A Moving to another question will save this response. Question 12 All informed search algorithms are only as good as the heuristic function that estimates cost between the current state and a targeted goal state. Which of the following are true about heuristic functions: The effective branching factor, b*, is a very useful measure of heuristic quality and can be calculated in straightforward fashion for h(n)s in search spaces with discrete step costs. Inductive learning would probably not work well with the simple atomic state descriptions (the current board) generally used in straightforward implementation of typical board games. In a problem scenario where multiple admissible h(n)’s exist, a significant problem lies in choosing which one to use, since you can use only one h(n) for any given “run” or attempt at the problem. An effective branching factor as close as possible to 1 indicates a good h(n) because it essentially indicates that the search mostly follows a path straight from the start state to the goal. Using a pattern database would likely be an effective heuristic function for searching out navigational routes for my automated Uber taxi The reason that a relaxed problem formulation is able to suggest (specifically) admissible h(n)’s is because it essentially creates “shortcuts” (by “cheating”) in solution paths, thereby always underestimating actual solution cost.