# Computer/Artificial Intelligence and Robotics (AIR) Set 2 Sample Test,Sample questions

## Question: ``` What is true aboout Iterative Deepening DFS? B. C. D. ```

1. it does not perform dfs in a bfs fashion.

2.it is the preferred informed search method

3.it’s a depth first search, but it does it one level at a time, gradually increasing the limit, until a goal is found.

4.is a depth-first search with a fixed depth limit l

## Question: ` A planning problem P in BSSP is defined as a _____________`

1.triple (s, g, o)

2.triple (s1, s2, o

3.triple (g1, g, o)

4.none of the above

## Question: ` Arc consistency in AO graph is concernd with _____________`

1.nodes

2. finding consistent values for pairs of variables

3.unary constraint

4.All of the above

## Question: ` effects⁺ (a) in Forward State Space Planning denotes ___________________`

1.denotes the set of negative effects of action a

2.denotes the set of neutral effects of action a

3.denotes the set of positive effects of action a

4.None of These

## Question: ` How can we convert AO graph with mixed nodes into graph with pure AND and OR nodes?`

1.by traversing multiple node

2.by deleting one of the node

4.None of the above

## Question: ` In Forward State Space Planning , Progress ( A, S) function returns`

1. the successor state s when action a is applied to state s.

2.the predecessor state s when action a is applied to state s.

3.both a & b

4.None of the above

## Question: ` In which chaining, the Left-Hand side is used to match the rules and Right-Hand side is used to check the effect of using the rule.`

1.forward chaining

2.backward chaining

3. reverse chaining

4.both b & c

## Question: ` The components of Expert system are?`

1.a set of rules, the inference engine (ie), forward chaining

2.a set of rules, backward chaining, a working memory (wm)

3.a set of rules, the inference engine (ie), a working memory (wm)

4. a set of rules, forward chaining, backward chaining

1.0

2.1

3.2

4.3

## Question: ` What is true aboout Iterative Deepening DFS?`

1.it does not perform dfs in a bfs fashion.

2. it is the preferred informed search method

3.it’s a depth first search, but it does it one level at a time, gradually increasing the limit, until a goal is found.

4. is a depth-first search with a fixed depth limit l

1.saint

2.xcon

3.dendral

4.both a and c

## Question: ` Which of the following are Informed search algorithms?`

1.best first search

2.a* search

3.iterative deeping search

4.both a & b

## Question: ` Which search strategy is combining the benefits of both BFS and DFS?`

1.depth limited search

2.a*

3.iterative deepening depth first search

4.best first search

## Question: `A planning problem P in BSSP is defined as a _____`

1. triple (s, g, o)

2. triple (s1, s2, o)

3. triple (g1, g, o)

4.None of the above

## Question: `A* Search Algorithm _______________`

1.does not expand the node which have the lowest value of f(n),

2.finds the shortest path through the search space using the heuristic function i.e f(n)=g(n) + h(n)

4.All of the above

1.h(n)>= h*(n)

2. h(n)< h*(n)

3.h(n)== h*(n)

4.h(n)<= h*(n)

## Question: `Agent interacts with the world via _______________ and ______________`

1.decision , effect

2.perception, decision

3.perception, action

4.perception, effect

## Question: `Backward State Space Planning (BSSP)_______________________________`

1.simply explores the set of all future states in possible order

2. start searching backwards from the goal

4.has no sense of direction

## Question: `Goal Stack Planning breaks up a _______`

1. initial state

2.stack in different part

3.set of goal predicates into individual subgoals

4. all of the above

## Question: `If there is a solution, breadth first search is _______________to find it`

1. difficult

2.guaranteed

3.not able to find

4.none of the above

## Question: `In Backward State Space Planning ,regress(A,G) that returns ______________________________`

1. the regressed goal over action a when applied to goal g.

2.the goal state over action a when applied to goal g.

3.the initial state over action a when applied to goal g.

4. both a & b

## Question: `Production/Rule looks like________________`

1. pattern-->data

2.action-->data

3.pattern-->action

4.none of the above

## Question: `Tabu search is designed __________________________`

1.as it does not follow aspiration criteria

2. to escape the trap of local optimality.

3.to unrecord forbidden moves, which are referred to as tabu moves .

4.None of these

## Question: `The components of Expert system are?`

1.a set of rules, the inference engine (ie), forward chaining

2.a set of rules, backward chaining, a working memory (wm)

3.a set of rules, the inference engine (ie), a working memory (wm)

4.a set of rules, forward chaining, backward chaining

## Question: `The efficiency of A* algorithm depends on __________________________`

1.depth

2.the quality of heuristic

3.unknown nodes

4.none of the above

## Question: `The start node for search in plan space planning is______`

1.bfs

2.dfs

3.both dfs and bfs

4.a*

## Question: `What arcs represents in AO Graph?`

1.subproblem to be solved individually

2.solution

3.path

4. sequence of actions

## Question: `What are the drawbacks of Forward State Space Planning?`

1. A. fssp has very huge search space

2. it includes the actions that have nothing go do with achieving the goal

3.regression is used in forward state space planning

4.both a and b

## Question: `What is Hyper-Edge in AO Graph?`

1.many edges together can be hyber edge

2. those are and edges only

3.both 1 and 2

4.none of the above

1.0

2.1

3.2

4.3

## Question: `what is the issue of Forward State Space Planning?`

1. low banching factor.

2.large branching factor

3.work in forward fashion

4.work in backward fashion

## Question: `What is the main advantage of backward state-space search?`

1. cost

2.actions

3. relevant actions

4.all of the mentioned

## Question: `What is the termination criteria in Hill climbing?`

1.when no successor of the node has better heuristic value.

2.when successor of the node has better heuristic value.

3.when no ancestor of the node has better heuristic value.

4. when ancestor of the node has better heuristic value.

## Question: `What is true aboout Iterative Deepening DFS?`

1. it does not perform dfs in a bfs fashion.

2. it is the preferred informed search method

3. it’s a depth first search, but it does it one level at a time, gradually increasing the limit, until a goal is found.

4.is a depth-first search with a fixed depth limit l

## Question: `What is true about Artificial Intelligence?`

1.the ability to solve problems.

2.the ability to act rationally.

3. the ability to act like humans

4.all of the above

## Question: `What is true about Backward State Space Planning?`

1. goal states are often incompletely specified.

2.expresses only what is desired in the final state, rather than a complete description of the final state.

3.it uses regression

4.all of the above

## Question: `What is true about Linear Planning?`

1. it refers to the fact that the subgoals are attempted and solved in a linear order.

2.attempts to solve subgoals individually one after another.

3. attempts to solve subgoal individually in non linear fashion

4. both a & b

## Question: `What is true about variable neighborhood function?`

1. neighbourhood functions that are sparse lead to quicker movement during search

2.algorithm has to inspect very fewer neighbours

3.vdn stars searching with sparse neighbourhood functions, when it reaches an optimum, it switches to denser function.

4.None of the above

1.unvisited

2.unsolved

3.solved

4.visited

1.plateau

2.ridges

3.local maximum

4.landscape

## Question: `Which property asks that the algorithm is locally admissible?`

2.monotonicity

3.informedness

4.All of the above