=Paper=
{{Paper
|id=Vol-2027/paper24
|storemode=property
|title=Modelling Chatbots with a Cognitive System Allows for a Differentiating User Experience
|pdfUrl=https://ceur-ws.org/Vol-2027/paper24.pdf
|volume=Vol-2027
|authors=Eric Michiels
|dblpUrl=https://dblp.org/rec/conf/ifip8-1/Michiels17
}}
==Modelling Chatbots with a Cognitive System Allows for a Differentiating User Experience==
https://ceur-ws.org/Vol-2027/paper24.pdf
Modelling Chatbots with a Cognitive System Allows for a
Differentiating User Experience
Eric Michiels (Executive Architect)
IBM Belgium, Eric_Michiels@be.ibm.com
Abstract. User Experience is a key differentiator in the era of Digital
Disruption. Chatbots are increasingly considered as part of a delighting User
Experience. But only a bright Conversation Modelling effort, which is part of
holistic Enterprise Modelling, can make a Chatbot effective. In addition, best
practices can be applied to achieve or even outperform user expectations.
Thanks to Cognitive Systems and associated modelling tools, effective Chatbot
dialogs can be developed in an agile manner, while respecting the enterprise
strategy.
Keywords : Agile - Artificial Intelligence - Chatbot - Cognitive - Conversation
- Conversational Commerce - Modelling - Dialog - Entity - Intent.
1 Introduction
This paper starts with the definition of Chatbots or Conversation Services and
positions Chatbot Modelling effort within Enterprise Modelling. Next, sample use
cases of Chatbots are explained. With the proliferation of Chatbot projects, it is
possible to mention early best practices together with some key trends. Conversation
Services that are built upon Cognitive Systems with associated services and tools
allow to strike the balance between strategic goals and agility. Watson Services are
used to illustrate this key conclusion.
2 Conversation Modelling within Holistic Enterprise Modelling
2.1 What are Conversation Services?
Conversation Services are a set of algorithms that can act on behalf of an individual or
business and that can mimic human conversation. They are designed to interact with
users primarily through voice or text. A Conversation Service Instance, is often called
a “Bot” or “Chatbot”. Associated industry terms include, but are not limited to:
Software Robot, Virtual Agent, Digital Personal Assistant, Conversation User
Interface, Conversation User Experience, Conversations as a Platform, and more. We
will essentially use two terms interchangeably: Chatbot and Conversation Service.
J. Ralyté, B. Roelens, and S. Demeyer (Eds.):
PoEM 2017 Doctoral Consortium and Industry Track Papers, pp. 70-78, 2017.
Copyright 2017 for this paper by its authors. Copying permitted for private and academic purposes.
There are two main types of Chatbots:
• Chatbots that are designed to serve one or more specific purposes for a business.
You often find them in messaging Apps such as Facebook Messenger, WhatsApp,
Slack, WeChat, and more. For example, you could order flowers from your local
flower shop, through your favorite messaging App.
• Chatbot that are designed to help with a variety of information and other needs,
a.k.a. as Chatbot Platforms. Examples are virtual assistants like Apple Siri, Google
Assistant, Amazon Echo, Amazon Alexa, and Microsoft Cortana, and IBM Cloud.
Conversation Services and Chatbots are transforming how individuals interact and
engage with brands. Conversation Services will probably replace the need for many
stand-alone smartphone Apps that do not offer chat functions. There is no turning
back to this trend! People expect to communicate in a more conversational way -
anytime, anywhere. This will impact both the consumer and enterprise markets.
The top 4 messaging Apps (Facebook, WhatsApp, WeChat, Viber) account over 3
billion users combined! Moreover, the App boom seems to be over when considering
the recent statistics about the number of downloaded Apps per month. Consumers are
fatigued by having to download a different App for each organization. In short,
brands that do not master Conversation Services will have a hard time competing in
this era of digital disruption.
