AI Project Showcase

PLang Recommendation System

An expert system to recommend the first
programming language that you should learn.

How it works? Try

A little bit about our expert system...

Powered by Python

A powerful, fast, can run anywhere, and one of the most popular programming language used to develop AI.

Accurate

We made a lot of thorough research by analyzing current trends, job market demands, and reading a lot of articles (i.e. expert's opinion) regarding picking the best first programming language.

Portable

The use of Python enables this program can run on any platform (Windows, Mac, Linux
as long as there is Python 3 installed.

Public

Our source code is public - i.e. other people can view it and help to expand our program. Besides, we are working to increase the choices and the suggestion of programming languages to make it better.

What is PLang Recommendation System?

PLang is an rule-based expert system that is developed and use to recommend the first programming language that user should learn. This expert system is developed with people who are interested in learning programming language, but don't know where to start, in mind. Our program uses simple, text-based console app to gain user's input and display the result accordingly.

Why did we develop it?

Choosing the first programming language is hard. Furthermore, picking the right one, the one that fits our need is much, much harder. Although the barrier to entry the programming world is fairly low - and also cheap, as much of resources and tools can be downloaded for free online - the overabundace of programming languages could overwhelm beginner and people who want to learn to code. This is known as Paradox of Choice.

I remember the first time I decided dive into the programming world. I spent days and days looking for the best language to start with, and well, it ended up with me switching back and forth between Python and Javascript. And that is not only a waste of time, as indecision is worse than making a bad decision. And I think that a lot of people out there might share the same problem as mine.

So by developing this expert system, we hope that we can help the newcomers, future computer science students, and aspiring developers to decide the best first programming language that they should learn, no matter as the tool that will help them secure a career in this interesting field or as a stepping stone, enough to get their feet wet, for their journey as a developer, as programming is a big, fun world.

How did we develop it?

1. Knowledge Acquisition

Knowledge is essential for an expert system to work. And one of the most important step when developing an expert system is the knowledge acquisition phase, where we extract knowledge from expert to transfer it into production rules.

For knowledge acquisition, we used the power of the information on the internet to make our research. We read numerous articles and blogs written by developers (who, we think are expert in their field and the respective programming language that they use), analyze the job markets and Google trends to verify the article.

We list all the list of the resources that we use here.

Production rules of our expert system

2. Design the Inference Engine

After gather the knowledge and making the production rules, we design the core of our expert system the Inference Engine. Basically, inference engine is inferring knowlege that is represented in rules - i.e. production rules - and draw conclusion from those rules.

For our inference engine, we use forward chaining - i.e. we start with facts and use rules to draw conclusion or take actions. We use forward chaining for our expert system as we think its goal-driven approach is the most suitable for recommendation system.

How forward chaining works is fairly simple. First, the program will analyze problem by looking for the facts that match the condition of the IF-THEN rules. Once it found the matching rules, it will select and execute the rules.

3. Code!

Now that we have all the data needed, and the production rules plus the inference engine, it's time to to do the coding. For developing the expert system, we use Python, rather than other conventional tools such as CLIPS. This is mainly due to personal preference, as we think it is easier to do this expert system in Python. Besides, we don't want to spend too much time on learning new syntax as we are fairly familiar with Python.

The coding process is rather simple, as the production rule is basically if statement. We use if else if ( or elif in Python) plus nested if to match and execute the production rules.

Our code snippet

4. The UI

Our expert system is only a console-only app. Thus, it can be used via a text-only user interface through the command line or terminal. Still, it is fully functional and can give the accurate result as desired.

During the execution, the program will ask the user some question. Then, the user can select the option by typing the number of the option. Then, the inference engine will match, select, and execute any matching rule behind the scene.

Once the conclusion is drawn, it will print the recommended programming language for the user plus a little bit fun facts about the language.

The snapshot of the program using execution.

When to use this program?

Although this program is developed with anyone who want to pick his/her programming language in mind, in can also be used by anyone who simply want to learn a new language.

However, it must be noted that this program might be biased toward certain programming language (as expert usually do). Also, the technological landscape is changing so fast that and may render this expert system's recommendation obselete some time in the future. Additionally, this expert system doesn't take all programming languages into our knowledge base, so we might overlook or oversimplify in some respects.

So, noted that you can always take the suggestion of this expert system, but also check the current trend and you can always decide which language is the right fit for you, as there might not be one size fits all for all problems.

We proudly present our members.

Charae a/L Eh Sin

Designer & QA

The man behind our expert system UI and our website.

Agilan

Developer

The guy that makes things work behind the scene.

Khalid

Knowledge Engineer

The one that encoded knowledge for our expert system.

Developing this expert system is a new experience for us. Not only we have the chance what we have learned during our AI class, we also gain a lot of information while doing the research. These experience will surely help latter in our course. As for the system itself, it is still far from other leading and well known expert system. But, we plan to improve this system by adding more knowledge and rules. The other thing on our radar is making a better, perhaps web-based, UI for this expert system. The code is hosted on GitHub means that we can make any tweak anytime and this will also open the door for others to view and help to improve this system, too.

View our code on GitHub.

Source:

Link 1 Link 2 Link 3 Link 4 Link 5

PLang Recommendation System

An expert system to recommend the first
programming language that you should learn.

Overview

Powered by Python

Accurate

Portable

Public

What is PLang Recommendation System?

Why did we develop it?

How did we develop it?

1. Knowledge Acquisition

2. Design the Inference Engine

3. Code!

4. The UI

When to use this program?

Our Team

Charae a/L Eh Sin

Designer & QA

Agilan

Developer

Khalid

Knowledge Engineer

Bottom line

Source:

PLang Recommendation System

An expert system to recommend the first programming language that you should learn.

Overview

Powered by Python

Accurate

Portable

Public

What is PLang Recommendation System?

Why did we develop it?

How did we develop it?

1. Knowledge Acquisition

2. Design the Inference Engine

3. Code!

4. The UI

When to use this program?

Our Team

Charae a/L Eh Sin

Designer & QA

Agilan

Developer

Khalid

Knowledge Engineer

Bottom line

Source:

An expert system to recommend the first
programming language that you should learn.