Comprehensive questions and detailed answers for Unit III: Problem Solving by Searching (9 Hrs.). Perfect for exam preparation and concept clarity.
How is informed search different from uninformed search? Create a state space with appropriate heuristics, now illustrate how hill climbing search expands nodes to reach a goal. Modify the state space heuristics and demonstrate when the hill climbing will not be complete.
How uniform cost search is used to search goal in the state apace ? Illustrate with example.
How is minmax algorithm used in game search? Consider state space is defined by a collection of pairs like (A, B) representing paths between states A and B. Construct state space for following and use a minmax algorithm
(A, B), (A, C), (B, D), (D, E), (C, F), (C, G), (D, H), (D, I), (E, J), (F, K), (F, L), (G, M), (G, N).
The utilities for states H, I, J, K, L, M, N are 1, 3, 2, 6, 3, 4, 1 respectively.
Define state space graph. Differenciate between A* search and greedy best first search.
What is game search? How minmax search used in game playing ? Illustrate with an example.
What is constraint satisfaction problem? Illustrate graph coloring problem as constraint satisfaction problem.
Define admissible heuristic with an example. Explain the working mechanism and limitations of hill climbing search.
How do you define problem? What are criteria for defining problem? Compare Constraint Satisfaction Problem and Real World Problem in detail with appropriate example.
What is state space representation? Illustrate with one example.
Define game. Write the benefits and limitations of depth limited search.
How informed search are different than uniformed? Given following stae space, illustrate how depth limited search and iterative depending dearch works? Use your own assumption for depth search.
Hence, A is start and K is goal.
Consider following facts
Every traffic chases driver. Every driver who horns is smart. No traffic catches any smart driver. Any traffic who chases some driver but does not catch him frusted. Now configure FoPL knowledge base for above statements. Use resolution algorithm to draw a conclusion that “If all drivers horn, then all traffics are frusted.”
Given following search space, determine if these exists any alpha and beta cutoffs.
Construct a state space with appropriate heuristics and local costs. Show that Greedy Best First search is not complete for the state space. Also illustrate A* is complete and guarantees solution for the same state space.
Illustrate with an example, how uniform cost search algorithm can be used for finding goal in a state space.
Define state space graph. Differentiate between A* search and greedy best first search.
What is game search? How Min-Max search is used in game playing? Illustrate with an example.
What is constraint satisfaction problem? Illustrate graph coloring problem as constraint satisfaction problem.
And more questions available on this page.