Today, ICT Architects often make a separation between "Systems of Record"
(“SoR”) and “Systems of Engagement” (SoE). An SoR is an ICT system that an
organization relies on to run its core business. It is a rather stable environment, with
highly integrated and consistent data and processes. An SoE is an ICT system which
is used directly by customers, employees and other end users. It evolves rapidly and
"engages" its users in the business processes. Conversation Services belong to latter.
2.2 Chatbot Responses must come in Different Forms
Modern Chatbots that provide the user with the expected experience, allow to answer
in different ways depending on the question addressed. Some examples:
• The question “How do I change my password?”, must guide the user through a
sequence of steps, avoiding the user to study a complex document and a set of
rules;
• The expression “Someone has stolen my credit card”, might ultimately lead to a
transfer to an agent;
• A customer asking, “Where is the nearest store”, might be answered by the launch
of a map with walking or driving directions;
• A client who types in “I need to pay my outstanding invoice”, needs to be guided
through the bill paying application;
• If a prospect says, “I want to cancel my service”, the Chatbot should generate the
necessary steps to present a retention offer;
• When a Platinum customer tells “I need to add my child to my data plan”, the
Telco Chatbot should allow for a cross-sell or up-sell offer;
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• When a consultant is looking for the top 3 documents that allow to make the right
investment decision, the Chatbot should offer these top 3 assets after involving a
knowledge discovery service.
In short, the technology that supports the modelling, development and deployment
of Chatbots, must support different integration paradigms.
2.3 Conversational Commerce
Today, we see five areas where Chatbots are becoming an essential part of the
customer experience:
• Customer Service, adding a Chatbot to an e-Commerce website that automatically
responds to the customers’ most frequently asked questions;
• Mobile Apps, allowing customers to use natural language virtual agents;
• Messaging Channels such as Twitter, Slack, Facebook Messenger, and other
messaging platform Chatbots that interact instantly with channel users;
• Internet-of-Things, allowing connected devices to understand language and
respond to users’ commands;
• Robots empowered with natural language understanding and conversational
capabilities.
These five applications areas make up a new trend, known as “Conversational
Commerce”, based itself on four pillars:
1. Anthropomorphism of IT: the attribution of human characteristics and value
judgements to non-human beings (in this case computer algorithms). Bot
interactions are evolving from “cold” to “warm” with a sufficient level of
Emotional Intelligence! Today there are hotel chains where friendly and gentle
humanoids answer questions from all types of guests.
2. The Concierge Economy: Robots are perfect slaves and will even anticipate human
behavior. Will we be represented in the future by a Bot, as our Alter-Ego?
3. The Battle for Micro-moments: this are short mobile moments with quick
information consumption and potential action (for example while waiting for a
train or bus). Several needs appear during these micro-moments: knowing, buying,
moving or doing. Chatbots will help with supporting these human desires.
4. The Conversational Office, or the application of Robots and Chatbots in the office.
Flavors are the Digital Coworker and the Digital Manager. There are corporations
today where a Bot is member of the Board of Directors.
Conversation Services will become the center of customer engagement, based on
natural language interactions, uniform omni-channel experiences, multi-platform
deployment, business process support, the detection of emotional states and advanced
machine learning capabilities.
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2.4 Conversation Modelling is part of Enterprise Modelling
Enterprise Modelling is generally an “umbrella term” covering different types of
modelling: business modelling, process modelling, service oriented modelling, and
data modelling. We suggest to also include “Conversation Modelling” to this set.
Conversation Modelling in this paper is about creating an outline of a typical
conversation between a human being and a Chatbot. This outline models a dialog,
which is a logical flow linking together a set of intents and entities. The intents are
typical human user conversational expressions such as saying “Hello”, asking for
pricing information, requesting access to an application, issuing a password reset,
saying goodbye and more. The entities are pretty much equivalent to the abstract or
concrete entities in a conceptual data model and are acted upon by the intents, such as
a product, a service, an account, an application, and more. The dialogue models a
typical conversation, but AI technology allows to reduce the number of scenarios to
be modelled drastically. Effective and successful Chatbots can only be created and
deployed after a well-considered Conversation Modelling effort.
