Using A* algorithm, find the shortest path from A to F. Assume the heuristic (straight-line distance to F) is: h(A)=5, h(B)=4, h(C)=2, h(D)=6, h(E)=3, h(F)=0 Show your step-by-step calculation, including f(n)=g(n)+h(n).

Consider the graph shown below where the numbers on the links are link costs and the numbers next to the states are heuristic estimates. Note that the arcs are undirected. Let A be the start state and G be the goal state. Simulate A* search with a strict expanded list on this graph. At each step, show the path to the state of the node that's being expanded, the length of that path, the total estimated cost of the path (actual + heuristic), and the current value of the expanded list (as a list of states).

Imagine you are designing an intelligent personal shopping agent for an online store. The agent should understand the user's preferences (style, budget, and past purchase history). Negotiate discounts with the store's pricing system. Recommend the best items while balancing cost, quality, and delivery time. How would you design the perception, reasoning, and action components of this agent, and what AI techniques would you apply in each step?

Explain the difference between simple reflex agents and model-based reflex agents.

Describe the function of vacuum cleaner as an Agent with suitable diagram.

Define Agent and Rational Agent through real-time example.

Imagine, Mary is a model-based Al that builds and maintains an internal representation of the world. And Greg, on the other hand, is a goal-based Al that focuses on achieving specific objectives. Describe the fundamental differences between Model-Based Mary and Goal-Based Greg in terms of their operational strategies. How does each approach affect their decision-making processes and adaptability to changes in their environment

Distinguish between the following properties of a task environment: i. Static or dynamic ii. Discrete or continuous iii. Single or multi-agent

Define Rational Agent with example. Is vacuum cleaner agent Rational? Why or why not? Explain with suitable reasons.

How does alpha-beta pruning improve the efficiency of the Minimax algorithm in evaluating game trees like the tree?

Explain the process of Minimax without alpha-beta pruning and how it determines the optimal move.

How does alpha-beta pruning improve the efficiency of the Minimax algorithm in evaluating game trees like the tree?

Consider the following constraint graph for a graph coloring problem (the constraints indicate that connected nodes cannot have the same color). The domains are shown in the boxes next to each variable node. Now answer the following questions: I. What are the variable domains after a full constraint propagation? II. Show the sequence of variable assignments during a pure backtracking search (do not assume that the propagation above has been done), assume that the variables are examined in numerical order and the values are assigned in the order shown next to each node.

Explain why heuristics are important in AI search problems with an example scenario.

Imagine you are designing an intelligent personal shopping agent for an online store. The agent should understand the user's preferences (style, budget, and past purchase history). Negotiate discounts with the store's pricing system. Recommend the best items while balancing cost, quality, and delivery time. How would you design the perception, reasoning, and action components of this agent, and what AI techniques would you apply in each step?

Explain the difference between simple reflex agents and model-based reflex agents.

What is AI? Differentiate between the strong Al and Weak AI.

How is Machine Learning related to Artificial Intelligence?

Distinguish between the following properties of a task environment: i. Static or dynamic ii. Discrete or continuous iii. Single or multi-agent

How is Machine Learning related to Artificial Intelligence?

Perform Breadth-First Search (BFS) starting from node A. List the order in which nodes are visited.

Write down the advantage of IDS over BFS and DFS. Also, shows times and space complexity among them.

Consider the following constraint graph for a graph coloring problem (the constraints indicate that connected nodes cannot have the same color). The domains are shown in the boxes next to each variable node. Now answer the following questions: I. What are the variable domains after a full constraint propagation? II. Show the sequence of variable assignments during a pure backtracking search (do not assume that the propagation above has been done), assume that the variables are examined in numerical order and the values are assigned in the order shown next to each node.

Write down the advantage of IDS over BFS and DFS. Also, shows times and space complexity among them.

Translate each of the following sentences into First Order Logic (FOL) i. Not all cars have carburetors. ii. All babies are illogical. iii. Every connected and circuit-free graph is a tree. iv. All that glitters is not gold.

What is forward checking algorithm? Give an example.

Explain the terms "max node" and "min node" in the Minimax algorithm. How are they used to represent players in a game?

Perform Breadth-First Search (BFS) starting from node A. List the order in which nodes are visited.

Consider the following constraint graph for a graph coloring problem (the constraints indicate that connected nodes cannot have the same color). The domains are shown in the boxes next to each variable node. Now answer the following questions: I. What are the variable domains after a full constraint propagation? II. Show the sequence of variable assignments during a pure backtracking search (do not assume that the propagation above has been done), assume that the variables are examined in numerical order and the values are assigned in the order shown next to each node.

What is Greedy Best First Search? Explain with an example the different stages of Greedy Best First search.

Using A* algorithm, find the shortest path from A to F. Assume the heuristic (straight-line distance to F) is: h(A)=5, h(B)=4, h(C)=2, h(D)=6, h(E)=3, h(F)=0 Show your step-by-step calculation, including f(n)=g(n)+h(n).

Consider the graph shown below where the numbers on the links are link costs and the numbers next to the states are heuristic estimates. Note that the arcs are undirected. Let A be the start state and G be the goal state. Simulate A* search with a strict expanded list on this graph. At each step, show the path to the state of the node that's being expanded, the length of that path, the total estimated cost of the path (actual + heuristic), and the current value of the expanded list (as a list of states).

