[amp_mcq option1=”Updating heuristic estimate” option2=”Reducing heuristic estimate” option3=”Eliminating heuristic estimate” option4=”None of the mentioned” correct=”option1″]
The correct answer is A. Updating heuristic estimate.
A heuristic estimate is a function that estimates the cost of reaching the goal from a given state. It is often used in search algorithms to guide the search towards promising states. However, if the heuristic estimate is inaccurate, it can lead the search to get stuck in a local minimum.
Updating the heuristic estimate can help to escape from local minima. This is because the updated heuristic estimate will be more accurate, and will therefore guide the search towards better states.
Reducing the heuristic estimate or eliminating it altogether will not help to escape from local minima. This is because the search will still be guided by the inaccurate heuristic estimate, and will therefore be more likely to get stuck in a local minimum.
Here is a more detailed explanation of each option:
- A. Updating heuristic estimate: This is the most effective method for escaping from local minima. The updated heuristic estimate will be more accurate, and will therefore guide the search towards better states.
- B. Reducing heuristic estimate: This will not help to escape from local minima. The search will still be guided by the inaccurate heuristic estimate, and will therefore be more likely to get stuck in a local minimum.
- C. Eliminating heuristic estimate: This will also not help to escape from local minima. The search will be completely blind, and will therefore be more likely to get stuck in a local minimum.
- D. None of the mentioned: This is not a correct option.