Statistical natural language processing methods for intelligent process automation

Nowadays, digitization is transforming the way businesses work. Recently, Artificial Intelligence (AI) techniques became an essential part of the automation of business processes: In addition to cost advantages, these techniques offer fast processing times and higher customer satisfaction rates, thus ultimately increasing sales. One of the intelligent approaches for accelerating digital transformation in companies is the Robotic Process Automation (RPA). An RPA-system is a software tool that robotizes routine and time-consuming responsibilities such as email assessment, various calculations, or creation of documents and reports (Mohanty and Vyas, 2018). Its main objective is to organize a smart workflow and therethrough to assist employees by offering them more scope for cognitively demanding and engaging work. Intelligent Process Automation (IPA) offers all these advantages as well; however, it goes beyond the RPA by adding AI components such as Machine- and Deep Learning techniques to conventional automation solutions. Previously, IPA approaches were primarily employed within the computer vision domain. However, in recent times, Natural Language Processing (NLP) became one of the potential applications for IPA as well due to its ability to understand and interpret human language. Usually, NLP methods are used to analyze large amounts of unstructured textual data and to respond to various inquiries. However, one of the central applications of NLP within the IPA domain – are conversational interfaces (e.g., chatbots, virtual agents) that are used to enable human-to-machine communication. Nowadays, conversational agents gain enormous demand due to their ability to support a large number of users simultaneously while communicating in a natural language. The implementation of a conversational agent comprises multiple stages and involves diverse types of NLP sub-tasks, starting with natural language understanding (e.g., intent recognition, named entity extraction) and going towards dialogue management (i.e., determining the next possible bots action) and response generation. Typical dialogue system for IPA purposes undertakes straightforward customer support requests (e.g., FAQs), allowing human workers to focus on more complicated inquiries. In this thesis, we are addressing two potential Intelligent Process Automation (IPA) applications and employing statistical Natural Language Processing (NLP) methods for their implementation. The first block of this thesis (Chapter 2 – Chapter 4) deals with the development of a conversational agent for IPA purposes within the e-learning domain. As already mentioned, chatbots are one of the central applications for the IPA domain since they can effectively perform time-consuming tasks while communicating in a natural language. Within this thesis, we realized the IPA conversational bot that takes care of routine and time-consuming tasks regularly performed by human tutors of an online mathematical course. This bot is deployed in a real-world setting within the OMB+ mathematical platform. Conducting experiments for this part, we observed two possibilities to build the conversational agent in industrial settings – first, with purely rule-based methods, considering the missing training data and individual aspects of the target domain (i.e., e-learning). Second, we re-implemented two of the main system components (i.e., Natural Language Understanding (NLU) and Dialogue Manager (DM) units) using the current state-of-the-art deep-learning architecture (i.e., Bidirectional Encoder Representations from Transformers (BERT)) and investigated their performance and potential use as a part of a hybrid model (i.e., containing both rule-based and machine learning methods). The second part of the thesis (Chapter 5 – Chapter 6) considers an IPA subproblem within the predictive analytics domain and addresses the task of scientific trend forecasting. Predictive analytics forecasts future outcomes based on historical and current data. Therefore, using the benefits of advanced analytics models, an organization can, for instance, reliably determine trends and emerging topics and then manipulate it while making significant business decisions (i.e., investments). In this work, we dealt with the trend detection task – specifically, we addressed the lack of publicly available benchmarks for evaluating trend detection algorithms. We assembled the benchmark for the detection of both scientific trends and downtrends (i.e., topics that become less frequent overtime). To the best of our knowledge, the task of downtrend detection has not been addressed before. The resulting benchmark is based on a collection of more than one million documents, which is among the largest that has been used for trend detection before, and therefore, offers a realistic setting for the development of trend detection algorithms.Robotergesteuerte Prozessautomatisierung (RPA) ist eine Art von Software-Bots, die manuelle menschliche Tätigkeiten wie die Eingabe von Daten in das System, die Anmeldung in Benutzerkonten oder die Ausführung einfacher, aber sich wiederholender Arbeitsabläufe nachahmt (Mohanty and Vyas, 2018). Einer der Hauptvorteile und gleichzeitig Nachteil der RPA-bots ist jedoch deren Fähigkeit, die gestellte Aufgabe punktgenau zu erfüllen. Einerseits ist ein solches System in der Lage, die Aufgabe akkurat, sorgfältig und schnell auszuführen. Andererseits ist es sehr anfällig für Veränderungen in definierten Szenarien. Da der RPA-Bot für eine bestimmte Aufgabe konzipiert ist, ist es oft nicht möglich, ihn an andere Domänen oder sogar für einfache Änderungen in einem Arbeitsablauf anzupassen (Mohanty and Vyas, 2018). Diese Unfähigkeit, sich an veränderte Bedingungen anzupassen, führte zu einem weiteren Verbesserungsbereich für RPAbots – den Intelligenten Prozessautomatisierungssystemen (IPA). IPA-Bots kombinieren RPA mit Künstlicher Intelligenz (AI) und können komplexe und kognitiv anspruchsvollere Aufgaben erfüllen, die u.A. Schlussfolgerungen und natürliches Sprachverständnis erfordern. Diese Systeme übernehmen zeitaufwändige und routinemäßige Aufgaben, ermöglichen somit einen intelligenten Arbeitsablauf und befreien Fachkräfte für die Durchführung komplizierterer Aufgaben. Bisher wurden die IPA-Techniken hauptsächlich im Bereich der Bildverarbeitung eingesetzt. In der letzten Zeit wurde die natürliche Sprachverarbeitung (NLP) jedoch auch zu einem der potenziellen Anwendungen für IPA, und zwar aufgrund von der Fähigkeit, die menschliche Sprache zu interpretieren. NLP-Methoden werden eingesetzt, um große Mengen an Textdaten zu analysieren und auf verschiedene Anfragen zu reagieren. Auch wenn die verfügbaren Daten unstrukturiert sind oder kein vordefiniertes Format haben (z.B. E-Mails), oder wenn die in einem variablen Format vorliegen (z.B. Rechnungen, juristische Dokumente), dann werden ebenfalls die NLP Techniken angewendet, um die relevanten Informationen zu extrahieren, die dann zur Lösung