Category: Artificial Intelligence Objective Questions

Artificial Intelligence Multiple Choice Questions on “Frames”.

1. What is the frame?
a) A way of representing knowledge
b) Data Structure
c) Data Type
d) None of the mentioned
Answer: a
Clarification: None.

2. Frames in artificial intelligence is derived from semantic nets.
a) True
b) False
Answer: a
Clarification: A frame is an artificial intelligence data structure used to divide knowledge into substructures by representing “stereotyped situations.”.

3. Which of the following elements constitutes the frame structure?
a) Facts or Data
b) Procedures and default values
c) Frame names
d) Frame reference in hierarchy
Answer: a
Clarification: None.

4. Like semantic networks, frames can be queried using spreading activation.
a) True
b) False
Answer: a
Clarification: None.

5. What is Hyponymy relation?
a) A is part of B
b) B has A as a part of itself
c) A is subordinate of B
d) A is superordinate of B
Answer: c
Clarification: In linguistics, a hyponym is a word or phrase whose semantic field is included within that of another word, its hypernym (sometimes spelled hypernym outside of the natural language processing community). In simpler terms, a hyponym shares a type-of relationship with its hypernym.

6. The basic inference mechanism in semantic network in which knowledge is represented as Frames is to follow the links between the nodes.
a) True
b) False
Answer: a
Clarification: None.

7. There exists two way to infer using semantic networks in which knowledge is represented as Frames.
1) Intersection Search
2) Inheritance Search
a) True
b) False
Answer: a
Clarification: None.

Artificial Intelligence Questions and Answers for Campus interviews 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
Answer: c
Clarification: 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
Answer: d
Clarification: 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.
a) True
b) False
Answer: a
Clarification: 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
b) Hill-Climbing
c) Hill algorithm
d) Reverse-Down-Hill search
Answer: b
Clarification: Refer the definition of Hill-Climbing approach.

5. When will Hill-Climbing algorithm terminate?
a) Stopping criterion met
b) Global Min/Max is achieved
c) No neighbor has higher value
d) All of the mentioned
Answer: c
Clarification: When no neighbor is having higher value, algorithm terminates fetching local min/max.

6. What are the main cons of hill-climbing search?
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
Answer: a
Clarification: 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.
a) True
b) False
Answer: a
Clarification: 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
Answer: c
Clarification: None.

9. Hill-Climbing approach stuck for which of the following reasons?
a) Local maxima
b) Ridges
c) Plateaux
d) All of the mentioned
Answer: d
Clarification: 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
Answer: b
Clarification: 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.
a) True
b) False
Answer: a
Clarification: 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. What are the 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
Answer: a
Clarification: 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
Answer: d
Clarification: Refer to the definitions of both the type of agent.

Global Education & Learning Series – Artificial Intelligence.

Artificial Intelligence Multiple Choice Questions on “Artificial Intelligence Agents”.

1. The performance of an agent can be improved by __________
a) Learning
b) Observing
c) Perceiving
d) None of the mentioned
Answer: a
Clarification: An agent can improve by saving the previous states on which it was earlier, hence in future it can learn to respond in the same situation better.

2. External actions of the agent is selected by __________
a) Perceive
b) Performance
c) Learning
d) Actuator
Answer: b
Clarification: It depends on how you want to improve and what the performance measures are.

3. The action of the Simple reflex agent completely depends upon __________
a) Perception history
b) Current perception
c) Learning theory
d) Utility functions
Answer: b
Clarification: These agents select actions based on the current perception, ignoring the rest of the perception history.

4. Which of the following could be the approaches to Artificial Intelligence?
a) Strong Artificial Intelligence
b) Weak Artificial Intelligence
c) Applied Artificial Intelligence
d) All of the mentioned
Answer: d
Clarification: Strong Artificial Intelligence aims to build machines that can truly reason and solve problems.
Weak Artificial Intelligence deals with the creation of some form of computer-based artificial intelligence that cannot truly reason and solve problems, but can act as if it were intelligent.
Applied Artificial Intelligence aims to produce commercially viable “smart” systems.
In the Cognitive Artificial Intelligence approach, a computer is used to test theories about how the human mind works.