3 Chatbots are a flavor of Artificial Intelligence
3.1 Artificial Intelligence and Cognitive Computing
“Artificial Intelligence” (AI) can be defined as “the science of making computers do
things that require intelligence when done by humans.” AI development is primarily
concerned with enabling computers to solve complex problems. You find task-
orientated AI at work today in a variety of places, from recommendation engines to
Natural Language Processing (NLP). Elsewhere AI provides the brainpower behind
the face recognition technology in the computer vision systems in driverless cars and
even the spam filters in your email.
Some of these AI systems use “Machine Learning”. This subset of AI enables
researchers, data scientists, engineers and analysts to construct algorithms that can
learn from, and make predictions based on data. Rather than following a specific set
of rules or instructions, an algorithm is trained to spot patterns in large amounts of
data. “Deep Learning” takes this idea further, processing information in layers where
the result/output from one layer becomes the input for the next one.
Cognitive Computing is close to the idea of artificial intelligence. So, what’s the
difference? An AI and a Cognitive Computing system approach a data intensive task
quite differently. Imagine that both an AI and a Cognitive System need to analyse a
huge database of research documents, patient records, medical reports, and
specialized press articles to determine the treatment for a patient. An AI system will
tell the doctor which actions to take based on its analysis. A Cognitive System
provides information to help the doctor decide. Cognitive Systems are designed to
solve problems the way humans solve problems, by understanding, learning,
reasoning and interacting. Ultimately, AI and Cognitive Systems are “based on the
ability of machines to sense, reason, act and adapt based on learned experience”.
Rather than being explicitly programmed, they learn and reason from their
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interactions with their environment and human beings. A Cognitive Computing
Platform provides “Augmented Intelligence” rather than Artificial Intelligence
Cognitive Computing became famous thanks to the Jeopardy Quiz back in 2011,
where IBM Watson has beaten the top human players. While this quiz demonstrated
the capabilities of a Cognitive System in a spectacular manner it is not representative
any more for the current Cognitive Systems. First, the current generation focuses on
assisted decision making, without replacing human beings. Second, the first Cognitive
System could just not be reproduced and sold to customers as an on-premise solution,
in an affordable and economic justifiable way. Rather, IBM chose to commercially
offer the Cognitive Computing functions under the form of Services available through
the IBM Platform as a Service Cloud.
3.2 The New Partnership
We all will need to get used to a new partnership. As with all paradigms, there will be
some resistance from certain categories of people. But ultimately, we will appreciate
that humans will focus on common sense, morals, imagination, compassion,
abstraction, dreaming and generalization. Cognitive Systems on the other hand, will
provide augmenting services like locating knowledge, pattern identification, natural
language recognition, machine learning and eliminating bias. Their endless capacity
will be of huge value. Chatbot Modelling Tools include Cognitive capabilities, which
make the modelling faster, easier, and more productive. They assist human beings
with staying in control of morals, and as such help with overcoming opposition
against Cognitive Systems.
4 Chatbot Trends and Best Practices
4.1 Some Sample Use Cases of Chatbots
Today Chatbots are gaining increasing adoption across several industries. An office
supply retailing corporation wanted to provide anywhere, anytime service. The “Easy
Button” was deployed, launching a Chatbot, allowing to order supplies from a variety
of devices using voice, text or email.
A company specialized in digital communications, social media, channel
marketing, integrated campaigns, content creation and delivery, is transforming client
applications into Chatbots that users can engage with using natural language.
A bank in Scotland launched a pilot with a Cognitive Chatbot that allows people to
interact with an AI-powered platform. It frees advisors from spending time on simple,
easily-addressed queries so they can help customers with more complex issues and
questions.
A Belgian bank launched a Chatbot for its teenage clients, guiding them through
the banking products and services, using the jargon they are familiar with.
