Question 1

Which is not problem in Hill climing?
A)plateau
B)ridges
C)local maximum
D)landscape

Accepted Answer

Hill climbing problems typically include plateaus, ridges, and local maxima as obstacles to finding the optimal solution. The term "landscape" refers to the overall search space or environment in which these problems can occur, rather than being a problem itself.

Question 2

Tabu search is designed __________________________
A)as it does not follow aspiration criteria
B)to escape the trap of local optimality.
C)to unrecord forbidden moves, which are referred to as tabu moves .
D)all of the above

Accepted Answer

Tabu search is specifically designed to escape the trap of local optimality by allowing the search to continue in new directions that are not immediately the most promising, thus potentially finding better overall solutions.

Question 3

Production/Rule looks like________________
A)pattern-->data
B)action-->data
C)pattern-->action
D)none of the above

Accepted Answer

Production or rule-based systems work by matching patterns to determine what action to take, hence the correct representation is pattern-->action.

Question 4

How can we convert AO graph with mixed nodes into graph with pure AND and OR nodes?
A)by traversing multiple node
B)by deleting one of the node
C)by addition of extra node
D)none of the above

Accepted Answer

Converting an AO graph with mixed nodes into a graph with pure AND and OR nodes often involves the addition of extra nodes to separate the mixed functionalities into distinct AND and OR operations, preserving the logical structure of the graph.

Question 5

Arc consistency in AO graph is concernd with ____________________________________
A)nodes
B)finding consistent values for pairs of variables.
C)unary constraint
D)all of the above

Accepted Answer

Arc consistency in constraint satisfaction problems (CSPs) is about ensuring that for every value of one variable, there is a consistent value in its connected variable, effectively dealing with pairs of variables to maintain consistency.

Question 6

A planning problem P in BSSP is defined as a _____________
A)triple (s, g, o)
B)triple (s1, s2, o)
C)triple (g1, g, o)
D)none of the above

Accepted Answer

A planning problem in the Belief, Desire, Intention (BDI) Software Model for Agent-based Systems (BSSP) is defined as a triple (s, g, o), where 's' stands for the initial state, 'g' for the goal state, and 'o' for the set of operators or actions.

Question 7

Plan representation in Plan Space Planning is done with__ -----------links
A)binding links
B)ordering links and casual link
C)contigent link
D)head step

Accepted Answer

Plan Space Planning uses ordering links and causal links to represent plans. Ordering links establish a sequence between actions, ensuring that one action occurs before another. Causal links, on the other hand, represent the dependencies between actions, specifically how one action achieves a precondition for another.

Question 8

What is true aboout Iterative Deepening DFS?
A)it does not perform dfs in a bfs fashion.
B)it is the preferred informed search method
C)it's a depth first search, but it does it one level at a time, gradually increasing the limit, until a goal is found.
D)is a depth-first search with a fixed depth limit l

Accepted Answer

Iterative Deepening DFS combines the benefits of depth-first and breadth-first searches by performing a depth-first search up to a certain depth limit, then increasing the limit and repeating until a goal is found.

Question 9

What is the main advantage of backward state-space search?
A)cost
B)actions
C)relevant actions
D)all of the mentioned

Accepted Answer

Backward state-space search focuses on relevant actions that directly contribute to reaching the goal, making it more efficient by ignoring irrelevant actions.

Question 10

Backward State Space Planning (BSSP)_______________________________
A)simply explores the set of all future states in possible order
B)start searching backwards from the goal
C)leads to huge search space
D)has no sense of direction

Accepted Answer

Backward State Space Planning (BSSP) starts the planning process from the goal state and works backwards towards the initial state, which is the opposite of forward state space planning that starts from the initial state and works towards the goal.

Question 11

In Backward State Space Planning ,regress(A,G) that returns ______________________________
A)the regressed goal over action a when applied to goal g.
B)the goal state over action a when applied to goal g.
C)the initial state over action a when applied to goal g.
D)both a & b

Accepted Answer

Regress(A,G) in Backward State Space Planning returns the regressed goal over action A when applied to goal G. This means it computes the preconditions that need to be satisfied before action A can be applied to achieve goal G.

