1-Basics

Watson Assistant Lab 1: Cognitive Chatbot Basics

In this lab we'll start to build a chatbot using Watson Assistant. This basic chatbot will recommend a new mobile phone for a user based on their feedback about previous experiences with phones.

Requirements

• IBM Cloud account

Agenda

• Introduction to Watson Assistant
• Setup the Watson Assistant service
• Understanding user intent: create intents and entities
• Building chatbot responses: create a dialog tree
• Test your chatbot
• Adding rich content: sending images

Introduction to Watson Assistant

IBM Watson Assistant allows you to create cognitive chatbots that you can customise for your business, and deploy them across multiple channels to bring help to your customers where and when they need it.

Most chatbots try to mimic human interactions, which can frustrate users when a misunderstanding arises. Watson Assistant is more. It knows when to search for an answer from a knowledge base, when to ask for clarity, and when to direct you to a human.

Anyone can build chatbots with Watson Assistant - it comes pre-trained with industry-relevant content, can make sense of your historical chat or call logs, and has a visual dialog editor. Industry-leading AI powers the underlying natural language models that understand your users and provides training recommendations as you build and run your chatbot. And you have the flexibility to deploy Watson Assistant within your own site, in a mobile app, and via many messaging channels and customer service tools.

Setup the Watson Assistant service

In this section we are going to create a Watson Assistant instance on IBM Cloud, and use it to build a basic chatbot that answers queries about mobile phones.

(1) Log into IBM Cloud and create a Watson Assistant service.

• Click on Catalog, then filter by clicking on AI
• Select Watson Assistant

(2) Create the service with a unique name: we'd suggest something like Watson Assistant-eventname-yourinitials, e.g. Watson Assistant-DSA-GRW

Scroll down and ensure you are using the Lite plan, then hit Create.

(3) Click on Launch Watson Assistant.

Understanding user intent: create intents and entities

(1) The first thing we need to do in Watson Assistant is create a Skill. A skill contains the training data and machine learning logic that enables your chatbot to understand and help users and customers.

Click on Skills in the menu bar, then Create Skill. Enter the name Phone Advisor for your skill, and hit Create dialog skill:

Now we'll go on to create the constructs required for our chatbot: Intents, Entities and Dialogs.

(2) Create the chatbot Intents.

An intent represents the purpose of a user's input. By recognising the intent expressed by a user, Watson Assistant can choose the correct dialog flow to use to respond to it. To plan the intents for your application, you need to consider what your chatbot users might want to do, and what you want your application to be able to handle.

Choosing the correct intent for a user's input is the first step in providing a useful response. The intents you identify for your application will determine the dialog flows you need to create; they also might determine which back-end systems your application needs to integrate with in order to complete customer requests (such as customer databases or payment-processing systems).

Select Create Intent to get started, and create an intent name of greeting. You can add a description for your intent here if you wish.

(3) Once you've hit Create Intent, you need to add multiple user examples of how a user might greet your bot, e.g.:

• hi
• hello
• good morning
• good afternoon
• good evening

Feel free to add a few more variations of your own.

(4) When you've done, click on the arrow at the top left of the screen to go back, then hit Create intent to repeat the process for our next intent named positive:

• Intent name: positive
• Description: Expressing positive opinion about mobile phones
• User examples:
  • Style is important to me
  • I care about looks
  • I like Galaxy phones
  • I love Apple
  • I need good battery life
  • I prefer Google
  • I want a good looking phone
  • I want a great battery
  • The new Samsung looks great

What we are doing here is providing Watson Assistant with examples of what a user might say when they are expressing a positive opinion about a particular phone brand, or the key attribute they are looking for in a phone. As with any machine learning or cognitive service, the more examples you provide - a larger training set - the more accurate your chatbot will be in identifying user intent.

Your positive intent should look something like this:

(5) Repeat this process twice more for the following intents:

• Intent name: negative

• Description: Expressing negative opinion about mobile phones

• User examples:

  • Google sucks
  • Apple is the worst
  • I don't care about style
  • I'm not bothered about battery life
  • Not concerned with looks
  • I do not like iPhones
  • I hate Samsung

• Intent name: newphone

• Description: Expressing intent to buy or get advice about buying a new phone

• User examples:

  • I am interested in buying a new phone
  • I'm looking for a new phone
  • I need a new phone
  • I want advice regarding phones
  • I'd like to replace my phone
  • Can you recommend a phone
  • What's the best phone available

Once you've created these, your Intents screen should look something like this:

(6) Now let's create some Entities.

An entity represents a term or object in the user's input that provides context for a particular intent. If intents represent verbs (something a user wants to do), entities represent nouns (such as the object of, or the context for, an action).

Entities make it possible for a single intent to represent multiple specific actions. For example in our case, the positive intent can be used with entities to recognise positive feeling about different mobile phones or their attributes. So in effect, an entity defines a class of objects, with specific values representing the possible objects in that class.

Select Entities from the menu bar, then Create Entity. Next enter an entity name of brand and hit Create Entity.

