The Water Jug Problem is a classical problem-solving problem in Artificial Intelligence that demonstrates how a computer can find a sequence of actions to reach a desired state from an initial state. The problem consists of two jugs with known capacities and no measurement markings. The objective is to obtain a specified quantity of water by filling, emptying, and transferring water between the jugs. In a Programming and AI Lab, this problem is useful for understanding state-space representation, search algorithms, problem formulation, and implementation using Python. Consider two jugs, one having a capacity of 4 litres and another having a capacity of 3 litres. Initially, both jugs are empty. The objective is to measure exactly 2 litres of water. A state can be represented as a pair `(x, y)`, where `x` represents the amount of water in the 4-litre jug and `y` represents the amount of water in the 3-litre jug. Therefore, the initial state is `(0, 0)`. The goal is any state in which either jug contains exactly 2 litres, such as `(2, 0)`, `(2, 3)`, `(4, 2)`, or `(0, 2)`. The possible operations in the Water Jug Problem are filling either jug completely, emptying either jug completely, pouring water from one jug into the other until the source jug becomes empty or the destination jug becomes full. Each operation generates a new state. These states can be explored systematically using a search technique. A Breadth-First Search (BFS) approach is suitable because it explores states level by level and can find the shortest sequence of operations when every operation has equal cost. The following Python program implements the Water Jug Problem using BFS: ```python from collections import deque def water_jug_problem(capacity_a, capacity_b, target): initial_state = (0, 0) queue = deque() queue.append(initial_state) visited = set() visited.add(initial_state) parent = {} action = {} while queue: current = queue.popleft() a, b = current if a == target or b == target: