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Hill-Climbing algorithm terminates when, a) Stopping criterion met b) Global Min/Max is achieved c) No neighbor has higher value d) Local Min/Max is achieved
4170One of the main cons of hill-climbing search is, a) Terminates at local optimum b) Terminates at global optimum c) Does not find optimum solution d) Fail to find a solution
2102Stochastic hill climbing chooses at random from among the uphill moves; the probability of selection can vary with the steepness of the uphil1 move. a) True b) False
1909Hill climbing sometimes called ____________ because it grabs a good neighbor state without thinking ahead about where to go next. a) Needy local search b) Heuristic local search c) Greedy local search d) Optimal local search
1 3529Hill-Climbing approach stuck for the following reasons a) Local maxima b) Ridges c) Plateaux d) All of above
3874___________ algorithm keeps track of k states rather than just one. a) Hill-Climbing search b) Local Beam search c) Stochastic hill-climbing search d) Random restart hill-climbing search
3706_________________ are mathematical problems defined as a set of objects whose state must satisfy a number of constraints or limitations. a) Constraints Satisfaction Problems b) Uninformed Search Problems c) Local Search Problems d) Only a) and b)
2071Which of the Following problems can be modeled as CSP? a) 8-Puzzle problem b) 8-Queen problem c) Map coloring problem d) Sudoku
3211What among the following constitutes to the incremental formulation of CSP? a) Path cost b) Goal cost c) Successor function d) Objective function e) Initial state
2162The term ___________ is used for a depth-first search that chooses values for one variable at a time and returns when a variable has no legal values left to assign. a) Forward search b) Backtrack search c) Hill algorithm d) Reverse-Down-Hill search
1 3868To overcome the need to backtrack in constraint satisfaction problem can be eliminated by a) Forward Searching b) Constraint Propagation c) Backtrack after a forward search d) Omitting the constraints and focusing only on goals
2202To overcome the need to backtrack in constraint satisfaction problem can be eliminated by a) Forward Searching b) Constraint Propagation c) Backtrack after a forward search d) Omitting the constraints and focusing only on goals
1 2743The BACKTRACKING-SEARCH algorithm in Figure 5.3 has a very simple policy for what to do when a branch of the search fails: back up to the preceding variable and try a different value for it. This is called chronological-backtracking. It is also possible to go all the way to set of variable that caused failure. State whether True or False. a) True b) False
1926Consider a problem of preparing a schedule for a class of student. This problem is a type of a) Search Problem b) Backtrack Problem c) CSP d) Planning Problem
3703Constraint satisfaction problems on finite domains are typically solved using a form of ___________. a) Search Algorithms b) Heuristic Search Algorithms c) Greedy Search Algorithms d) DFS/BFS Search Algorithms
2296
Which search agent operates by interleaving computation and action? a) Offline search b) Online search c) Breadth-first search d) Depth-first search
How does ill-conditioning affect nn training?
Explain the tensorflow?
Is it better to learn python or r?
Explain the goal of artificial intelligence?
What are good programming languages for artificial intelligence?
What prior knowledge is required to become data scientist?
What is the rule of simple reflex agent? a) Simple-action rule b) Condition-action rule c) Both a & b d) None of the mentioned
_________________ are mathematical problems defined as a set of objects whose state must satisfy a number of constraints or limitations. a) Constraints Satisfaction Problems b) Uninformed Search Problems c) Local Search Problems d) Only a) and b)
Differentiate between regression and classification algorithms?
Have you used sampling? What are the various types of sampling have you worked with?
Explain what is not cluster analysis?
How do I become an expert in machine learning?
What are your favorite use cases of machine learning models?
You have two sorted array of integers, write a program to find a number from each array such that the sum of the two numbers is closest to an integer i.