(7) Enter a value name of Apple, and then select Show recommendations:

Watson Assistant can also recommend synonyms for your entity values. The recommender finds related synonyms based on contextual similarity extracted from a vast body of existing information, and uses natural language processing techniques to identify words similar to the existing synonyms in your entity value.

As you might expect, synonyms allow us to use multiple values to represent a single value. In this case, we will translate the use of either Apple or iPhone to a @brand value of Apple, so select iphone from the list presented, and then hit Add selected.

Now select Add value to add Apple to your @brand entity.

(8) Repeat the process for these values:

• Google (with synonym pixel)
• Samsung (with synonyms galaxy, j3, a8 and edge)

Note that you can add your own synonyms too. You'll need to do this when adding the Samsung and Google values and their synonyms to your @brand entity, like so:

Feel free to include more phone brands here if you like. When you've finished it should look something like this:

(9) You can also use pattern matching when creating entity values. This is really helpful if you want to pick up entities that have specific formats, e.g. phone numbers or website addresses.

Take a look at the Watson Assistant documentation if you want to know more.

(10) Create another entity, named attribute, with these values:

• battery (with synonym battery life)
• style (with synonyms looks, stylish, fashion)

Building chatbot responses: create a dialog tree

(1) The next step is to create a dialog tree.

A dialog uses the intents and entities that are identified in the user's input, plus context from the application that uses Watson Assistant, to interact with the user and ultimately provide a useful response. Our dialog tree should help the user choose a new mobile phone based on an existing preference or a characteristic important to the user.

(2) Select Dialog from the menu bar, and then hit Create dialog.

(3) Welcome and Anything else nodes will be automatically generated for you. The former is used to initialise the dialog with the user, and the latter catches any user input that we don't provide a specific response for within our chatbot.

Modify the Welcome node so it welcomes the user with the message Hello. I am a mobile phone advisor. How can I help you? by selecting the node, then changing the Then Respond With field.

Now hit the plus sign next to welcome, change the operator from and to or, and add the #greeting intent to If assistant recognizes:

These tests ensure that we will always provide a welcome response when the dialog first starts, or when someone 'greets' the chatbot.

You can leave the Anything Else node with its default settings.

(4) Next, create a Help node by selecting Add Node. Name it Help, and set the response to Can I help you with something else?. We'll use this node to ask the user if they want to ask us another question, after we've satisfied a previous query.

(5) Now we'll construct a New Phone node, which will pick up the user's intent to ask for help with a new phone recommendation. We'll then ask the user what they are looking for in a new phone, and then look for positive or negative responses based on our previously built intents, as well as picking up either a brand name or a phone attribute defined by our entities. Our recommendation response will then differ, and be dependent on the user's feedback.

Start by adding a new node New Phone, which is invoked if the #newphone intent is recognised. Add a couple of responses confirming that the chatbot understands in the Then Respond With field:

• I understand you want to choose a new phone.
• So you'd like some help choosing a new phone.

Now click Set to random. This will randomly present one of these two responses to the user when we pick up the #newphone intent, adding a bit of variety.

(6) Next add a child node to your New Phone node. You can do this by selecting the New phone node and choosing the Add Child Node option at the top of the dialog tree, or by selecting the three dots on the New phone node and then Add Child Node from the popup menu.

Call this child node Ask User Preference, ensure it is called If assistant recognizes true, and enter these two responses:

• What's your current phone like? Does it have features you like or dislike?
• Tell something good or bad about your current phone, or what particular attributes you like in a phone.

Using true in this way ensures we will always execute the logic in a node (i.e. we're not going to test for any conditions). In this case it means we'll send one of these two responses whenever we reach this point in the dialog.

This node is asking the user what they like/dislike about their phone. We'll add some more logic to this soon, but in order to drop into this child node directly from the New Phone node (i.e. without waiting for a further user response), we need to instruct the New Phone node to not wait for any user input.

Select the New phone node and change the And finally option to Skip User Input.

(7) Finally, we need to create four child nodes of the Ask User Preference node that will ultimately decide the chatbot's response to the user's input. When completed it will look like this:

(8) The Brand Positive node should recognise a #positive intent together with a @brand entity - essentially looking for positive feelings about a brand - and then respond with a recommendation.

Add a child node to the Ask User Preference node. When you edit this and each of the other child nodes you are about to add here, you should ensure that you allow for multiple responses by first selecting Customize from the top right, ensure Multiple responses is set to On, then hit Apply.

Now name the node Brand Positive, and ensure it's called If assistant recognizes both a #positive intent and a @brand entity. Then configure the responses like this:

If assistant recognizes	Respond with
@brand:Apple	If you like Apple you could get the iPhone XR. It's pretty cool.
@brand:Google	A Google fan, huh? I'd probably go for the Pixel 3.
@brand:Samsung	If you like Samsung I'd recommend a new Galaxy S9.

