=Paper=
{{Paper
|id=Vol-3865/07_paper
|storemode=property
|title=Intelligent Human-Computer Interaction in Innovative AI Solutions in Travel and Its Impact on the E-Society (short paper)
|pdfUrl=https://ceur-ws.org/Vol-3865/07_paper.pdf
|volume=Vol-3865
|authors=Olena Shlyakhetko,Solomiia Fedushko,Natalia Kryvinska
|dblpUrl=https://dblp.org/rec/conf/aiia/ShlyakhetkoFK24
}}
==Intelligent Human-Computer Interaction in Innovative AI Solutions in Travel and Its Impact on the E-Society (short paper)==
https://ceur-ws.org/Vol-3865/07_paper.pdf
Intelligent Human-Computer Interaction in Innovative AI
Solutions in Travel and Its Impact on the E-Society⋆
Olena Shlyakhetko1,†, Solomiia Fedushko1,2,∗,† and Natalia Kryvinska1, †
1
Comenius University Bratislava, 82005 Bratislava, Slovakia
2
Lviv Polytechnic National University, 79000 Lviv, Ukraine
Abstract
The use of artificial intelligence to revolutionize travel and tourism has sparked significant interest in recent
years. A chatbot, a computer program that mimics human responses and can understand and communicate
in multiple languages through Natural Language Processing, is a prime example of intelligent Human-
Computer Interaction. Our system, a web application with a backend housing a centralized knowledge
database and a GUI front end accessible via the Internet, is uniquely designed to elevate virtual sightseeing
experiences in the tourism industry. The growing preference for chatbot interactions over traditional
methods is a testament to their potential to be as effective as humans, if not more.
Keywords
AI, chatbot, Natural Language Processing, virtual guide, E-Society, Human-Computer Interaction1
1. Introduction
Artificial intelligence (AI) is becoming increasingly important in our daily lives, as it enhances
our everyday activities, such as finding the quickest route to work. A chatbot, a typical example of
an artificial intelligence system, is one of the most basic and widely used examples of intelligent
Human-Computer Interaction (HCI). A computer program interacts almost like a human, enabling
communication through text or voice and understanding one or more human languages through
Natural Language Processing (NLP) [1]. The system can improve over time by adding more data
through a machine-learning architecture. What's truly remarkable about chatbots is their
adaptability. They can be used in various aspects of life, serving as personal assistants on mobile
devices, providing technical support, and even for online sales. By September 2016, Facebook
Messenger had 30,000 chatbots [3] on its platform, and by 2018, there were over 300,000 active bots,
which indicates the increasing popularity and versatility of chatbots [4]. Google is also developing
AI-powered personal assistants that could help users book or reserve services via telephone. This
system will communicate with a human clerk or receptionist to assist the user in completing a
booking or reservation task, just like a human assistant would.
Due to the worldwide pandemic crisis, traveling has become less comfortable. Statistics show that
with increasing virus infections, the number of people traveling has dropped rapidly [5].
This paper will focus on creating a chatbot to provide a virtual tour guide for sightseeing in Lodz
city. The number of tourists was approximately halved in 2020 compared to 2019 [6]. We aim to help
people explore Lodz from home using a chatbot as a tour guide, enabling them to experience the city
from their homes.
Solutions using artificial intelligence already exist to assist with travel and tourism. These
solutions help people choose destinations and activities and automate processes such as buying
tickets. However, we are aiming for a different solution. We came across an article where someone
discussed an application that allows users to find attractions, read information about them, and plan
3rd Workshop on Artificial Intelligence for Cultural Heritage (AI4CH 2024, https://ai4ch.di.unito.it/), co-located with the 23nd
International Conference of the Italian Association for Artificial Intelligence (AIxIA 2024). 26-28 November 2024, Bolzano, Italy
∗
Corresponding author- Solomiia Fedushko.
†
These authors contributed equally.
olenaivanochko@gmail.com (O. Shlyakhetko); solomiia.s.fedushko@lpnu.ua (S. Fedushko);
natalia.kryvinska@fm.uniba.sk (N. Kryvinska)
0009-0000-7156-4397 (O. Shlyakhetko); 0000-0001-7548-5856 (S. Fedushko); 0000-0003-3678-9229 (N. Kryvinska)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
travel routes between them. Our system is designed to provide a sightseeing experience with a
chatbot virtual assistant that acts as a tourist guide instead of just helping users choose a destination
and travel to it. We selected Lodz as our focus because it is a city dear to our hearts, and we want to
conveniently showcase its true beauty to people. In 2017, Lodz became part of the UNESCO Creative
Cities Network as the City of Film, thanks to its renowned Lodz Film School. The city's tourism
organization describes it as a place of contrasts that captivates at every turn. It is characterized by
its numerous factories, yet it is also very green and diverse, with a touch of avant-garde. Lodz
impresses with its abundance of palaces belonging to former factory owners and surprises with its
artistic installations. There are many free-to-visit places in Lodz listed on a single online portal,
further solidifying the city as our top choice. In our project, we can include all of them and train AI
to suggest one of these places when someone asks about free-to-see attractions.
