Que- For general graph, how one can get rid of repeated states?
a. By maintaining a list of visited vertices
b. By maintaining a list of traversed edges
c. By maintaining a list of non-visited vertices
d. By maintaining a list of non-traversed edges
Ans- By maintaining a list of visited vertices
Que- DFS is ______ efficient and BFS is __________ efficient.
a. Space, Time
b. Time, Space
c. Time, Time
d. Space, Space
Ans- Space, Time
Que- The main idea of Bidirectional search is to reduce the time complexity by searching two way simultaneously from start node and another from goal node.
a. TRUE
b. False
c. Nothing can be said
d. None of the mentioned
Ans- TRUE
Que- What is the other name of informed search strategy?
a. Simple search
b. Heuristic search
c. Online search
d. None of the mentioned
Ans- Heuristic search
Que- How many types of informed search method are in artificial intelligence?
a. 1
b. 2
c. 3
d. 4
Ans- 4
Que- Which search uses the problem specific knowledge beyond the definition of the problem?
a. Informed search
b. Depth-first search
c. Breadth-first search
d. Uninformed search
Ans- Informed search
Que- Which function will select the lowest expansion node at first for evaluation?
a. Greedy best-first search
b. Best-first search
c. Depth-first search
d. None of the mentioned
Ans- Best-first search
Que- What is the heuristic function of greedy best-first search?
a. f(n) != h(n)
b. f(n) < h(n)
c. f(n) = h(n)
d. f(n) > h(n)
Ans- f(n) = h(n)
Que- Which search uses only the linear space for searching?
a. Best-first search
b. Recursive best-first search
c. Depth-first search
d. None of the mentioned
Ans- Recursive best-first search
Que- Which method is used to search better by learning?
a. Best-first search
b. Depth-first search
c. Metalevel state space
d. None of the mentioned
Ans- Metalevel state space
Que- Which search is complete and optimal when h(n) is consistent?
a. Best-first search
b. Depth-first search
c. Both Best-first & Depth-first search
d. A* search
Ans- A* search
Que- Which is used to improve the performance of heuristic search?
a. Quality of nodes
b. Quality of heuristic function
c. Simple form of nodes
d. None of the mentioned
Ans- Quality of heuristic function
Que- Which search method will expand the node that is closest to the goal?
a. Best-first search
b. Greedy best-first search
c. A* search
d. None of the mentioned
Ans- Greedy best-first search
Que- 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
Ans- All of the mentioned
Que- A* algorithm is based on
a. Breadth-First-Search
b. Depth-First -Search
c. Best-First-Search
d. Hill climbing
Ans- Best-First-Search
Que- 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
Ans- All of the mentioned
Que- Uninformed search strategies are better than informed search strategies.
a. TRUE
b. False
c. Nothing can be said
d. None of the mentioned
Ans- TRUE
Que- 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
Ans- Evaluation function returning lowest evaluation
Que- Best-First search can be implemented using the following data structure.
a. Queue
b. Stack
c. Priority Queue
d. Circular Queue
Ans- Priority Queue
Que- 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. State whether true or false.
a. TRUE
b. False
c. Nothing can be said
d. None of the mentioned
Ans- TRUE
Que- 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
Ans- Estimated cost of cheapest path from root to goal node
Que- Greedy search strategy chooses the node for expansion
a. Shallowest
b. Deepest
c. The one closest to the goal node
d. Minimum heuristic cost
Ans- The one closest to the goal node
Que- In greedy approach evaluation function is
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
Ans- Heuristic function
Que- What is the space complexity of Greedy search?
a. O(b)
b. O(bl)
c. O(m)
d. O(bm)
Ans- O(bm)
Que- In A* approach evaluation function is
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
Ans- Path cost from start node to current node + Heuristic cost
Que- 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
c. Nothing can be said
d. None of the mentioned
Ans- TRUE
Que- 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
Ans- Local Search Techniques
Que- 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
Ans- Less memory & Finds a solution in large infinite space
Que- 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.