verschiedener Probleme verwendet werden können. NLP im Rahmen von IPA beschränkt sich jedoch nicht auf die Extraktion relevanter Daten aus Textdokumenten. Eine der zentralen Anwendungen von IPA sind Konversationsagenten, die zur Interaktion zwischen Mensch und Maschine eingesetzt werden. Konversationsagenten erfahren enorme Nachfrage, da sie in der Lage sind, eine große Anzahl von Benutzern gleichzeitig zu unterstützen, und dabei in einer natürlichen Sprache kommunizieren. Die Implementierung eines Chatsystems umfasst verschiedene Arten von NLP-Teilaufgaben, beginnend mit dem Verständnis der natürlichen Sprache (z.B. Absichtserkennung, Extraktion von Entitäten) über das Dialogmanagement (z.B. Festlegung der nächstmöglichen Bot-Aktion) bis hin zur Response-Generierung. Ein typisches Dialogsystem für IPA-Zwecke übernimmt in der Regel unkomplizierte Kundendienstanfragen (z.B. Beantwortung von FAQs), so dass sich die Mitarbeiter auf komplexere Anfragen konzentrieren können. Diese Dissertation umfasst zwei Bereiche, die durch das breitere Thema vereint sind, nämlich die Intelligente Prozessautomatisierung (IPA) unter Verwendung statistischer Methoden der natürlichen Sprachverarbeitung (NLP). Der erste Block dieser Arbeit (Kapitel 2 – Kapitel 4) befasst sich mit der Impementierung eines Konversationsagenten für IPA-Zwecke innerhalb der E-Learning-Domäne. Wie bereits erwähnt, sind Chatbots eine der zentralen Anwendungen für die IPA-Domäne, da sie zeitaufwändige Aufgaben in einer natürlichen Sprache effektiv ausführen können. Der IPA-Kommunikationsbot, der in dieser Arbeit realisiert wurde, kümmert sich ebenfalls um routinemäßige und zeitaufwändige Aufgaben, die sonst von Tutoren in einem Online-Mathematikkurs in deutscher Sprache durchgeführt werden. Dieser Bot ist in der täglichen Anwendung innerhalb der mathematischen Plattform OMB+ eingesetzt. Bei der Durchführung von Experimenten beobachteten wir zwei Möglichkeiten, den Konversationsagenten im industriellen Umfeld zu entwickeln – zunächst mit rein regelbasierten Methoden, unter Bedingungen der fehlenden Trainingsdaten und besonderer Aspekte der Zieldomäne (d.h. E-Learning). Zweitens haben wir zwei der Hauptsystemkomponenten (Sprachverständnismodul, Dialog-Manager) mit dem derzeit fortschrittlichsten Deep Learning Algorithmus reimplementiert und die Performanz dieser Komponenten untersucht. Der zweite Teil der Doktorarbeit (Kapitel 5 – Kapitel 6) betrachtet ein IPA-Problem innerhalb des Vorhersageanalytik-Bereichs. Vorhersageanalytik zielt darauf ab, Prognosen über zukünftige Ergebnisse auf der Grundlage von historischen und aktuellen Daten zu erstellen. Daher kann ein Unternehmen mit Hilfe der Vorhersagesysteme z.B. die Trends oder neu entstehende Themen zuverlässig bestimmen und diese Informationen dann bei wichtigen Geschäftsentscheidungen (z.B. Investitionen) einsetzen. In diesem Teil der Arbeit beschäftigen wir uns mit dem Teilproblem der Trendprognose – insbesondere mit dem Fehlen öffentlich zugänglicher Benchmarks für die Evaluierung von Trenderkennungsalgorithmen. Wir haben den Benchmark zusammengestellt und veröffentlicht, um sowohl Trends als auch Abwärtstrends zu erkennen. Nach unserem besten Wissen ist die Aufgabe der Abwärtstrenderkennung bisher nicht adressiert worden. Der resultierende Benchmark basiert auf einer Sammlung von mehr als einer Million Dokumente, der zu den größten gehört, die bisher für die Trenderkennung verwendet wurden, und somit einen realistischen Rahmen für die Entwicklung von Trenddetektionsalgorithmen bietet

