Model$-$Based Reflex Agent:

Extend the room$-$cleaning scenario by implementing a model$-$based reflex

agent in Python. The agent should maintain an internal model of the

environment, including a history of room states, and use both the current

perception and historical information to make decisions.

a$.$ Perception Function:

Implement a function called perceive that takes the current state of the

room and the internal model as input. Update the internal model with the

current room state and add it to the history. The state can be represented

as a dictionary with attributes such as cleanliness, presence of obstacles,

and occupancy.

$\{\text{'}$cleanliness$\text{'}$: 'dirty', 'obstacle': False, 'occupied': False$\}$

$\{\text{'}$cleanliness$\text{'}$: 'clean', 'obstacle': True, 'occupied': True$\}$

b$.$ Internal Models:

Create a data structure to represent the internal model of the agent. This

model should store information about the historical state of rooms,

obstacles, and any other relevant details.

c$.$ Decision$-$Making Function:

Implement a function called decide$\_$action that takes the perceived room

state and the internal model as input. Utilize both the current perception

and the history of room states to make decisions. Example actions:

'clean': Clean the room.

'avoid$\_$obstacle': Avoid an obstacle.

'wait': Wait for the room to be unoccupied.

'move$\_$to$\_$next$\_$room': Move to the next room.

d$.$ Action$-$Performing Function:

Implement a function called perform$\_$action that takes the decided action

as input and updates the internal model. Additionally, print a

corresponding message.

e$.$ Interface:

Create a simple user interface to interact with the model$-$based agent.

Allow users to input the state of the room and observe how the agent's

decisions and internal model evolve