Q1.
Explain Task environment, State space and PEAS representation. Give the PEAS representation
for Vacuum-Cleaner world along with its State space diagram. Write its Agent function. Give
some examples of the percept sequence and action mapping of this agent.
Q2.
Describe various consistencies for Constraints Propagation in CSPs. Write the Arc consistency
algorithm with an example showing its use.
Q3.
List and explain the methodologies that can be adopted to improve the Backtracking algorithm
used to solve CSPs. Give appropriate example for each.
Q4.
Give a comparison between all the uninformed search algorithms and write their advantages
and disadvantages. Write the algorithm for solving 8 queen problem using the hill climbing
approach.
Q5.
Consider the search problem below with start state S and goal state G. The transition
costs are next to the edges, and the heuristic values are above the states.
(i) What is the path if best first search algorithm is used to reach the goal?
(ii) What is the path if depth first search is used? If a node has multiple successors, then
expand the successors in increasing alphabetical order
(iii) If A* algorithm is used, what is the path?
(iv) Is the heuristic function in this problem admissible?
Q6.
What is Best First Search and A* Search? Explain the algorithms and complexities of Best First
Search and A* Search with an example.
Q7.
Explain the following uninformed search strategies with examples:
i. Breadth First Search (BFS)
ii. Uniform Cost Search (UCS)
iii. Depth First Search (DFS)
iv. Depth Limited Search (DLS)
v. Iterative Deepening Search (IDS)
Q8.
How does alpha-beta pruning work? Explain with example.
Q9.
Write the Alpha-Beta Algorithm. Explain Alpha cut and Beta Cut briefly. Solve the following
example and show how alpha-beta pruning helped in pruning the search tree.
Q10.
Explain the scenario of ‘Imperfect Real Time’ decisions. What methodologies could be adopted
to deal with such situations? Explain by giving some examples of such scenario.
Q11.
Write the Minimax Algorithm and explain ‘Evaluation function’ and ‘Optimal strategy’. Take an
example of Tic-tac-toe game and explain how evaluation function can be used to find the next
best move.
Q12.
What is Logical Agent? What are its properties? Demonstrate the Wumpus world problem and
its solution with the help of logical agents.
Q13.
State and explain clearly, the Knowledge Engineering process in First order Logic (FOL).
Explain the ‘Quantifiers’ used in FOL giving some examples.
Q14.
Explain how the Planning problems can be represented. Explain the ‘Block’s World’ problem
and how it can be solved using Total ordered planning.
Q15.
Consider the following figure:
Use the heuristic function h(n)=+1, if the block is on the correct block/table and h(n) =-1,
Otherwise.
Which type of problem do you face using hill climbing algorithm to reach the goal?
Use a suitable heuristic function to avoid this problem to reach the goal. Show all
the steps.
Q16.
Write the Hill climbing search algorithm. Analyze the performance of the algorithm basing upon
appropriate characteristics feature.
Q17.
Explain backward chaining with example.
Q18.
If KANSAS + OHIO = OREGON Then find the value of G + R + O + S + S (apply constraint
satisfaction rules)
Q19.
HERE = COMES - SHE, (Assume S = 8) Find the value of R + H + O (apply constraint
satisfaction rules)
Q20.
Define constraint satisfaction problem (CSP). How CSP is formulated as a search problem?
Explain with an example.
Q21.
Explain with examples:
(a) Cryptarithmetic problem
(b) Adversarial search problem
Q22.
Suppose, there are 3 jugs of capacities 8, 5 and 3 litres respectively. There is no scale on the jugs,
so it’s only their capacities that are known. Initially the 8 litre jug is full of water the other two
jugs are empty. The water can be poured from one jug to another. The goal is to have exactly 4
litre of water in any of the jugs. The amount of the water in other two jugs at the end is irrelevant.
Formulate this problem as a state space search problem and draw the state space graph of this
problem.
Q23.
Explain forward chaining with example.
Q24.
Explain minimax algorithm with example.
Q25.
Explain how to formally define a problem using constraint satisfaction problem? Provide the
constraint propagation and backtracking process in relation to map coloring problem.
Q26.
Formally define crypt-arithmetic problem, map coloring problem and N-queen’s problem as
constraint satisfaction problems. Solve Map Coloring Problem using constraint satisfaction
problem.
Q27.
What is the problem with informed search algorithms? Why uninformed search techniques in
some conditions are better than informed search techniques?
Q28.
What do you understand by informed search techniques? Given A* algorithm, explain how you
can modify the A* algorithm to behave as Greedy Best First algorithm.
Q29.
What is the problem with informed search algorithms? Derive the time complexity and space
complexity of Iterative Depth First Search algorithm.
Q30.
What do you understand by soundness and completeness in inference mechanism? Provide the
architecture of a knowledge-based agent for partially observable environment.
Q31.
Provide the architecture of a knowledge-based agent for partially observable environment.
Elaborate how propositional logic can be used in designing knowledge-based agent. You may
take example of “The Wumpus World”.
Q32.
How is Resolution algorithm used for inference mechanism? provide the algorithm and
elaborate using an example of your choice.
Q33.
Contrast Multiagent Planning with conditional and continuous planning. For a partially
observable environment what type of planning do you suggest.
Q34.
Given a partially observable environment what type of planning do you suggest. Justify by giving
proper technical explanation.
Q35.
Elaborate and explain using proper example the difference between inference using forward and
backward chaining.
Q36.
Define Constraint Satisfaction Problems along with its components. Solve the following
Cryptarithmetic Problem, properly defining its variables, domains and constraints involved-
EAT
THAT
-------------
APPLE
Draw the constraint graph of the same.
Q37.
Explain the Backtracking search for CSPs. What are its drawback? What is Backjumping and
how it is better than backtracking ? Explain by taking the example of 4-Queens problem.
Q38.
What is Logical Agent? What are its properties? Give the PEAS representation and
characteristics feature for ‘wumpus world’ problem. Take an example and show how the logical
agent can solve the problem.
Q39.
State and explain the Knowledge Engineering process in First order Logic. Provide the
architecture of a knowledge-based agent for partially observable
environment. Elaborate how propositional logic can be used in designing
knowledge based agent. You may take example of “The Wumpus World”.
Q40.
What is AI? What is PEAS? Explain different agent types with their PEAS descriptions. Explain
in detail the properties of Task Environments. Differentiate Informed & Uninformed search.
Give examples.
Q41.
Contrast Multiagent Planning with conditional and continuous planning. For a
partially observable environment what type of planning do you suggest .
Q42.
Given a partially observable environment, what type of planning do you suggest?
Justify by giving proper technical explanation.
Q43.
Elaborate and explain using proper example the difference between inference
using forward and backward chaining.
Q44.
What is Greedy Best First Search? Explain with an example the different stages of
Greedy Best First search.
Q46.
Define constraint satisfaction problem (CSP). Explain with an example, how CSP is formulated
as a search problem.
Q47.
Define the syntactic elements of First Order Logic (FOL). Illustrate the use of FOL to represent
knowledge.
