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Modelling
AI Modelling refers to developing algorithms, also called models which can be trained to get intelligent outputs.
That is, writing codes to make a machine artificially intelligent. The model is trained using data. There are mainly
two types of AI models:
AI Model
Learning-based Rule-based
Machine Learning Deep Learning
Supervised Unsupervised Reinforcement Artificial Neural Convolutional
Learning Learning Learning Network Neural Network
Rule-based Approach
The Rule-based Approach is one of the earliest and simplest methods of implementing artificial intelligence.
It relies on predefined rules and facts created by developers to enable machines to perform specific tasks and
generate desired outputs. Developers manually define a set of rules that determine how the machine processes
data and responds to various scenarios. The main drawback of this approach is that the machine's learning is static.
Once trained, the machine does not adapt to changes made in the original training dataset. If the machine is tested
on a dataset that differs from the rules and data provided during the training stage, it will fail to produce accurate
results and will not learn or adjust to the new conditions it encounters.
CASE STUDY: Banks' Chatbot
A bank's website features a chatbot to assist users with basic queries related to account management, such as
checking account balance, resetting passwords, or locating nearby ATMs.
1. Data: The data required to train the chatbot is a simple scripted document based on questions and their
corresponding answers.
2. Rules: The chatbot uses a simple decision tree approach with defined rules under the category of “Yes” or “No”
to complete the conversation. Some of the rules which bank's chatbots follow are:
a. Rule 1: Predefined Questions and Answers. It prompts the user for “Account Balance”, if the answer is Yes, it
suggests a mobile app of the bank or login to an online banking account. Based on the user choice, it does
the needful.
b. Rule 2: Keyword Matching. The chatbot recognises user input through keywords and matches them to
pre-set rules in its database. For example, when a user mentions "ATM," the bot asks for location details and
retrieves nearby ATMs from a stored database.
c. Rule 3: Guided Flow Under the “Change Password” option. It will prompt the user to answer the details like
registered email or phone number. It will direct the user to the login page.
d. Rule 4: Options and Redirection. Provides links to relevant pages (e.g., login, directions) and redirects to
human support for more complex issues.
3. Interaction: When a user communicates with the chatbot, it processes the message by matching it with the
predefined rules. Depending on the situation, the chatbot replies with a ready-made response or asks for more
details to address the query.
Advanced Concepts of Modeling in AI 119