a. TRUE
b. False
c. Nothing can be said
d. None of the mentioned
Ans- TRUE
Que- _______________ 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
Ans- Hill-Climbing
Que- 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
Ans- No neighbor has higher value
Que- 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
Ans- Terminates at local optimum & Does not find optimum solution
Que- 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
c. Nothing can be said
d. None of the mentioned
Ans- TRUE
Que- 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
Ans- Greedy local search
Que- Hill-Climbing approach stuck for the following reasons
a. Local maxima
b. Ridges
c. Plateaux
d. All of the mentioned
Ans- All of the mentioned
Que- ___________ 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
Ans- Local Beam search
Que- 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
c. Nothing can be said
d. None of the mentioned
Ans- TRUE
Que- 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
Ans- Fitness function & Crossover techniques
Que- 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
Ans- Goal Based & Online agent
Que- _________________ 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. All of the mentioned
Ans- Constraints Satisfaction Problems
Que- Which of the Following problems can be modeled as CSP?
a. 8-Puzzle problem
b. 8-Queen problem
c. Map coloring problem
d. All of the mentioned
Ans- All of the mentioned
Que- What among the following constitutes to the incremental formulation of CSP?
a. Path cost
b. Goal cost
c. Successor function
d. All of the mentioned
Ans- All of the mentioned
Que- The 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
Ans- Backtrack search
Que- To 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
Ans- Forward Searching
Que- The 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
c. Nothing can be said
d. None of the mentioned
Ans- TRUE
Que- Consider 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
Ans- CSP
Que- Constraint satisfaction problems on finite domains are typically solved using a form of ___________
a. Search Algorithms
b. Heuristic Search Algorithms
c. Greedy Search Algorithms
d. All of the mentioned
Ans- All of the mentioned
Que- Solving a constraint satisfaction problem on a finite domain is an/a ___________ problem with respect to the domain size.
a. P complete
b. NP complete
c. NP hard
d. Domain dependent
Ans- NP complete
Que- ____________ is/are useful when the original formulation of a problem is altered in some way, typically because the set of constraints to consider evolves because of the environment.
a. Static CSPs
b. Dynamic CSPs
c. Flexible CSPs
d. None of the mentioned
Ans- Dynamic CSPs
Que- Flexible CSPs relax on _______
a. Constraints
b. Current State
c. Initial State
d. Goal State
Ans- Constraints
Que- Language/Languages used for programming Constraint Programming includes
a. Prolog
b. C Sharp
c. C
d. Fortrun
Ans- Prolog
Que- Backtracking is based on,
a. Last in first out
b. First in first out
c. Recursion
d. Both Last in first out & Recursion
Ans- Both Last in first out & Recursion
Que- Constraint Propagation technique actually modifies the CSP problem.
a. TRUE
b. False
c. Nothing can be said
d. None of the mentioned
Ans- TRUE
Que- When do we call the states are safely explored?
a. A goal state is unreachable from any state
b. A goal state is denied access
c. A goal state is reachable from every state
d. None of the mentioned
Ans- A goal state is reachable from every state
Que- Which of the following algorithm is generally used CSP search algorithm?
a. Breadth-first search algorithm
b. Depth-first search algorithm
c. Hill-climbing search algorithm
d. None of the mentioned
Ans- Depth-first search algorithm
Que- General games involves
a. Single-agent
b. Multi-agent
c. Neither Single-agent nor Multi-agent
d. Only Single-agent and Multi-agent
Ans- Only Single-agent and Multi-agent
Que- Adversarial search problems uses
a. Competitive Environment
b. Cooperative Environment
c. Neither Competitive nor Cooperative Environment
d. Only Competitive and Cooperative Environment
Ans- Competitive Environment
Que- Mathematical game theory, a branch of economics, views any multi-agent environment as a game provided that the impact of each agent on the others is "significant," regardless of whether the agents are cooperative or competitive.
a. TRUE
b. False
c. Nothing can be said
d. None of the mentioned
Ans- TRUE
Que- Zero sum games are the one in which there are two agents whose actions must alternate and in which the utility values at the end of the game are always the same.
a. TRUE
b. False
c. Nothing can be said
d. None of the mentioned
Ans- False
Que- Zero sum game has to be a ______ game.
a. Single player
b. Two player
c. Multiplayer
d. Three player
Ans- Multiplayer
Que- A game can be formally defined as a kind of search problem with the following components:
a. Initial State
b. Successor Function
c. Terminal Test
d. All of the mentioned
Ans- All of the mentioned
Que- The initial state and the legal moves for each side define the __________ for the game.
a. Search Tree
b. Game Tree
c. State Space Search
d. Forest
Ans- Game Tree
Que- General algorithm applied on game tree for making decision of win/lose is ____________
a. DFS/BFS Search Algorithms
b. Heuristic Search Algorithms
c. Greedy Search Algorithms
d. MIN/MAX Algorithms
Ans- MIN/MAX Algorithms