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There are many more examples across multiple sectors: Energy and Utilities,
Media and Entertainment, Telco, HR, Retail Banking, Automotive, Hospitality and
Hotels, IT Helpdesks, to just name a few.
4.2 Chatbot Trends
Conversation Services and Chatbots are being heavily discussed in all media
channels. Expect the hype to continue throughout 2017 and 2018 as vendors add
offerings, the media publishes articles and the social media buzz. While the leading
business benefits sought are “improving user experience” and “increasing
productivity”, there are some more trends to notice:
• Chatbot startups are in early stages of growth, but seem to benefit from relatively
great investments.
• Developers want tools that make it easier for them to model and integrate
Conversation Services with other platforms, including IoT, Mobile, and Cloud.
• While the top early use cases include customer service and support, customer
advice and recommendation, and Conversational Commerce, the leading verticals
include computer and professional services, retail and manufacturing.
• Facebook Messenger and Slack are the most popular Bot platforms; Facebook
Messenger is a leader among consumer Bots and Slack leads for business Bots.
4.3 Barriers for Adoption
We list a few barriers for adoption that were mentioned by user communities:
• Discoverability: there’s not always a simple way to find and begin using Chatbots.
• There is a skills gap: Natural Language Processing is not a core competency in IT
departments. Developer tools and platforms are still nascent.
• Enterprises are very careful of Chatbots causing brand-damaging blunders. Also,
robust connectivity that integrates Chatbot capability into existing infrastructure is
required. Support for multiple languages is a “must” but not always available.
• Consumers are concerned about privacy breaches and security issues.
• A lot of Chatbots disappoint today as they are not necessarily intuitive enough.
4.4 Chatbot Best Practices
Although Chatbot Modelling is still in its infancy, and the first assets are just starting
to appear, we are already able to describe some early best practices. Generally, there
is no one-size-fits-all, so Chatbots are built based on an organization’s specific needs.
• Give the Bot personality. It is an official representative of a brand, so its dialogues
now constitute part of the services and products! When talking about “personality”,
it is also about making a Bot sound less like a machine.
• Choose the features carefully. The more features, the longer it will take to build a
Chatbot, and the more likely it will malfunction. The Bot must be fine-tuned with
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features that add value. Jokes are great, but users will appreciate a Chatbot that can
also answer their concrete questions.
• Make your Chatbot proactive. Many users are not sure how to use them and
sometimes decide not to interact with them at all. To counter this, build a proactive
Chatbot that interacts with relevance with users as soon as they open their chat
window (e.g. browsing products, making a purchase).
• Use the Bot as a customer self-service tool, as this will save time and resources.
This requires that users must have the ability to change their profile information,
update their billing details, view products and services, view their purchase history
and order statuses, process orders, schedule appointments with human
representatives.
• The most successful Bots are used as a part of a holistic online marketing strategy.
They can be used for example as distribution channel for exclusive promotions and
offers, and collecting user information to build buyer personas.
• Connect the Chatbot to e-commerce platforms to take orders and payments via
messages, posts, and comments.
• Optimize the Bot by using in-depth metrics with the goal to track performance.
Aspects of a Chatbot to be tested are its way of invocation, its tone of voice, the
conversation flow, and its self-serving options.
• IT Needs to get involved with Chatbots. The reasons are security (avoid phishing
via Chatbots for example), Chatbot support, budgeting and ownership (Chatbots
cross often the entire customer lifecycle), and integration with enterprise systems.
5 Watson Services for Modelling Conversation Services
In this chapter we describe a Cognitive System for modelling and deploying
Conversation Services or Chatbots, which is accessible as a Watson Conversation
Service. This is a Chatbot Platform in the Cloud.
5.1 IBM Cloud
The IBM Cloud Platform as a Service provides a wealth of Web Services and APIs. It
is an open standard based Cloud platform, that supports the development,
deployment, running and maintenance of Applications and Mobile Apps. Please
consult https://www.ibm.com/cloud/why-ibm.