2. Model
Our sightseeing chatbot system is composed of five main components:
1. Natural Language Understanding (NLU) – responsible for understanding the user's intent
2. Dialog Manager (DM) – responsible for maintaining the dialog state and accessing a knowledge
database
3. Database (DB) – stores information about attractions in Lodz
4. Natural Language Generation (NLG) – generates output for the user based on the data provided
by the DM
5. Chat – a text interface between the user and the chatbot, used to receive commands and display
chatbot responses.
Figure 1: Components Diagram of chatbot model
The application will extract information from user messages using natural language processing,
which consists of five phases. Each phase will be handled by a specific module part [12]. First, the
Lexical Analyzer will divide the text received from the user into sentences and words. The Syntactic
Analyzer will carry out the second phase, which will analyze the relationships among words. Next,
the Semantic Analyzer will focus on the literal meaning of phrases, sentences, and words. Finally,
the program will determine the users' intentions and what they want to see, and this information
will be sent to the next application module.
2.1. Natural Language Understanding module
Figure 2: Components diagram of the Natural Language Understanding module
2.2. Natural Language Generator module
Figure 3: Components diagram of Natural Figure 4: Components diagram of Dialog
Language Generator Manager
After receiving the data (groups of facts about places), the module will select a few and create
sentences that are understandable for the recipient. We need to design AI for each part of this
module. The first AI will be responsible for creating sentences from groups of facts concerning city
attractions or simply responding to another form of user message. The second AI should create a
logically correct paragraph from single sentences and send it to the GUI as a message.
2.3. Dialog Manager
The Dialog Manager (DM) is responsible for managing the flow of the conversation with the user.
It consists of two trainable subcomponents: the Dialog State Tracer and the Policy Learning Module
[13].
The Dialog State Tracker is in charge of creating a dependable snapshot of the conversation's
current state. This snapshot forms the foundation for all system actions. It records the history of user
inputs, system actions, and database query results. Additionally, it generates a vector based on a
feature extracted from the current dialogue state. The Policy Learning module then utilizes this
vector.
The Policy Learning Module chooses specific system actions to help the user reach their goal in
the fewest steps. This module receives a new state (embedding vector) from the Dialog State Tracker
and then performs a new action using implemented Deep Q-Networks [14].
2.4. Database
Literature also serves as a knowledge database. Initially, it may seem like a primary database
containing information about sightseeing locations, history, and photos of Lodz. However, upon
closer examination, additional contextual information about these places is necessary. Users may
inquire about locations near the center of Lodz, requiring data about the surrounding neighborhoods.
For instance, someone might request information about the most popular places, necessitating the
inclusion of a popularity index.
3. Practical implementation and tests
Tests. Chatbot systems must behave as closely to humans as possible to make the conversation
more pleasant for the user. Let us break an example conversation into pieces:
Figure 6: Chatbot's response to a user asking
Figure 5: Example exchange of greetings
about Manufaktura
between user and chatbot
In this scenario, the user initiates the conversation with a greeting, which the Natural Language
Understanding (NLU) component correctly identifies. The Dialog Manager then searches for the best
response, and the Natural Language Generation (NLG) generates the appropriate greeting for the
user. Next, when the user requests specific information (history) about a particular place
(Manufaktura), the NLU identifies the intent. Subsequently, it provides the Dialog Manager with
keywords such as "History" and "Manufaktura," the Dialog Manager queries the database for this
information. The NLG then produces a response for the user.
Figure 7: Chatbot's response to the user's Figure 8: Chatbot's response to the user asking
continuing dialog and asking about Freedom for photos of Piotrkowska Street
Square
In this scenario, the user does not explicitly state their intent by saying, "Show me history."
However, "and" suggests that this phrase refers to a previous user interaction. As a result, the Dialog
Manager is responsible for tracking the dialog state. By doing so, the dialog manager replaces
"Manufaktura" in the previous database query with "Freedom Square." The chatbot determines the
user's intent by asking a question [15]. Thanks to the Dialog Manager system, it understands the
user's meaning by typing "Both" and provides the correct response.
3.1. Technologies Used
Our system is designed to work as a web application with a backend containing a centralized
knowledge database and a GUI front end that can be accessed through the Internet. This approach
allows for easier deployment and better control of the chatbot and the assets it serves.
Figure 9: Simplified diagram of system architecture
We have chosen the Python programming language for the backend, as it is well-suited for
working with artificial intelligence due to the availability of publicly supported libraries for data
analysis and AI. The backend can receive user messages and send appropriate responses through the
network. Deploying the backend to the cloud will make the API easily accessible online. We plan to
use Google Cloud for this purpose. The front end's main tasks include displaying the conversation
to users, accepting user input, and sending messages to the backend. We can create a React.js
application to run in the user's web browser, or we may consider developing a mobile application.
This application can be hosted as a static resource that is accessible online.
4. Conclusions
Our technology is designed to offer the best experience in virtual sightseeing. The growing
number of users who prefer interacting with chatbots instead of traditional communication methods
shows that they can be as effective as humans. With the advancements in AI and machine learning,
it is becoming increasingly difficult to distinguish between a chatbot and a conversation with a
human. The project aimed to develop an application that would act as a virtual tourist guide,
providing information to the user based on received questions. Machine learning will enhance the
accuracy of the information provided over time, thus improving the overall experience.
Acknowledgements
Funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia
under the project No. 09I03-03-V01-000153
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