Welcome to this page with an overview of some of the required background information for this project. You most likely have already seen some of this information in other courses, but some of it will also be new to you. We expect each of you to have a decent understanding of the material listed below, as that is necessary to be able to complete this project successfully. You need to complete a quiz, where the material presented below can be used to prepare.
Page Overview
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Git
We use GitHub Classroom to provide you with the initial agent code. GitHub is acode hosting platform for version control and collaboration. You need to join the GitHub classroom and use it for developing and sharing your code, and for storing and updating all the deliverables in this project. In order to understand how to do that, we introduce you to some basic readings and a tutorial to gain knowledge of how to use git. Git is a common tool used by many coding teams worldwide to develop code in tandem and facilitate its alignment. Getting to know git as part of this course will surely be of benefit to you in the long term.
Git Commands
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You can do just the basics reading, just the interactive tutorial, or both. There is also a more in-depth explanation of each command on the third page. |
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If you want to know more (not required): Everything on Git.
Git Merging and Conflicts
https://www.simplilearn.com/tutorials/git-tutorial/merge-conflicts-in-git
Git Best Practices
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One of the most important takeaways from the link below is that: Commits are Supposed to Be Small and Frequent Whenever you have made a single logical alteration, you can commit code. Frequent commit helps you to write brief commit messages that are short yet informative. Also, it will provide significant meaning for those who may be reading through your code. |
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In the case of this project commits must be made with certain specifications and things in mind please read Commits for more information. |
Training and Testing Machine Learning Models
Natural Language Understanding and NLU Pipelines
- what is nlu
- what is an nlu pipeline
ASR and WHISPER
Intent and Slot Classification
- Explain what the task is generally
- Explain how it is done
- What is an ontology - importance
- intent and slot definition
LLMs and Hugging Face
- Explain Pretrained LLMs like BERT used
HTML and Bootstrap
You will be developing a few basic web pages to provide some visual support to a user while they is conversing with your agent. We assume you are familiar with basic forms of HTML, the markup language for developing a webpage. If not, please check out this https://www.w3schools.com/html/default.asp to get you started.
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To understand how you can integrate Bootstrap components into your agent, you need to read our https://socialrobotics.atlassian.net/wiki/pages/createpage.action?spaceKey=PM2&title=2025%20Visual%20Support%20Guide. This is where we explain in more detail how you can use Bootstrap components for creating a webpage in this project.
Prolog
You will develop your recipe recommendation agent using MARBEL and SWI Prolog. The MARBEL agent implements a dialog management engine that you will use. You do not need to change this agent. You are, however, allowed to modify it if you like. The focus will be mostly on using Prolog to provide the agent with the knowledge it needs and to make it smarter by providing it with some logic related to its recipe recommendation task.
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