Artificial-Intelligence
November 18, 2023NTA UGC NET JUNE-2023 Paper-2
November 18, 2023Artificial-Intelligence
| Question 9 |
Consider the following:
(a) Trapping at local maxima
(b) Reaching a plateau
(c) Traversal along the ridge.
Which of the following option represents shortcomings of the hill climbing algorithm?
(a) Trapping at local maxima
(b) Reaching a plateau
(c) Traversal along the ridge.
Which of the following option represents shortcomings of the hill climbing algorithm?
| (a) and (b) only | |
| (a) and (c) only | |
| (b) and (c) only | |
| (a), (b) and (c) |
Question 9 Explanation:
Hill climbing limitations:
1. Local Maxima: Hill-climbing algorithm reaching the vicinity a local maximum value, gets drawn towards the peak and gets stuck there, having no other place to go.
2. Ridges: These are sequences of local maxima, making it difficult for the algorithm to navigate.
3. Plateaux: This is a flat state-space region. As there is no uphill to go, algorithm often gets lost in the plateau.
To avoid above problems using 3 standard types of hill climbing algorithm is
1. Stochastic Hill Climbing selects at random from the uphill moves. The probability of selection varies with the steepness of the uphill move.
2. First-Choice Climbing implements the above one by generating successors randomly until a better one is found.
3. Random-restart hill climbing searches from randomly generated initial moves until the goal state is reached.
1. Local Maxima: Hill-climbing algorithm reaching the vicinity a local maximum value, gets drawn towards the peak and gets stuck there, having no other place to go.
2. Ridges: These are sequences of local maxima, making it difficult for the algorithm to navigate.
3. Plateaux: This is a flat state-space region. As there is no uphill to go, algorithm often gets lost in the plateau.
To avoid above problems using 3 standard types of hill climbing algorithm is
1. Stochastic Hill Climbing selects at random from the uphill moves. The probability of selection varies with the steepness of the uphill move.
2. First-Choice Climbing implements the above one by generating successors randomly until a better one is found.
3. Random-restart hill climbing searches from randomly generated initial moves until the goal state is reached.
Correct Answer: D
Question 9 Explanation:
Hill climbing limitations:
1. Local Maxima: Hill-climbing algorithm reaching the vicinity a local maximum value, gets drawn towards the peak and gets stuck there, having no other place to go.
2. Ridges: These are sequences of local maxima, making it difficult for the algorithm to navigate.
3. Plateaux: This is a flat state-space region. As there is no uphill to go, algorithm often gets lost in the plateau.
To avoid above problems using 3 standard types of hill climbing algorithm is
1. Stochastic Hill Climbing selects at random from the uphill moves. The probability of selection varies with the steepness of the uphill move.
2. First-Choice Climbing implements the above one by generating successors randomly until a better one is found.
3. Random-restart hill climbing searches from randomly generated initial moves until the goal state is reached.
1. Local Maxima: Hill-climbing algorithm reaching the vicinity a local maximum value, gets drawn towards the peak and gets stuck there, having no other place to go.
2. Ridges: These are sequences of local maxima, making it difficult for the algorithm to navigate.
3. Plateaux: This is a flat state-space region. As there is no uphill to go, algorithm often gets lost in the plateau.
To avoid above problems using 3 standard types of hill climbing algorithm is
1. Stochastic Hill Climbing selects at random from the uphill moves. The probability of selection varies with the steepness of the uphill move.
2. First-Choice Climbing implements the above one by generating successors randomly until a better one is found.
3. Random-restart hill climbing searches from randomly generated initial moves until the goal state is reached.
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