Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
3rd Edition
ISBN: 9780136042594
Author: Stuart Russell, Peter Norvig
Publisher: Prentice Hall
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Chapter 4, Problem 1E

Give the name of the algorithm that results from each of the following special cases:

  1. a. Local beam search with k = 1.
  2. b. Local beam search with one initial state and no limit on the number of states retained.
  3. c. Simulated annealing with T = 0 at all times (and omitting the termination test).
  4. d. Simulated annealing with T = ∞ at all times.
  5. e. Genetic algorithm with population size N = 1.
Expert Solution & Answer
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Explanation of Solution

a.

The local beam search with “k=1” is hill-climbing search.

b.

  • Local beam search with one initial state and no limit on the number of states retained resembles with Breadth-First search.
  • In breadth first search, before adding the next layer it adds one complete layer nodes.
  • Starting from one state, the algorithm would be essentially identical to breadth-first search except that each layer is generated all at once.

c.

Simulated annealing with “T=0” at all time:

  • There is a fact that termination step would be triggered immediately. Ignoring this fact, the search would be identical to first choice hill climbing.
  • This is because; every downward successor would be rejected with probability 1.

d.

Simulated annealing with “T = ∞” at all times is a random-walk search, it always accepts a new state.

e.

Generic algorithm with population size “N=1”:

  • The two selected parents will be same individual, if the population size is “1”.
  • The crossover yields an exact copy of individuals. Here, the mutation chance occurs.
  • Thus, the algorithm executes a random walk in the space of individuals.

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