5. An Artificial Neural Network Is based on?
a) Strong Artificial Intelligence approach
b) Weak Artificial Intelligence approach
c) Cognitive Artificial Intelligence approach
d) Applied Artificial Intelligence approach
Answer: c
Clarification: In the Cognitive Artificial Intelligence approach, a computer is used to test theories about how the human mind works, for example, theories about how we recognize faces and other objects, or about how we solve abstract problems.

6. The Face Recognition system is based on?
a) Strong Artificial Intelligence approach
b) Weak Artificial Intelligence approach
c) Cognitive Artificial Intelligence approach
d) Applied Artificial Intelligence approach
Answer: d
Clarification: Applied Artificial Intelligence approach aims to produce commercially viable “smart” systems such as, for example, a security system that is able to recognize the faces of people who permitted to enter a particular building. Applied Artificial Intelligence has already enjoyed considerable success.

7. A completely automated chess engine (Learn from previous games) is based on?
a) Strong Artificial Intelligence approach
b) Weak Artificial Intelligence approach
c) Cognitive Artificial Intelligence approach
d) Applied Artificial Intelligence approach
Answer: a
Clarification: Strong Artificial Intelligence aims to build machines that can truly reason and solve problems. These machines must be self-aware and their overall intellectual ability needs to be indistinguishable from that of a human being. Strong Artificial Intelligence maintains that suitably programmed machines are capable of cognitive mental states.

8. A basic line following robot is based on __________
a) Strong Artificial Intelligence approach
b) Weak Artificial Intelligence approach
c) Cognitive Artificial Intelligence approach
d) Applied Artificial Intelligence approach
Answer: b
Clarification: Weak Artificial Intelligence deals with the creation of some form of computer-based artificial intelligence that cannot truly reason and solve problems, but can act as if it were intelligent. Weak Artificial Intelligence holds that suitably programmed machines can simulate human cognition.

9. Which of the following task/tasks Artificial Intelligence could not do yet?
a) Understand natural language robustly
b) Web mining
c) Construction of plans in real time dynamic systems
d) All of the mentioned
Answer: d
Clarification: These are the areas in which need more focus for improvements.

10. What among the following is/are the example of the intelligent agent/agents?
a) Human
b) Robot
c) Autonomous Spacecraft
d) All of the mentioned
Answer: d
Clarification: Humans can be looked upon as agents. They have eyes, ears, skin, taste buds, etc. for sensors; and hands, fingers, legs, mouth for effectors. Robots are agents. Robots may have camera, sonar, infrared, bumper, etc. for sensors. They can have grippers, wheels, lights, speakers, etc. for actuators. Autonomous Spacecraft takes decision on its own based on perceptions.

Artificial Intelligence (AI) Question Bank on “Semantic Net – 2”.

1. Which of the following is an extension of the semantic network?
a) Expert Systems
b) Rule Based Expert Systems
c) Decision Tree Based networks
d) Partitioned Networks
Answer: d
Clarification: None.

2. Basic idea of an partitioned nets is to break network into spaces which consist of groups of nodes and arcs and regard each space as a node.
a) True
b) False
Answer: a
Clarification: None.

3. Semantic Network represents _____________
a) Syntactic relation between concepts
b) Semantic relations between concepts
c) All of the mentioned
d) None of the mentioned
Answer: b
Clarification: None.

4. A semantic network is used when one has knowledge that is best understood as a set of concepts that are related to one another.
a) True
b) False
Answer: a
Clarification: None.

5. What are the limitations of the semantic networks?
a) Intractability
b) Lack in expressing some of the properties
c) Incomplete
d) Has memory constraints
Answer: b
Clarification: None.

6. What among the following is/are the best example of semantic networks?
a) Wordnet
b) Human Food Chain
c) MYSIN
d) Autonomous car driver
Answer: a
Clarification: Wordnet is a lexical database of English.

7. Semantic Network is also known as Frame networks.
a) True
b) False
Answer: a
Clarification: None.

8. What is Synonymy relation?
a) A is part of B
b) A denotes same as B
c) A is a kind of B
d) A is superordinate of B
Answer: b
Clarification: None.