5.2 Watson Services
An important set of Web Services and corresponding APIs belong to the category
“Watson”. These allow to develop new or enrich existing applications with Cognitive
capabilities. For Chatbot modelling and development, Watson Conversation Service is
the core.
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5.3 Watson Conversation Service
A Watson Conversation Service Instance which can be invoked by one or several
Applications via an API. Watson Conversation Service includes also a modelling tool
that allows for iterative dialog development. The Cognitive aspect is in the fact that
the tool does not need to be trained with all possible options and variations.
The invoking applications can also be Messaging Platforms such a Facebook
Messenger and Slack. Within a Service Instance, one or several Workspaces can be
created. Such a Workspace allows to model entities, intents and dialogs. These
constructs allow to quickly build dialogs for a Chatbot.
Intents are purposes or goals expressed in a customer's input, such as answering a
question or processing a bill payment. By recognizing the intent expressed in a
customer's input, the Conversation service can choose the correct dialog flow for
responding to it. Entities represent a class of object or a data type that is relevant to a
user's purpose. By recognizing the entities that are mentioned in the user's input, the
Conversation service can choose the specific actions to take to fulfill an intent. The
dialog uses the intents and entities that are identified in the user's input, plus context
from the application, to interact with the user and ultimately provide a useful
response. The Cognitive aspect is in the fact that the tool does not need to be trained
with all possible options and variations.
Fig. 1. Example of a modelled dialog, and the built-in agile testing tool.
Watson Conversation Service supports an increasing number of languages:
English, Arabic, French, German, Italian, Spanish, Portuguese, Japanese, Korean,
Chinese, Czech, and Dutch. Watson Conversation Service instances can scale easily
when the number of chatting users increases. The standard pricing model is based on
the number of API calls, making the tool really cost effective.
Watson Conversation Service could be trained by business experts from the
business to cover approximately 80 % of the high frequency questions and associated
dialogs. But for the remaining 20 % of low frequency requests, it might be very
difficult and costly to model for all these possible scenarios. For these use cases, it is
a wise practice to cover the frequent requests with Watson Conversation Service and
cover the remaining less frequent requests with the results of knowledge discovery in
documents and articles, using Watson Discovery Service. For the latter, the end user
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would receive a suggested set of links to relevant information, instead of a to-the-
point answer.
Organizations that deploy a Chatbot, often like to know what the overall
sentiments during the dialogues have been. A tool like Watson Tone Analyzer allows
for this type of analysis, providing insights about the conversation sentiments in an
objective manner, allowing to fine tune the customer experience.
Watson Conversation Service based applications can also be extended with other
Watson Services like Speech-To-Text, Text-to-Speech and Visual Recognition,
contributing to an even more human-like experience.
6 Conclusion
As key contributors to the user experience of today’s Web Applications and Mobile
Apps, Chatbots are gaining continuously ground in enterprises. To ensure that
Chatbots can efficiently solve problems while having a natural feel, the conversation
models should be included in enterprise architecture. A Cognitive Conversational
Service keeps the effort of Conversation Modelling agile, yet leads to an impacting
user Experience which supports the Enterprise Strategy.
7 References
1. Watson HJ. Preparing for the Cognitive Generation of Decision Support. MIS
Quarterly Executive. 2017 Sep 1.
2. Turban E, Outland J, King D, Lee JK, Liang TP, Turban DC. Intelligent (Smart) E-
Commerce. Electronic Commerce 2018 (pp. 249-283).
3. Chamberlin B. Conversational Services and Chatbots Trend Report, 2017 (External
Version).
4. Shimazu H. ExpertClerk: A Conversational Case-Based Reasoning Tool for
Developing Salesclerk Agents in E-Commerce Webshops. Artificial Intelligence
Review. 2002 Dec 1;18(3):223-44.
5. Lucente M. Conversational interfaces for e-commerce applications.
Communications of the ACM. 2000 Sep 1;43(9):59-61.
6. IBM Watson Academy. https://www.watson-academy.info/
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