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

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The Project

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

At the end of this project, you and your team should have a fully functioning conversational agent!

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The bot is made up of parts in Eclipse and Dialogflow which you need to help finish. The goal of this project is to use Dialogflow, MARBEL, and Prolog to provide a conversational agent with the knowledge to assist us in a certain task. In this case, we have a crazy large recipe database(recipe_database.pl) and we really need some help choosing what to eat. We should be able to tell the agent certain criteria and it should provide us with a fitting recipe. As mentioned your team will be split into pairs. The sections of work are described a briefly below so you can try and figure out who wants to do what.

Dialogflow and Filters

Do you want to get to know a very cool Google Cloud tool? 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

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 includes not only subtitles to the conversation but also so much more. 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. If you have chosen this role we recommend familiarizing yourself with the task by reading our guide.

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For more information go to the Visual Support Section:

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Patterns and Responses

Week - 1 Deliverable

For your Week-1 submission, you need to submit a weekly progress report in .doc format (See Guidelines).Do you like to have a conversation? Well either way this section 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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