Your MARBEL Agent

Here is an overview of how to implement your MARBEL agent step by step.

1. Getting Started with your MARBEL Agent

Summary Description

The first step is to get your conversational agent up and running. There is one ingredient that is still missing, but required before you can run the code provided to you. You need to create your own Dialogflow agent that you will use throughout the project and connect it to the MARBEL agent that manages the dialog (or conversation) between a user and your agent. When you have created this agent, you will be able to run your agent. Next, you will also be asked to put something in the agenda of the MARBEL agent to get things going. And, last but not least, you need to add code for creating an initial start screen (a simple webpage). When you have done this too, you can run your conversational agent and should see the start webpage that you created. Your agent won’t do any talking yet, though. It is just a first step.

Implementation Tasks Overview

1. Set Up Dialogflow Agent

   • Create a Dialogflow agent with default settings (English as the language).

   • Share it with team members by assigning the Developer role.

   • Import the provided zip file for basic setup.

2. Integrate Dialogflow with MARBEL

   • Download the JSON key file for Dialogflow and add it to the agent folder in your repository.

3. Initialize MARBEL Agenda

   • Replace the empty agenda in the dialog_init.mod2g file with the start pattern (agenda([start])).

4. See the Start Page

5. Run and Debug the Agent

   • Test the integration by running the MARBEL agent and verifying state changes in Eclipse’s Debug perspective.

Instructions

Getting Started with your MARBEL Agent

2. Greet, and Self-Identify

Summary Description

Natural conversations are typically opened. Our agent should not just straight away embark on the task of recommending a recipe. It should first secure the attention of its user. Opening a conversation is a basic conversational competence. A common way of opening a conversation is by greeting each other. You should provide your agent with this basic conversational competence too. It should, moreover, be able to introduce itself to its user as in the following conversational pattern. A welcoming webpage should also be created.

Implementation Tasks Overview

Tasks Focused on Dialogue Patterns

1. Implement Basic Greeting Pattern

   • Define the c10 pattern in patterns.pl for the greeting exchange: [agent, greeting] and [user, greeting].

   • Add the condition agentName('') to ensure it is used only when the agent has no name.

2. Add Self-Identification Pattern

   • Extend c10 in patterns.pl to include [agent, selfIdentification].

   • Add not(agentName('')) to ensure it is used only when the agent has a name.

3. Specify Agent Responses

   • Add phrases for greeting and selfIdentification intents in responses.pl.

   • Dynamically retrieve the agent’s name using agentName(Name) to generate self-identification responses.

4. Add Patterns to the Agenda

   • Update dialog_init.mod2g to include c10 in the agenda after start.

Tasks Focused on Visual Support

1. Create a Welcoming Page.

Tasks Focused on Debugging and Testing

1. Test Greeting Patterns

   • Test the basic greeting pattern by running the agent and initiating interaction from the Start page.

   • Set the agent’s name in dialog_init.mod2g and verify self-identification functionality.

2. Test User Response Variations

   • Provide non-greeting inputs to test the agent’s response and inspect session updates in Debug mode.

Instructions

Greet, and Self-Identify

3. Request a Random Recipe Recommendation

Summary Description

The second capability will enable the agent to recommend a recipe when a user does not care (to refine) what kind of recipe they are looking for (see the example conversational pattern below). A new pattern will be introduced to this purpose for selecting a recipe, and a new intent should be added to your Dialogflow agent to enable understanding user requests for recommending “just some recipe”. Quite some work will need to be done for introducing the first code needed for retrieving recipes from the recipe database. The first versions of two new pages will also need to be created. One page for making clear to the user that they should inform the agent about features of recipes that help refine the selection of recipes and clarify what the user is looking for. Another page for asking a user to (dis)confirm that the recipe that the agent has selected and recommends to the user is what the user is looking for.

Implementation Tasks Overview

Tasks Focused on Dialogue Patterns

1. Implement Recipe Selection Pattern

   • Define the a50recipeSelect pattern in patterns.pl with these moves:

     • [agent, specifyGoal]: Agent asks the user what recipe they want.

     • [user, requestRecommendation]: User requests a random recipe.

