Final answer:
Susan's heuristic, which includes an additional Northeast direction, allows her to explore more of the problem space and is less likely to get trapped in local optima compared to Larry's heuristic. The thorough exploration increases her chances of finding a more optimal solution.
Step-by-step explanation:
The student is asking about heuristics in the context of problem-solving strategies in artificial intelligence and algorithms. Heuristics are often used in AI to make the problem-solving process faster and more efficient by providing a "rule of thumb" for making decisions.
Specifically, the question is about who will likely have fewer local optima when searching a problem space represented as a checkerboard, Larry with his heuristic that involves searching to the North, South, East, and West or Susan who adds the Northeast direction in her search pattern.
Susan's heuristic includes an additional direction (Northeast) compared to Larry's. This means Susan's approach covers a larger search area, considering more possibilities at each step.
By having an additional direction to search, Susan's heuristic is less likely to get trapped in local optima since it can explore the space more thoroughly.
On the other hand, Larry's heuristic may miss potential solutions or better paths because it only searches in four cardinal directions.
The extra coverage of angles in Susan's heuristic increases the likelihood of escaping local optima and finding a more optimal overall solution, assuming that the problem space is such that diagonal movements are meaningful and can lead to better solutions. Therefore, Susan will likely encounter fewer local optima in her search.