Greedy search strategy chooses the node for expansion in………..
a) Shallowest
b) Deepest
c) The one closest to the goal node
d) Minimum heuristic cost
Explanation: Sometimes minimum heuristics can be used, sometimes maximum heuristics function can be used. It depends upon the application on which the algorithm is applied.
What is the evaluation function in greedy approach?
a) Heuristic function
b) Path cost from start node to current node
c) Path cost from start node to current node + Heuristic cost
d) Average of Path cost from start node to current node and Heuristic cost
Explanation: Greedy best-first search3 tries to expand the node that is closest to the goal, on the grounds that this is likely to lead to a solution quickly. Thus, it evaluates nodes by using just the heuristic function: f (n) = h(n).
Local Search Problems & Optimization Problems:
In many problems the path to goal is irrelevant, this class of problems can be solved using ……
a) Informed Search Techniques
b) Uninformed Search Techniques
c) Local Search Techniques
d) Informed & Uninformed Search Techniques
Explanation: If the path to the goal does not matter, we might consider a different class of algorithms, ones that do not worry about paths at all. Local search algorithms operate using a single current state (rather than multiple paths) and generally move only to neighbors of that state.
Though local search algorithms are not systematic, key advantages would include ……
a) Less memory b) More time
c) Finds a solution in large infinite space
d) Less memory & Finds a solution in large infinite space
Explanation: Two advantages: (1) they use very little memory-usually a constant amount; and (2) they can often find reasonable solutions in large or infinite (continuous) state spaces for which systematic algorithms are unsuitable.
A complete, local search algorithm always finds goal if one exists, an optimal algorithm always finds a global minimum/maximum.
a) True
b) False
Explanation: An algorithm is complete if it finds a solution if exists and optimal if finds optimal goal (minimum or maximum).