     • [agent, recommend]: Agent suggests a recipe.

   • Add [agent, insert(a50recipeConfirm)] to direct the agent to the recipe confirmation pattern.

2. Implement Recipe Confirmation Pattern

   • Define the a50recipeConfirm pattern in patterns.pl to allow users to confirm or disconfirm a recipe.

3. Specify Agent Responses

   • Add responses in responses.pl:

     • text(specifyGoal, "..."): Agent asks about recipe preferences.

     • text(recommend, "..."): Agent suggests a random recipe.

Tasks Focused on Recipe Filtering

1. Add Recipes to the Database

   • Each team member adds one favorite recipe to recipe_database.pl, ensuring unique recipe IDs and consistent ingredient names.

2. Implement Recipe Selection Logic

   • Add rules in recipe_selection.pl:

     • currentRecipe(RecipeID): Retrieves the selected recipe from memory.

     • recipeIDs(RecipeIDs): Collects all recipe IDs from the database.

     • recipesFiltered(RecipeIDs): Filters recipes based on user preferences or constraints.

3. Update Conversational Memory

   • Add a rule in dialog_update.mod2g to update the memory with the randomly selected recipe when a user requests a recommendation.

Tasks Focused on Visual Support

1. Design Recipe Overview 1 Page

2. Design Recipe Confirmation Page

Tasks Focused on Debugging and Testing

1. Test Recipe Recommendation

   • Add a50recipeSelect to the agenda in dialog_init.mod2g and verify the agent's ability to:

     • Ask for recipe preferences.

     • Respond with a random recipe recommendation.

2. Test Recipe Confirmation

   • Ensure the agent correctly displays the recommended recipe and allows user confirmation.

Instructions

Request a Recommendation

4. Select Recipes by Name

Summary Description

Instead of leaving it up to the agent to suggest a recipe and choose one out of all remaining recipes, a user also can mention a specific recipe themselves and ask the agent to present that. This means that the Dialogflow agent should have knowledge of all of the recipes that are in the database too. We will make this available to that agent by adding a recipe entity (type). That will also enable the agent to recognize these recipes in user expressions.

Implementation Tasks Overview

Tasks Focused on Dialogue Patterns

1. Add a New Recipe Request Pattern

   • Define a variant of the a50recipeSelect pattern in patterns.pl with these moves:

     • [agent, specifyGoal]: Agent asks what recipe the user wants to cook.

     • [user, recipeRequest]: User requests a specific recipe by name.

     • [agent, recipeChoiceReceipt]: Agent acknowledges the selected recipe.

     • [agent, insert(a50recipeConfirm)]: Move to the confirmation phase.

2. Specify the Agent’s Recipe Acknowledgment Response

   • In responses.pl, define a rule for text(recipeChoiceReceipt, Txt) to construct the response dynamically.

   • Retrieve the recipe name from memory using currentRecipe/1 and construct the acknowledgment (e.g., "Artichoke and pine nut pasta is a great choice!") using string_concat/3.

Tasks Focused on Visual Support

1. Update Visuals for Recipe Request

Tasks Focused on Debugging and Testing

1. Test Recipe Request Handling

   • Add the new pattern to the agent’s agenda in dialog_init.mod2g after the c10 pattern.

   • Test the interaction flow:

     • Agent asks for a recipe.

     • User requests a specific recipe.

     • Agent acknowledges the recipe choice and moves to the confirmation phase.

2. Verify Intent Recognition

   • Check the Dialogflow Training Tool to confirm the recipeRequest intent is recognized.

   • Verify the SIC server logs show the correct intent and transcript.

3. Handle ASR Failures

   • Use the chatbox feature to avoid ASR failures and ensure the agent can handle specific recipe requests reliably.

Instructions

Select Recipes by Name

5. Filter Recipes by Inclusion

Summary Description

Implementation Tasks Overview

Tasks Focused on Filtering Logic

Refine the Recipe Filtering Logic

• Expand the recipesFiltered/3 predicate to handle lists of filters.

• Implement recursive filtering by applying each filter to the recipe list sequentially:

  • Apply the first filter and pass the remaining recipes to the next recursive call.

