$3.$ Rules and Recommendations: Define a set of rules that map customer preferences to

appropriate recommendations. Each rule consists of a condition $($e$.$g$.,$ vegetarian and Italian

cuisine$)$ and a corresponding recommendation $($e$.$g$.,"$We recommend the Margherita pizza or the

Vegetable pasta"$).$

To complete this task, you will need to implement the following components:

$1.$ Menu Representation: Define the restaurant's menu as a data structure in Python, such as a list

or a dictionary, where each item represents a dish with its relevant details.

$2.$ Searching Algorithm: Implement a searching algorithm, such as linear search, binary search,

or even a more complex algorithm like A$*$ search, to search through the menu and find suitable

recommendations based on customer preferences.

$3.$ Recommendation Generation: Based on the search results, generate appropriate

recommendations for the customer by applying the predefined rules and matching them with the

customer's preferences.

Once you have implemented the searching algorithm in Python, you can test it by simulating

customer interactions, providing their preferences, and evaluating the agent's ability to find

suitable recommendations based on the predefined rules and the menu.