have

f(n) = estimated cost of the cheapest solution through n .

Thus, if we are trying to find the cheapest solution, a reasonable thing to try first is the

node with the lowest value of g(n) + h(n). It turns out that this strategy is more than just

reasonable: provided that the heuristic function h(n) satisfies certain conditions, A∗ search is

Figure 2: Straight-line distances to Bucharest

Problem 2 (10 points): Consider a state space where the start state is number 1 and each state k has two successors:

numbers 2k and 2k + 1.

(a) Suppose the goal state is 11. List the order in which states will be visited for breadthfirst search, depth-limited search

with limit 3, and iterative deepening search.

(b) How well would bidirectional search work on this problem? List the order in which states will be visited. What is the

branching factor in each direction of the bidirectional search?

Problem 3 (5 points): Which of the following statements are correct and which ones are wrong?

(a) Breadth-first search is a special case of uniform-cost search.

(b) Depth-first search is a special case of best-first tree search.

(c) Uniform-cost search is a special case of A∗

search.

(d) Depth-first graph search is guaranteed to return an optimal solution.

(e) Breadth-first graph search is guaranteed to return an optimal solution.

(f) Uniform-cost graph search is guaranteed to return an optimal solution.

(g) A∗ graph search is guaranteed to return an optimal solution if the heuristic is consistent.

(h) A∗ graph search is guaranteed to expand no more nodes than depth-first graph search if the heuristic is consistent.

(i) A∗ graph search is guaranteed to expand no more nodes than uniform-cost graph search if the heuristic is consistent.

Problem 4 (2 points): Iterative deepening is sometimes used as an alternative to breadth first search. Give one advantage

of iterative deepening over BFS, and give one disadvantage of iterative deepening as compared with BFS. Be concise and

specific.

Problem 5 (10 points): Prove that if a heuristic is consistent, it must be admissible. Construct an example of an admissible

heuristic that is not consistent. (Hint: you can draw a small graph of 3 nodes and write arbitrary cost and heuristic values

so that the heuristic is admissible but not consistent).

Problem 6 (3 points): In a Constraint Satisfaction Problem (CSP) search, explain why it is a good heuristic to choose the

variable that is most constrained but the value that is least constraining.

Problem 7 (10 points): Consider the following game tree, where the first move is made by the MAX player and the second

move is made by the MIN player.

(a) What is the best move for the MAX player using the minimax procedure?

(b) Perform a left-to-right (left branch first, then right branch) alpha-beta pruning on the tree. That is, draw only the parts

of the tree that are visited and don’t draw branches that are cut off (no need to show the alpha or beta values).

(c) Do the same thing as in the previous question, but with a right-to-left ordering of the actions. Discuss why different

pruning occurs.

Alpha-Beta Pruning

oblem #22

nsider the following game tree.

)Find the best move for the MAX player using the minimax proced)Perform a left-to-right alpha-beta pruning on the tree. Indicate wthe cutoffs occur.

)Perform a right-to-left alpha-beta pruning on the tree. Discuss whdifferent pruning occurs.

A

B C

D E F G H

I J K L

M N

3 5

0 7

5 7 8

4

MAX

Zoran Duric (CS Dept., GMU) Midterm Review 3 5/ 10 October 7, 2008Problem 8 (10 points): Which of the following are admissible, given admissible heuristics h1, h2? Which of the following

are consistent, given consistent heuristics h1, h2? Justify your answer.

a) h(n) = min{h1(n), h2(n)}

b) h(n) = wh1(n) + (1 − w)h2(n), where 0 ≤ w ≤ 1

c) h(n) = max{h1(n), h2(n)}

Which one of these three new heuristics (a, b or c) would you prefer to use for A*? Justify your answer.

Problem 9 (10 points): Simulated annealing is an extension of hill climbing, which uses randomness to avoid getting stuck

in local maxima and plateaux.

a) For what types of problems will hill climbing work better than simulated annealing? In other words, when is the

random part of simulated annealing not necessary?

b) For what types of problems will randomly guessing the state work just as well as simulated annealing? In other

words, when is the hill-climbing part of simulated annealing not necessary?

c) Reasoning from your answers to parts (a) and (b) above, for what types of problems is simulated annealing a useful

technique? In other terms, what assumptions about the shape of the value function are implicit in the design of

simulated annealing?

d) As defined in your textbook, simulated annealing returns the current state when the end of the annealing schedule is

reached and if the annealing schedule is slow enough. Given that we know the value (measure of goodness) of each

state we visit, is there anything smarter we could do?

(e) Simulated annealing requires a very small amount of memory, just enough to store two states: the current state and

the proposed next state. Suppose we had enough memory to hold two million states. Propose a modification to

simulated annealing that makes productive use of the additional memory. In particular, suggest something that will

likely perform better than just running simulated annealing a million times consecutively with random restarts. [Note:

There are multiple correct answers here.]

(f) Gradient ascent search is prone to local optima just like hill climbing. Describe how you might adapt randomness in

simulated annealing to gradient ascent search avoid trap of local maximum.