  • Continue until all filters have been applied.

Define Ingredient-Based Filtering

• Add rules for the hasIngredient/2 predicate:

  • Check if a recipe contains a specific ingredient (e.g., "ginger").

  • Check if a recipe uses an ingredient type (e.g., "meat") by associating it with specific examples (e.g., "chicken").

  • Use the typeIngredient/2 predicate for hierarchical ingredient filtering.

Add Filtering by Cuisine and Meal Type

• Extend the applyFilter/4 predicate to include filtering by:

  • Cuisine: Ensure recipes match a specified cuisine type (e.g., "Italian").

  • Meal Type: Filter recipes based on meal categories (e.g., "dessert").

Support Dietary Restrictions

• Add logic for filtering recipes by dietary restrictions:

  • Implement the diet/2 predicate to check if a recipe satisfies restrictions like vegetarian, vegan, gluten-free, or spicy.

  • Use the ingredientsMeetDiet/2 helper predicate to validate dietary restrictions for all ingredients in a recipe.

Tasks Focused on Dialogue Patterns

Enable Recipe Feature Requests

• Add a new a21featureRequest pattern for user requests to refine filters dynamically.

• Design two variants of the pattern:

  1. Recipe Selection Context: The user requests a feature during the recipe selection phase (a50recipeSelect).

  2. Recipe Confirmation Context: The user requests a feature while reviewing a specific recipe (a50recipeConfirm).

Handle Conflicting Feature Requests

• Use the removeConflicts(Params) action to handle conflicts when a new feature request overrides an existing one.

• Ensure the agent acknowledges conflicts and updates filters accordingly.

Agent Responses

• Implement agent intents and responses:

  • ackFilter: Acknowledge successful application of filters.

  • noRecipesLeft: Inform the user when no recipes match their filters.

  • featureRemovalRequest: Prompt the user to remove conflicting filters.

Tasks Focused on Visual Support

Extend Recipe Overview Pages

• Recipe Overview 1 Page:

  • Show user-requested filters when more than 15 recipes remain.

  • Display only a summary of filters to avoid overwhelming the user.

• Recipe Overview 2 Page:

  • Display recipe titles and images when the filtered list has 15 or fewer recipes.

  • Use visually appealing cards to present recipe details.

Extend the Recipe Confirmation Page

• Add detailed recipe information, including:

  • Cooking instructions.

  • Ingredient list with quantities.

  • Estimated preparation/cooking time.

  • Number of servings.

• Optionally, include a summary of user-applied filters for context.

Tasks Focused on Debugging and Testing

Test Filtering Functionality

• Test the new filtering logic with various scenarios:

  • Combine filters for ingredients, cuisine, and dietary restrictions.

  • Verify that recipes are correctly filtered and conflicts are resolved.

Validate Feature Requests

• Confirm that the agent correctly handles feature requests in both a50recipeSelect and a50recipeConfirm contexts.

• Check the agent’s responses and ensure they align with the updated logic.

Ensure Robust Filtering

• Test edge cases:

  • Apply filters that leave no recipes (e.g., contradictory filters).

  • Apply multiple filters that narrow down to fewer than 16 recipes.

Instructions

Filtering by Inclusion

6. Connecting Your Entire Pipeline

Connect MARBEL with Intent/Slot Classifier and WHISPER remove Dialogflow.

Instructions

Connecting Your Entire Pipeline

7. Unexpected Intents

Summary Description

A conversational agent should be able to handle dialog moves of users that do not necessarily fit into the currently active pattern. Such moves are “unexpected” in the sense that they do not fit into a conversational pattern, but should be expected as users will almost always slightly deviate from a rigidly framed pattern. Typical examples are expressions of appreciation and requests for information about how the agent can assist a user (a capability check). A second type of unexpected move is not due to user behavior but due to failures of speech recognition. Dialogflow will match with a so-called default fallback intent in cases where it is unable to recognize what a user says and cannot classify it as one of the (other) intents of the agent. There is another case that we will look into too where what a user says does not seem to fit into the conversational context. Again, we can capture the “unexpected” in patterns, which will enable the conversational agent to handle them:

Implementation Tasks Overview

Tasks Focused on Repair Patterns

1. Responding to Fallbacks

• Scenario: When the agent cannot recognize a user’s intent (i.e., Dialogflow matches the fallback intent).

