Artificial Intelligence: A Modern Approach
3rd Edition
ISBN: 9780136042594
Author: Stuart Russell, Peter Norvig
Publisher: Prentice Hall
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Expert Solution & Answer
Chapter 4, Problem 2E
Explanation of Solution
Formulating the problem:
- The question mentioned is raises many issues despite its humble origins as the scientifically important problem of protein design.
- There is a discrete assembly space in which the track is filled with pieces chose and a “joint angle” is used to determine the continuous configuration space at every place where two pieces are linked.
- Thus the user can define a state with a set of linked, oriented pieces and the associated joint angles in the range [10,0], and with a set of pieces that are unlinked.
- The joint angles and linkage exactly determines the physical layout of the track.
- The user can allow or disallow for layouts in which tracks are arranged like one above another.
- The evolution function includes terms for,
- Number of pieces used,
- Number of loose ends,
- The degree of overlap.
- The user might include a penalty for the amount of deviation, and the deviation can be from 0-degree joint angles...
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Students have asked these similar questions
Let l be a line in the x-yplane. If l is a vertical line, its equation is x = a for some real number a.
Suppose l is not a vertical line and its slope is m. Then the equation of l is y = mx + b, where b is the y-intercept.
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If (x₁, y₁) and (x₂, y₂) are two points in the x-y plane and x₁ ≠ x₂, the slope of line passing through these points is m = (y₂ - y₁)/(x₂ - x₁).
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Input x₁
Input y₁
Input x₂
Simulated annealing is an extension of hill climbing, which uses randomness to avoid getting stuck in local maxima and plateaux.
a) 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?
(b) 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.]
(c) Gradient ascent search is prone to local optima just like hill climbing.…
The language for coding must be in python
Neural Network Units
Implement a single sigmoid neural network unit with weights of [-1.2, -1.1, 3.3, -2.1]
Calculate the outputs for two training examples:Example 1: [0.9, 10.0, 3.1, 1]Example 2: [0.9, 2.1, 3.7, 1]
Note that you don't have to explicitly include a threshold or bias since the examples include a last element of 1 which means that the last weight effectively operates as a threshold.
Assuming that a sigmoid unit response >0.5 denotes a positive class and <0.5 is negative class, is example 1 positive or negative? is example 2 positive or negative?
Create a single ReLU unit and provide the outputs for those examples.
Calculate the derivative of the sigmoid with respect to net input for both examples
Calculate the derivative of the ReLU with respect to net input for both examples
Chapter 4 Solutions
Artificial Intelligence: A Modern Approach
Knowledge Booster
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