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Prolog and Patterns
This section is about implementing the dialog management capabilities of the conversational agent but also about implementing the domain logic that the agent needs to be able to perform its task. As our conversational agent is a task-based agent for recipe recommendation, the conversational competence of our agent focuses on patterns and responses for recipe recommendation. The logic that we need to implement concerns the capabilities our agent needs to reason about user requests about recipes that are available in the agent’s database. We will first focus on adding some conversational capabilities for our agent to respond to a user request to provide a (random) recipe. As before, the approach to dialog management that we use here will require the definition of a pattern and the specification of a (textual) response to the user request. When we have completed that part, we will continue with the part focusing on implementing the reasoning capabilities for extracting such a recipe from the database in Prolog. This will require the definition of several rules for extracting a suitable recipe from the agent’s recipe database.
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This clause specifies that if there are no filters to apply (i.e., the filter list is empty), the output of this filtering of a list of RecipeIDs thus remains unchanged and is that same list of recipe identifiers RecipeIDs.
Visuals
Recipe Overview 1 page
The objective of this capability is to create a page that facilitates users in having a conversation about recipes with the agent. We will walk you through the detailed steps to illustrate how to implement the code for such a page below.
At this stage, for this capability, we will only provide code for a very basic skeleton page that provides the minimal functionality needed to implement the main requirement for this page: enable a user to talk to the agent using a microphone button. The design is kept minimal below and in the step-by-step walkthrough below we will only use a single and simple alert element to style the page. As the recipe recommendation page will be the first interaction point for users who are actually interested in finding a recipe, we leave it up to you to try and make this page more inviting and add clear and useful content that the agent will show. Note that we will also ask you to extend this page at a later stage when you are implementing [TBU]Filtering by Inclusion. See Visual Requirements for more information.
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If your team has implemented the Dialogflow intents and patterns for this capability, you can already test the page you just implemented for the So try and Run your Conversational Agent again to see the page you just created. Also, check out the MARBEL agent’s state using the introspector. You should see that the top-level pattern that is at the front of the session is |
Recipe
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Confirmation page
The next page that we want you to add is a recipe confirmation page. At the end of the pattern enabling a user to request a (random) recipe, the agent inserts the a50recipeConfirm pattern ID in the agenda. No pattern has been added for this pattern ID yet, but because it will appear at the top level in the agent’s agenda, we can already create a confirmation page for the recipe that is selected. The idea is that this page allows the user to preview the recipe and indicate whether they are satisfied with the recipe (confirm) or not (disconfirm). To enable a user to make this decision, the main requirement for this page is that it shows the recipe’s name, and what the result of cooking the recipe will look like (a picture of the recipe). A second requirement is that the page shows a microphone button to enable the user to inform the agent about whether they want to (dis)confirm the recipe. Note that at a later stage, we will ask you to extend the recipe confirmation page when you implement [TBU]Filtering by Inclusion .
We have introduced the bare essentials for these pages but you should feel free to modify it in any way you like to make it look more appealing. However, please note that the a50recipeConfirm page is extended later to include recipe information.
It is important that you make sure that the requirements are met.
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The conversational agent at this stage is not able to handle misunderstandings or speech recognition errors. Dialogflow will return a default fallback intent(fallback) (check the intents in your Dialogflow agent) if it is not able to classify speech into one of the intents created. We have not yet introduced a pattern, however, needed for handling such a fallback intent and the agent will therefore get stuck if speech recognition fails. Dialogflow should not have too much trouble with recognizing a simple greeting phrase, however. |
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