  • Example:

    • User: "Have you read The Hobbit?"

    • Agent: "What do you mean?"

• Steps:

  1. Add a b12 pattern in patterns.pl to handle fallback intents.

  2. Define a paraphrase request (paraphraseRequest) as the agent’s response.

  3. Add text responses in responses.pl for the paraphraseRequest intent.

2. Responding to Out-of-Context Intents

• Scenario: When the agent recognizes a user’s intent but finds it irrelevant to the current conversational context.

  • Example:

    • Agent: "What recipe would you like to cook?"

    • User: "Hey there."

    • Agent: "I am not sure what that means in this context."

• Steps:

  1. Add a b13 pattern in patterns.pl:

     • First move: Any user intent.

     • Second move: Agent’s contextMismatch(Intent) response, using the intent as a parameter.

  2. Regulate this pattern in the updateSession.mod2g file as the last fallback option for recognized intents.

  3. Add responses for contextMismatch(Intent) in responses.pl:

     • Use text/3 to customize responses based on the conversational context.

     • Include an acknowledgment of the mismatch and provide guidance on expected user behavior.

Tasks Focused on Enhancing User Interaction

1. Handling User Appreciation

• Scenario: When the user expresses gratitude.

  • Example:

    • User: "Thanks."

    • Agent: "You're welcome."

• Steps:

  1. Add a b42 appreciation pattern in patterns.pl.

  2. Define appreciation (user intent) and appreciationReceipt (agent response) intent labels.

  3. Add responses for appreciationReceipt in responses.pl.

2. Handling Capability Checks

• Scenario: When the user asks what the agent can do.

  • Example:

    • User: "What can you do?"

    • Agent: "I can help you find recipes, filter them by preferences, and guide you through the cooking process."

• Steps:

  1. Add a c30 capability check pattern in patterns.pl:

     • Intent labels: checkCapability (user intent) and describeCapability (agent response).

  2. Define responses for describeCapability in responses.pl:

     • Provide a concise, context-aware summary of the agent’s capabilities.

     • Use text/3 to tailor responses based on the ongoing conversational context.

Tasks Focused on Visual Support

Update Visuals

• Enhance the visuals to support the repair patterns:

  • Fallbacks: Display a friendly prompt asking the user to rephrase their input.

  • Out-of-Context Intents: Provide subtle visual feedback indicating the agent’s confusion and suggest appropriate actions.

  • Capability Checks: Add a visually appealing list of capabilities or icons to accompany the agent’s verbal response.

Tasks Focused on Debugging and Testing

Test Repair Patterns

1. Fallbacks:

   • Test the agent’s ability to respond with a paraphrase request when encountering unrecognized intents.

   • Example: Say random phrases like “the sky is blue” and verify the agent responds appropriately.

2. Out-of-Context Intents:

   • Test responses to user intents that do not align with the current context.

   • Example: Interrupt the recipe selection process with a greeting and ensure the agent provides context-aware feedback.

3. Capability Checks:

   • Test responses to general and context-specific capability inquiries.

   • Example: Ask "What can you do?" at different stages of the conversation.

Instructions

Handle Unexpected Intents

8. Confirm the Chosen Recipe

Summary Description

When users have finished their search for a nice recipe to cook, their choice should be displayed (which we already implemented for Filtering by Inclusion ) and they should be asked to check the details to confirm the recipe is indeed what they would like to cook. Of course, we need to take at least two scenarios into account: (1) a user confirms they are happy with the recipe, or (2) they indicate it is after all not quite what they were looking for. If all is fine and well, the agent should close the conversation by saying farewell. Otherwise, the conversation should move back to the recipe selection stage. The following conversational pattern needs to be implemented:

Implementation Tasks Overview

Tasks Focused on Confirming Recipe Choice

1. Implementing Recipe Confirmation Patterns

• Scenario: After showing a recipe, the agent asks the user to confirm their choice.