9. What is Antonymy relation?
a) A is part of B
b) B has A as a part of itself
c) A denotes opposite of B
d) A is superordinate of B
Answer: c
Clarification: None.

10. Most semantic networks are not cognitive based.
a) True
b) False
Answer: b
Clarification: None.

Artificial Intelligence (AI) Questions and Answers for Aptitude test on “Informed Search and Exploration”.

1. A heuristic is a way of trying ___________
a) To discover something or an idea embedded in a program
b) To search and measure how far a node in a search tree seems to be from a goal
c) To compare two nodes in a search tree to see if one is better than another
d) All of the mentioned
Answer: d
Clarification: In a heuristic approach, we discover certain idea and use heuristic functions to search for a goal and predicates to compare nodes.

2. A* algorithm is based on ___________
a) Breadth-First-Search
b) Depth-First –Search
c) Best-First-Search
d) Hill climbing
Answer: c
Clarification: Best-first-search is giving the idea of optimization and quick choose of path, and all these characteristic lies in A* algorithm.

3. The search strategy the uses a problem specific knowledge is known as ___________
a) Informed Search
b) Best First Search
c) Heuristic Search
d) All of the mentioned
Answer: d
Clarification: The problem specific knowledge is also known as Heuristics and Best-First search uses some heuristic to choose the best node for expansion.

4. Uninformed search strategies are better than informed search strategies.
a) True
b) False
Answer: a
Clarification: Informed search strategies uses some problem specific knowledge, hence more efficient to finding goals.

5. Best-First search is a type of informed search, which uses ________________ to choose the best next node for expansion.
a) Evaluation function returning lowest evaluation
b) Evaluation function returning highest evaluation
c) Evaluation function returning lowest & highest evaluation
d) None of them is applicable
Answer: a
Clarification: Best-first search is an instance of the general TREE-SEARCH or GRAPH-SEARCH algorithm in which a node is selected for expansion based on an evaluation function, f (n). Traditionally, the node with the lowest evaluation is selected for expansion, because the evaluation measures distance to the goal.

6. Best-First search can be implemented using the following data structure.
a) Queue
b) Stack
c) Priority Queue
d) Circular Queue
Answer: c
Clarification: Best-first search can be implemented within our general search framework via a priority queue, a data structure that will maintain the fringe in ascending order of f-values.

7. The name “best-first search” is a venerable but inaccurate one. After all, if we could really expand the best node first, it would not be a search at all; it would be a straight march to the goal. All we can do is choose the node that appears to be best according to the evaluation function.
a) True
b) False
Answer: a
Clarification: If the evaluation function is exactly accurate, then this will indeed be the best node; in reality, the evaluation function will sometimes be off, and can lead the search astray.

8. Heuristic function h(n) is ________
a) Lowest path cost
b) Cheapest path from root to goal node
c) Estimated cost of cheapest path from root to goal node
d) Average path cost
Answer: c
Clarification: Heuristic is an estimated cost.

9. 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
Answer: c
Clarification: Sometimes minimum heuristics can be used, sometimes maximum heuristics function can be used. It depends upon the application on which the algorithm is applied.

10. 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
Answer: a
Clarification: 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).

11. What is the space complexity of Greedy search?
a) O(b)
b) O(bl)
c) O(m)
d) O(bm)
Answer: d
Clarification: O(bm) is the space complexity where b is the branching factor and m is the maximum depth of the search tree. Since this algorithm resembles the DFS.

12. What is the evaluation function in A* 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
Answer: c
Clarification: The most widely-known form of best-first search is called A* search. It evaluates nodes by combining g(n), the cost to reach the node, and h(n.), the cost to get from the node to the goal: f(n) = g(n) + h(n). Since g(n) gives the path cost from the start node to node n, and h(n) is the estimated cost of the cheapest path from n to the goal.

13. A* is optimal if h(n) is an admissible heuristic-that is, provided that h(n) never underestimates the cost to reach the goal.
a) True
b) False
Answer: a
Clarification: A* is optimal if h(n) is an admissible heuristic-that is, provided that h(n) never overestimates the cost to reach the goal. Refer both the example from the book for better understanding of the algorithms.

Global Education & Learning Series – Artificial Intelligence.