Question 12

What is true about Backward State Space Planning?
A)goal states are often incompletely specified.
B)expresses only what is desired in the final state, rather than a complete description of the final state.
C)it uses regression
D)all of the above

Accepted Answer

Backward State Space Planning starts from the goal state and works backward to determine the actions needed to reach that state. A) is true because goal states in planning problems often do not specify every detail of the desired final state, only the important conditions that need to be met. B) is also true as it emphasizes the goal-oriented nature of the planning, focusing on achieving specific objectives rather than detailing every aspect of the final state. C) is correct because backward planning uses regression, a process of working backwards from the goal to find the actions that lead to it. Therefore, D) all of the above is the correct answer.

Question 13

effects? (a) in Forward State Space Planning denotes ___________________
A)denotes the set of negative effects of action a
B)denotes the set of neutral effects of action a
C)denotes the set of positive effects of action a
D)none of the above

Accepted Answer

In Forward State Space Planning, $ \text{effects}^+(a) $ denotes the set of positive effects of action $a$, meaning the conditions that become true as a result of performing the action $a$.

Question 14

In Forward State Space Planning , Progress ( A, S) function returns ___________________
A)the successor state s when action a is applied to state s.
B)the predecessor state s when action a is applied to state s.
C)both a & b
D)none of the above

Accepted Answer

The Progress function, often denoted as Progress(A, S), returns the successor state that results from applying action A to the current state S in the context of forward state space planning. This means it computes the new state that emerges as a consequence of executing a specific action in a given state, rather than the predecessor state or any other outcome.

Question 15

What are the drawbacks of Forward State Space Planning?
A)fssp has very huge search space
B)it includes the actions that have nothing go do with achieving the goal
C)regression is used in forward state space planning
D)both a & b

Accepted Answer

Forward State Space Planning has a very large search space and often includes actions that are irrelevant to achieving the goal, making both A and B correct. Regression is not typically used in forward state space planning; it is more associated with backward state space planning.

Question 16

What arcs represents in AO Graph?
A)subproblem to be solved individually
B)solution
C)path
D)sequence of actions

Accepted Answer

The answer of What arcs represents in AO Graph?
A)subproblem to...

Question 17

Which are the first AI applications of AO graph?
A)saint
B)xcon
C)dendral
D)both a and c

Accepted Answer

The first AI applications of AO graph include SAINT, which was an early symbolic integration program, and DENDRAL, which was a project in artificial intelligence to produce chemical analyses of organic compounds. Both of these applications utilized AO (And-Or) graph representations in their problem-solving processes.

Question 18

What is Hyper-Edge in AO Graph?
A)many edges together can be hyber edge
B)those are and edges only
C)both 1 and 2
D)none of the above

Accepted Answer

Hyper-Edge in AO (And-Or) Graphs represents a combination of multiple edges, indicating that all the edges together form a single hyper-edge, which aligns with both provided descriptions.

Question 19

What cost is assumed for arc while solving AO* progress example?
A)0
B)1
C)2
D)3

Accepted Answer

The cost assumed for an arc while solving AO* algorithm examples is typically 1, unless specified otherwise. This simplification helps in focusing on the algorithm's decision-making process rather than on complex cost calculations.

Question 20

What is the heuristic cost of SOLVED nodes in AO* example?
A)0
B)1
C)2
D)3

Accepted Answer

In AO* algorithm, the heuristic cost of solved nodes is considered to be 0 because these nodes represent a state where the goal has been achieved, and no further cost is expected to be incurred to reach the goal from these nodes.

Quiz 4: State Space Planning and Ao Graph in AI

Which is not problem in Hill climing?

Tabu search is designed __________________________

Production/Rule looks like________________

How can we convert AO graph with mixed nodes into graph with pure AND and OR nodes?

Arc consistency in AO graph is concernd with ____________________________________

A planning problem P in BSSP is defined as a _____________

Plan representation in Plan Space Planning is done with__ -----------links

What is true aboout Iterative Deepening DFS?

What is the main advantage of backward state-space search?

Backward State Space Planning (BSSP) _______________________________

In Backward State Space Planning ,regress(A,G) that returns ______________________________

What is true about Backward State Space Planning?

effects? (a) in Forward State Space Planning denotes ___________________

In Forward State Space Planning , Progress ( A, S) function returns ___________________

What are the drawbacks of Forward State Space Planning?

What arcs represents in AO Graph?

Which are the first AI applications of AO graph?

What is Hyper-Edge in AO Graph?

What cost is assumed for arc while solving AO* progress example?

What is the heuristic cost of SOLVED nodes in AO* example?