  • Example:

    • Agent: "Can you confirm that Chicken Alfredo is the recipe you would like to cook?"

    • User: "Yes." (Alternatively: "No.")

    • Agent: "Great!" (Alternatively: "That is unfortunate. Let's pick another recipe.")

• Steps:

  1. Add three variants of the a50recipeConfirm pattern in patterns.pl:

     • User Confirms: User says "yes" or expresses appreciation. The conversation moves to the farewell phase.

     • User Disconfirms: User says "no." The agent returns to the recipe selection phase (a50recipeSelect).

     • Fallback Handling: Handle unrecognized or ambiguous responses gracefully (optional).

  2. Handle appreciation as a confirmation:

     • In the a50recipeConfirm pattern, treat appreciation as equivalent to confirmation when it occurs in this context.

  3. Add logic for what happens after confirmation:

     • User Confirms: Transition to the c43 farewell pattern.

     • User Disconfirms: Transition back to the a50recipeSelect pattern to allow the user to choose another recipe.

2. Agent Responses for Recipe Confirmation

• Add responses for the recipeCheck intent in responses.pl:

  • For Confirmation:

    • Example: "Great! I’m happy you like this recipe."

  • For Disconfirmation:

    • Example: "That’s unfortunate. Let’s pick another recipe."

• Dynamically insert the recipe name into the confirmation question:

  • Example: "Can you confirm that Chicken Alfredo is the recipe you would like to cook?"

Tasks Focused on Saying Farewell

1. Implementing Farewell Patterns

• Scenario: The agent bids farewell to conclude the conversation.

  • Example:

    • Agent: "Goodbye!"

    • User: "Bye."

    • Agent: "Feel free to start again if you’d like to find more recipes."

• Steps:

  1. Add a c43 pattern in patterns.pl to model the farewell sequence:

     • Agent Goodbye: The agent says goodbye.

     • User Goodbye: The user responds with a farewell.

  2. Add a final topic check:

     • Before ending the conversation, the agent asks if the user wants to find another recipe.

     • Example:

       • Agent: "Is there anything else you’d like to do? Would you like to find another recipe?"

       • If the user confirms: Add [agent, restart] to the pattern to restart the conversation.

2. Agent Responses for Saying Farewell

• Add responses for the farewell intent in responses.pl:

  • Examples:

    • "Goodbye! Have a great day."

    • "Thanks for chatting! See you next time."

• Add responses for the topic check if the user wants to restart:

  • Example: "Okay, let’s start fresh! What recipe are you looking for?"

Tasks Focused on Visual Support

1. Create a Farewell Page

• Purpose: Match the top-level intent c43 to provide a closing webpage that thanks the user and invites them to return.

• Content:

  • A warm farewell message:

    • Example: "Thank you for using Recipe Assistant! We hope you found something delicious."

  • An encouragement to return:

    • Example: "Feel free to start again to explore more recipes."

• Design:

  • Simple, clean layout with visually appealing graphics (e.g., a smiling chef or a plate of food).

  • Prominently display a "Start Over" button (if applicable).

Tasks Focused on Debugging and Testing

1. Test Recipe Confirmation Flow

• Add a50recipeConfirm to patterns.pl but do not add it to the agent’s agenda:

  • This pattern is inserted dynamically after a recipe is selected, so no need to include it in the agenda manually.

• Verify:

  • The agent transitions to the confirmation phase after a recipe is selected.

  • The agent handles "yes," "no," and appreciation responses correctly.

  • The conversation flows back to recipe selection after disconfirmation.

2. Test Farewell Flow

• Add c43 to the agent’s agenda in dialog_init.mod2g.

• Verify:

  • The agent says goodbye and asks if the user wants to restart.

  • If the user confirms restarting, the conversation resets to the initial agenda.

  • If the user declines, the conversation ends gracefully.

Instructions

Confirmation and Closing

9. Removing Filters and Showing Recipes on Demand

Summary Description

The last capability on our list, before we ask you to extend the capabilities of the conversational agent using your own insights and ideas, is about allowing users to remove requests they made before and to allow them to indicate that they are done providing their preferences. You should implement the following patterns:

Implementation Tasks Overview

Tasks Focused on Allowing Users to Finalize Recipe Constraints

1. User Stops Adding Preferences

• Scenario: The user indicates they do not want to add more constraints.

