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Table of Contents

Info

The information provided here should be sufficient for you to complete the project. For those of you who are interested and want to learn more about conversational patterns and the related coding scheme that we use here (e.g. C4, etc.), see: Moore, R. J., Arar, R. (2019), Conversational UX Design: A Practitioner's Guide to the Natural Conversation Framework. ACM.

Background Knowledge Preparation

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Assigning Roles Within Your Team

At the start of the course, you are expected to have formed a team of 6.One of the first things to think about as a team is how to divide the three main tasks or roles amongst your team members:

  1. Dialogflow and Filters (2 team members);

  2. Visual Support (2 team members); and

  3. Patterns and Responses (2 team members).

The two team members with a similar role should work together in pairs. You can successfully do pair programming in person or remotely. As you will all need to work on one and the same agent, however, it will also be essential for the three pair programming subteams to communicate well with each other to make sure the three programming tasks are adequately integrated and work well together. Another task that you will have as a team that will be essential to develop an effective and robust agent is to continuously evaluate and test your bot once it is up and running (even if it is still only minimally functioning!). For this, make sure you also read the Agent testing page.

At the end of this project, you and your team should have a fully functioning conversational agent that is able to assist users with selecting a recipe they would like to cook!

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This agent will be composed of MARBEL modules and Prolog files which you can develop and edit in Eclipse and which will interact with a Dialogflow agent that you need to create and further develop. As a start, we will use a large recipe database that the agent can use to suggest recipes to users. The user should be able to tell the agent what they would like, and the agent should try to find a matching recipe (of course, assuming that one is available in the database).

To help you decide which team member fits best to which role, the three roles are described briefly below.

Role 1: 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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Role 2: 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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Role 3: 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.

For more information go to the Pattern and Responses Section:

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Setting up the Tools You Need

Before you actually install the tools that you need for this project, first have a brief look at this page to get a quick overview of what you will use for developing your agent:

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Now you have some idea of what software tools you will be using, get started by following the instructions on this page for setting up the tools you will need:

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Getting up to Speed: the Background Knowledge You Need

Before beginning your project, we would like you all to familiarize yourself with some important background knowledge needed about Git and Dialogflow. You will find you will need this to successfully complete your project. These tools are Git and Dialogflow. Everything we think you navigate this project, and it will help you to better understand what some of the tools and one of the key components of the agent are all about. The basic stuff that we think you really need to know can be found here:

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Note

Note that to test your knowledge of these subjects, there are

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Canvas quizzes

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that must be completed.

Team Forming and Initial Set Up

By the end of the week, you will need to form teams of 5 and begin to prepare for your project. Roles are divided in this project into three categories: Dialogflow and Filters(2); Visual Support(2); and Patterns and Responses(2). The number in parenthesis is the number of people we recommend you assign to each category. You will still need to work together in many regards, so make sure there is still communication between categories. Together you will set up your project and familiarize yourself with the project tools.

Instructions for the tools and set up can be found here:

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The Roles: D&F, VS, P&R

Dialogflow and Filters

Visual Support

The Visual Support team will incorporate visuals to enrich the conversation. If you have chosen this role we recommend familiarizing yourself with the task by reading our guide.

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Patterns and Responses

Week - 1 Deliverable

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Info

The information provided here should be sufficient for you to complete the project. For those of you who are interested and want to learn more about conversational patterns and the related coding scheme that we use here (e.g. C4, etc.), see: Moore, R. J., Arar, R. (2019), Conversational UX Design: A Practitioner's Guide to the Natural Conversation Framework. ACM.