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Before beginning your project, we would like you all to familiarize yourself with some background knowledge about Git and Dialogflow, which are needed to successfully complete your project.
Everything we think you need to know can be found here: 2023: Getting Started: What to Know About Git and DialogFlow
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Additionally, on the page 2023: Getting Started: The Tools Used we explain and help you set up the infrastructure needed to complete this project.
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Instructions for the tools and setup can be found here:
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Team Roles
At the end of this project, you and your team should have a fully functioning conversational agent!
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To help you decide which team member fits best to which role, the three roles are described briefly below. Make sure you also read the System testing page as you should be continuously testing your bot once it is minimally functioning.
Dialogflow and Filters (D&F)
Do you want to get to know a very cool Google Cloud tool, and like to optimize the agent’s understanding of what the user says? In the introductory part of the course, you should have learned a bit about Dialogflow. If that sounded super cool to you then this could be the section for you! You will create Intents, Entities, and Prolog Filter Rules in order to provide the agent with the vocabulary comprehension and filtering abilities it needs to converse about recipes.
For more information go to the Dialogflow and Filters Section:
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Visual Support (VS)
Do you want to get creative? Your agent will not only have a conversational component but a visual one. The program uses dynamic webpages to provide the user with visual support in their conversation. This not only includes subtitles to the conversation, but also information in support of what is talked about at any moment - such as the recipes that fit the preferences voiced by the user. If you are ready to break out a bit of HTML and Prolog, you can create cool pages through rules. The Visual Support team will incorporate visuals to enrich the conversation.
For more information go to the Visual Support Section:
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Patterns and Responses (P&R)
Do you like to think in more detail about the anatomy of a conversation? Then this role could be of interest to you. The patterns and responses section focuses on the conversation part of the conversational agent. Your bot will not be the most robust it can only handle some specific tasks. Thus, we create patterns of what we think conversations in the agent’s intended domain will look like so it knows what to expect and how to respond. You will encode these patterns in Prolog and add responses for every situation.
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