After we've provided a response to the user, we want to make sure we ask them if they need any more help. Edit the And finally section so that we Jump to to our Help node. Choose Respond after you've selected Help as the destination node for the jump:

(9) Now create the Brand Negative child node. It should look pretty much like Brand Positive, except it will test for a #negative intent and a @brand entity. You can create this node from scratch as before, or you can Duplicate the Brand Positive node and customise it as described here.

Use responses like these if you get negative sentiment about a brand:

If assistant recognizes	Respond with
@brand:Apple	If you don't like Apple you could go for an Android phone, maybe a Samsung Galaxy S9 or Google Pixel 3.
@brand:Google	If you don't like Google but want to stay with Android, try a Samsung Galaxy S9, or if you fancy a change, maybe a new iPhone XR?
@brand:Samsung	If you want to steer away from Samsung but stay with Android then you could try a Google Pixel 3, or for a change you could go for a new iPhone XR.

(10) For the Attribute Positive child node look for #positive intent and an @attribute entity, using responses similar to these:

If assistant recognizes	Respond with
@attribute:battery	If you need a long battery life then go retro! There's an updated Nokia 3310 out now.
@attribute:style	Beauty is in the eye of the beholder... but the Huawei P20 Pro looks very cool.

Don't forget to Jump to to your Help node again for both of the Brand Negative and Attribute Positive child nodes!

(11) The final child node should be a catch-all for any user input we don't understand in this dialog branch. It should test for anything_else, and respond with I'm not sure I understand.

This time, because we know the user is interested in a phone recommendation but we couldn't classify their feedback regarding their existing phone, instead of jumping to the Help node, we should ask the user to try providing feedback again. We do this by setting And finally to Jump to the Ask User Preference node and Respond.

Test your chatbot

(1) You can test your dialog inside the Watson Assistant application. Select the Try It button at the top right of the screen to enter the dialog tester:

(2) Try and test all of your dialog branches. It'll look something like this:

See how Watson Assistant is picking out the intents and entities it sees in the user input, and responds accordingly.

(3) If you enter something Watson Assistant doesn't recognise - or if it's interpreting it incorrectly - you have the chance to further train Watson. In the example below I've entered My Galaxy is very average. Watson Assistant is currently seeing this as positive, when in reality it's probably a fairly negative statement.

At this point, as I understand that saying something is 'very average' is probably indicative of a negative user intent, we can select #negative from the drop down menu:

When we do this, Watson retrains our skill by adding this text as another example to our #negative intent. After training is complete, a similar user response will now work! If you check your #negative intent you'll also see that the new example has been added to it.

Adding rich content: sending images

You can return responses with multimedia or interactive elements such as images or clickable option buttons to simplify interaction within your chatbot and enhance the user experience.

In addition to the default response type of Text, for which you specify the text to return to the user as a response, the following response types are also supported:

Image: Embeds an image into the response. The source image file must be hosted somewhere and have a URL that you can use to reference it. It cannot be a file that is stored in a directory that is not publicly accessible.

Option: Adds a list of one or more options. When a user clicks one of the options, an associated user input value is sent to the service. We'll add some options to our chatbot later.

Pause: Forces the application to wait for a specified number of milliseconds before continuing with processing. You might use this response type if you need to perform an action that might take some time, for example, if a parent node makes an external API call that takes a few seconds to respond.

Let's add some images to our existing responses.

(1) Select the Brand Positive dialog node, and then the Edit Response icon for the @brand:Apple response.

(2) Select Add response type and then Image from the drop-down menu.

(3) Enter iPhone XR as the Title, use https://bit.ly/2o9LfQY as the Image source, and hit Save. If you enter this URL into a web browser you should see an image of an iPhone XR. You can use any image URL you like here of course - try searching Google Images if you want to use your own.

(4) Test this out by using the Try It tool again. When the user says they are an Apple fan, they now also get an image of the recommended device:

(5) Complete the New Phone dialog by adding images to all responses. You can find your own images to add to your skill, or use the ones listed below.

For the Brand Positive node:

Brand	Title	Image source
Apple	iPhone XR	https://bit.ly/2o9LfQY
Google	Pixel 3	https://bit.ly/2EhAuC1
Samsung	Galaxy S9	https://bit.ly/2zWQlmH

(6) For the Brand Negative node, ensure you display both of the options we are suggesting in our text response to the user, using the same Title and Image source values as above. As an example, the @brand:Apple response should look like this:

(7) For the Attribute Positive node, use these values:

Attribute	Title	Image source
Battery	Nokia 3310	https://bit.ly/2RX48kj
Style	Huawei P20 Pro	https://bit.ly/2BdHME0

(8) Test out your dialog again using the Try It button. Here's how it should look:

Summary

Well done! You've created your first basic chatbot that understands user intent, can pick out entities, and responds differently depending on user feedback, using both text and images. The next lab will show you how to actually get your chatbot in front of your users, using multiple integration points.

If you need to you can download the Watson Assistant skill we've created so far from here.

There's an Import Skill option available whenever you create a new skill - it will be useful when you are backing up, exchanging skills, etc.:

Now go to Lab 2: Chatbot Integrations to get your chatbot working in a live environment.