  • Example:

    • Agent: "Can you elaborate on what you're aiming for in your recipe?"

    • User: "I don't want to add anything else."

    • Agent:

      • If ≤100 recipes remain: "OK. Here is a list of recipes that you can choose from."

      • If >100 recipes remain: "Sorry, there are still too many recipes left to show them all. Please add more preferences."

• Steps:

  1. Implement the a21noMoreFilters Pattern:

     • Two Variants:

       • pictureGranted: Triggered when ≤100 recipes remain.

       • pictureNotGranted: Triggered when >100 recipes remain.

  2. Agent Memory Update:

     • If the agent grants the user’s request, add [agent, update(['show'='true'])] to the pattern to update the agent’s memory.

     • Modify the condition for showing the second recipe overview page (html.pl) to check for the memory key-value pair 'show', 'true'.

2. Agent Responses for Finalizing Preferences

• Add responses for the pictureGranted and pictureNotGranted intents in responses.pl:

  • pictureGranted:

    • Example: "Here is a list of recipes that you can choose from."

  • pictureNotGranted:

    • Example: "Sorry, there are still too many recipes left to show them all. Please add more preferences."

• Dynamically calculate the number of remaining recipes using the recipesFiltered/1 predicate to determine which response to use.

Tasks Focused on Removing Filters

1. Completing Filter Removal Responses

• Scenario: The user requests to remove filters, and the agent provides feedback based on the number of remaining recipes.

  • Four Cases:

    1. Large Number of Recipes (>800):

       • Example: "What kind of recipe would you like?"

    2. Moderate Number of Recipes (16–800):

       • Example: "What other preference would you like to add?"

       • Condition: Memory key-value pair 'show', 'true' must not be set.

    3. No Recipes Left (0):

       • Example: "There are no recipes, please remove more requirements."

    4. Small Number of Recipes or Show Command (<16 or show=true):

       • Example: "Here are some recipes that fit your requirements."

• Steps:

  1. Add rules for the featureInquiry intent in responses.pl:

     • Use recipesFiltered/1 to calculate the number of remaining recipes.

     • Use length/2 to count the recipes and apply appropriate conditions.

  2. Handle the show=true memory key condition for small or moderate recipe sets.

2. Debugging the a21removeKeyFromMemory Patterns

• Check the existing patterns in patterns.pl for removing filters (a21removeKeyFromMemory).

• Verify that all filter removal scenarios trigger the correct featureInquiry response.

Tasks Focused on Visual Support

Update Visuals for Recipe Overviews

• Recipe Overview 2 to Recipe Overview 1:

  • When users remove filters or finalize preferences, ensure a seamless transition between pages:

    • Example: From showing recipe titles and images (Overview 2) to just a list of filters (Overview 1).

  • Use dynamic visual cues to inform users of what has changed (e.g., "Filter removed!").

Dynamic Recipe Display

• Modify the second recipe overview page to account for the memory key-value pair 'show', 'true':

  • Show recipes even if there are more than 15 left, provided the user requested to stop adding filters.

Tasks Focused on Debugging and Testing

1. Test Finalizing Preferences

• Verify the agent correctly handles cases where the user stops adding filters:

  • Test with ≤100 and >100 recipes left to ensure the pictureGranted and pictureNotGranted responses trigger appropriately.

  • Confirm the memory update (show=true) triggers the correct recipe overview page.

2. Test Filter Removal

• Test removing filters at different recipe counts:

  • Large Numbers: Prompt the user to add more specific preferences.

  • Moderate Numbers: Suggest adding more preferences unless show=true.

  • No Recipes: Ask the user to remove filters.

  • Small Numbers or Show Command: Display the remaining recipes.

3. Test Visual Transitions

• Ensure smooth transitions between Recipe Overview 1 and Recipe Overview 2:

  • Test with dynamic changes in recipe counts and user actions (e.g., removing filters or finalizing preferences).

Instructions

Remove a Filter and See Recipes on Demand