What is Greedy Best First Search? Explain with an example the different stages of Greedy Best First search.

Is the heuristic given in Problem 5.a consistent? Explain.

Write down the advantage of IDS over BFS and DFS. Also, shows times and space complexity among them.

Explain the process of Minimax without alpha-beta pruning and how it determines the optimal move.

How does alpha-beta pruning improve the efficiency of the Minimax algorithm in evaluating game trees like the tree?

Explain the terms "max node" and "min node" in the Minimax algorithm. How are they used to represent players in a game?

The graph above shows the step costs for different paths going from the start (S) to the goal (G). Use uniform cost search to find the optimal path to the goal.

Translate each of the following sentences into First Order Logic (FOL) i. Not all cars have carburetors. ii. All babies are illogical. iii. Every connected and circuit-free graph is a tree. iv. All that glitters is not gold.

What is logic, syntax and semantics ? Write down some propositional logic syntax.

Show that p→(q→r) is logically equivalent to (p∧q)→r

Translate each of the following sentences into First Order Logic (FOL) i. Not all cars have carburetors. ii. All babies are illogical. iii. Every connected and circuit-free graph is a tree. iv. All that glitters is not gold.

How does alpha-beta pruning improve the efficiency of the Minimax algorithm in evaluating game trees like the tree?

Define in your own words the following terms: state, state space, search tree, search node, goal, action, transition model, and branching factor.

Perform Breadth-First Search (BFS) starting from node A. List the order in which nodes are visited.

Compare uninformed search and informed search in terms of completeness, optimality, time complexity, and space complexity.

Explain why heuristics are important in AI search problems with an example scenario.

What is Greedy Best First Search? Explain with an example the different stages of Greedy Best First search.

Compare uninformed search and informed search in terms of completeness, optimality, time complexity, and space complexity.

Write down the advantage of IDS over BFS and DFS. Also, shows times and space complexity among them.

What is logic, syntax and semantics ? Write down some propositional logic syntax.

Compare uninformed search and informed search in terms of completeness, optimality, time complexity, and space complexity.

Write down the advantage of IDS over BFS and DFS. Also, shows times and space complexity among them.

The graph above shows the step costs for different paths going from the start (S) to the goal (G). Use uniform cost search to find the optimal path to the goal.

The graph above shows the step costs for different paths going from the start (S) to the goal (G). Use uniform cost search to find the optimal path to the goal.

Draw the final search tree showing the paths explored by A*.

Explain which algorithm (BFS or A*) is more efficient in this case and why.

Describe the function of vacuum cleaner as an Agent with suitable diagram.

Define the following terms in your own words: state, state space, search tree, search node, goal, action, transition model, and branching factor.

Give the values calculated by minimax for all states in the tree. Do not use alpha-beta pruning.

Consider the search space below, where S is the start node and G1 and G2 are goal nodes. Arcs are labeled with the value of a cost function; the number gives the cost of traversing the arc. Above each node is the value of a heuristic function; the number gives the estimate of the distance to the goal. Assume that the algorithms do not keep track of and recognize repeated states.

For each of the following search strategies,

BFS, DFS, GBFS

i. indicate which goal state is reached first (if any) and

ii. List in order, all the states that are popped off the OPEN list.

The graph above shows the step costs for different paths going from the start (S) to the goal (G).Use uniform cost search to find the optimal path to the goal.

What is probabilistic reasoning? How is probabilistic reasoning different from logical reasoning?

Translate each of the following sentences into First Order Logic (FOL):

i. Not all cars have carburetors

ii. Everyone loves everyone.

iii.Every connected and circuit-free graph is a tree iv. All that glitters is not gold

v. Not all that glitters is gold

vi.There is a barber who shaves all men in the town who do not shave themselves

Identify the four sources that demonstrate Evidence of Human Intelligence.

Convert the following

sentence into clausal form:

Perform A* Algorithm on the following figure. Explicitly write down the queue at each step

Indicate which branches of the tree will be pruned by alpha-beta pruning?

Write down the advantage of IDS over BFS and DFS. Also, shows times and space complexity among them.

A simple Bayesian Network, with corresponding conditional probability tables, is given below:

From the Bayesian network, answer the following questions:

i) What is the probability that it is cloudy, it is raining, the sparkler is off, and the grass is wet?

ii) What is the probability that it is not cloudy, it is not raining, the sparkler is on, and the grass is wet?

Discuss the limitations of first-order logic when it comes to expressing certain types of statements or reasoning.

Is the heuristic given in Problem 5.a admissible? Explain.

Distinguish between the following properties of a task environment:

i. Static or dynamic

ii. Discrete or continuous

iii.Single or multi-agent

For the following database facts, write a completion formula that states that Johny is the only person that lives in Barishal.

LIVESIN (johny, Barishal)

LIVESIN(alice, Pabna)

LIVESIN(darth, bogura)

OWNS(johny, smartphone)

STUDENT(darth)

Explain backward chaining algorithm with suitable example.

A proposition with three variables, A, B, and C. Prove that proposition A∧¬A∧(BVC) is a contradiction.

Briefly explain the PEAS description of the task environment for an automated taxi.

Prove (AVB)and[(¬A)∧(¬B)] are equivalent.