This set of Artificial Intelligence Questions and Answers for Campus interviews focuses on “Local Search Problems and Optimization Problems”.
1. 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.
2. 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.
3. A complete, local search algorithm always finds goal if one exists, an optimal algorithm always finds a global minimum/maximum. State whether True or False.
Explanation: An algorithm is complete if it finds a solution if exists and optimal if finds optimal goal (minimum or maximum)
4. _______________ Is an algorithm, a loop that continually moves in the direction of increasing value – that is uphill
a) Up-Hill Search
c) Hill algorithm
d) Reverse-Down-Hill search
Explanation: Refer the definition of Hill-Climbing approach.
5. Hill-Climbing algorithm terminates when,
a) Stopping criterion met
b) Global Min/Max is achieved
c) No neighbor has higher value
d) All of the mentioned
Explanation: When no neighbor is having higher value, algorithm terminates fetching local min/max.
6. One of the main cons of hill-climbing search is,
a) Terminates at local optimum & Does not find optimum solution
b) Terminates at global optimum & Does not find optimum solution
c) Does not find optimum solution & Fail to find a solution
d) Fail to find a solution
Explanation: Algorithm terminates at local optimum values, hence fails to find optimum solution.
7. Stochastic hill climbing chooses at random from among the uphill moves; the probability of selection can vary with the steepness of the uphil1 move.
Explanation: Refer to the definition of variants of hill-climbing search.
8. Hill 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
9. Hill-Climbing approach stuck for the following reasons
a) Local maxima
d) All of the mentioned
Explanation: Local maxima: a local maximum is a peak that is higher than each of its neighboring states, but lower than the global maximum. Ridges: Ridges result in a sequence of local maxima that is very difficult for greedy algorithms to navigate. Plateaux: a plateau is an area of the state space landscape where the evaluation function is flat.
10. ___________ 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
Explanation: Refer to the definition of Local Beam Search algorithm.
11. A genetic algorithm (or GA) is a variant of stochastic beam search in which successor states are generated by combining two parent states, rather than by modifying a single state.
Explanation: Stochastic beam search, analogous to stochastic hill climbing, helps to alleviate this problem. Instead of choosing the best k from the pool of candidate successors, stochastic beam search chooses k successors at random, with the probability of choosing a given successor being an increasing function of its value.
12. Mark two main features of Genetic Algorithm
a) Fitness function & Crossover techniques
b) Crossover techniques & Random mutation
c) Individuals among the population & Random mutation
d) Random mutation & Fitness function
Explanation: Fitness function helps choosing individuals from the population and Crossover techniques defines the offspring generated.
13. Searching using query on Internet is, use of ___________ type of agent
a) Offline agent
b) Online agent
c) Both Offline & Online agent
d) Goal Based & Online agent
Explanation: Refer to the definitions of both the type of agent.
Sanfoundry Global Education & Learning Series – Artificial Intelligence.