Augmented Business Process Management Systems: A Research Manifesto

Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems that draws upon trustworthy AI technology. An ABPMS enhances the execution of business processes with the aim of making these processes more adaptable, proactive, explainable, and context-sensitive. This manifesto presents a vision for ABPMSs and discusses research challenges that need to be surmounted to realize this vision. To this end, we define the concept of ABPMS, we outline the lifecycle of processes within an ABPMS, we discuss core characteristics of an ABPMS, and we derive a set of challenges to realize systems with these characteristics.Comment: 19 pages, 1 figur

Customisable chatbot as a research instrument

Abstract. Chatbots are proliferating rapidly online for a variety of different purposes. This thesis presents a customisable chatbot that was designed and developed as a research instrument for online customer interaction research. The developed chatbot facilitates creation of different bot personas, data management tools, and a fully functional online chat user interface. Customer-facing bots in the system are rulebased, with basic input processing and text response selection based on best match. The system uses its own database to store user-chatbot dialogue history. Further, bots can be assigned unique dialogue scripts and their profiles can be customised concerning name, description and profile image. In the presented validation studies, participants completed a task by taking part in a conversation with different bots, as hosted by the system and invoked through distinct URL parameters. Second, the participants filled in a questionnaire on their experience with the bot, designed to reveal differences in how the bots were perceived. Our results suggest that the chatbot’s personality impacted how customers experienced the interactions. Therefore, the developed system can facilitate research scenarios that deal with investigating participant responses to different chatbot personas. Future work is necessary for a wider range of applications and enhanced response control.Personoitava chatbot tutkimustyökaluna. Tiivistelmä. Chatbotit yleistyvät nopeasti Internetissä ja niitä käytetään enenevissä määrin useissa eri käyttötarkoituksissa. Tämä diplomityö esittelee personoitavan chatbotin, joka on kehitetty tutkimustyökaluksi verkon yli tapahtuvaan vuorovaikutustutkimukseen. Kehitetty chatbot sisältää erilaisten bottipersoonien luonnin, apuvälineitä datan käsittelyn, ja itse botin käyttöliittymän. Järjestelmän käyttäjille vastailevat bottipersoonat ovat sääntöihin perustuvia, niiden syötteet käsitellään suoraviivaisesti ja vastaukseksi valitaan vertailun mukaan paras ennaltamääritellyn skriptin mukaisesti. Järjestelmä käyttää omaa tietokantaa tallentamaan käyttäjä-botti keskusteluhistorian. Lisäksi boteille voidaan asettaa uniikki dialogimalli, ja niiden profiilista voidaan personoida URL-parametrillä nimi, botin kuvaus ja profiilikuva. Chatbotin tekninen toiminta todettiin tutkimuksella, jossa osallistujat suorittivat annetun tehtävän seuraamalla osittain valmista käsikirjoitusta eri bottien kanssa. Tämän jälkeen osallistujat täyttivät käyttäjäkyselyn liittyen heidän kokemukseensa botin kanssa. Kysely oli suunniteltu paljastamaan mahdolliset eroavaisuudet siinä, kuinka botin käyttäytyminen miellettiin keskustelun aikana. Käyttäjätestin tulokset viittaavat siihen, että chatbotin persoonalla oli vaikutus käyttäjien kokemukseen. Kehitetty järjestelmä siis pystyy mahdollistamaan tutkimusasetelmia, joissa tutkitaan osallistujien reaktioita erilaisten chattibottien persooniin. Jatkotyö kehitetyn chatbotin yhteydessä keskittyy monimutkaisempien käyttötarkoitusten lisäämiseen ja botin vastausten parantamiseen edistyksellisemmän luonnollisen kielen käsittelyn avulla

The Defense Mapping Agency's Navigation Information Network

More than a decade ago, the Defense Mapping Agency made a commitment to improve the means of processing, managing, eind producing navigation safety publications and information using automation to the fullest extent possible. As the present Automated Notice to Mariners System (ANMS) developed and matured, it became apparent that the future of dissemination of these data lay in telecommunications, thus the creation of the Navigation Information Network (NAV1NFONET). This paper will review the history, design, and use of the ANMS and then discuss the present and future utility of the NAVINFONET. As the age of the Electronic Chart Display and Information System (ECDIS) approaches reality, the potential of the NAVINFONET as the only functional existing system to support corrections to ECDIS at sea may well prove its greatest value. In the interim, its worth is proven daily by the myriad of users who seek up-to-date marine safety information to correct their charts and publications far in advance of receipt of the printed word through the mails

Advancements in AI-driven multilingual comprehension for social robot interactions: An extensive review

In the digital era, human-robot interaction is rapidly expanding, emphasizing the need for social robots to fluently understand and communicate in multiple languages. It is not merely about decoding words but about establishing connections and building trust. However, many current social robots are limited to popular languages, serving in fields like language teaching, healthcare and companionship. This review examines the AI-driven language abilities in social robots, providing a detailed overview of their applications and the challenges faced, from nuanced linguistic understanding to data quality and cultural adaptability. Last, we discuss the future of integrating advanced language models in robots to move beyond basic interactions and towards deeper emotional connections. Through this endeavor, we hope to provide a beacon for researchers, steering them towards a path where linguistic adeptness in robots is seamlessly melded with their capacity for genuine emotional engagement

Applications of Affective Computing in Human-Robot Interaction: state-of-art and challenges for manufacturing

The introduction of collaborative robots aims to make production more flexible, promoting a greater interaction between humans and robots also from physical point of view. However, working closely with a robot may lead to the creation of stressful situations for the operator, which can negatively affect task performance. In Human-Robot Interaction (HRI), robots are expected to be socially intelligent, i.e., capable of understanding and reacting accordingly to human social and affective clues. This ability can be exploited implementing affective computing, which concerns the development of systems able to recognize, interpret, process, and simulate human affects. Social intelligence is essential for robots to establish a natural interaction with people in several contexts, including the manufacturing sector with the emergence of Industry 5.0. In order to take full advantage of the human-robot collaboration, the robotic system should be able to perceive the psycho-emotional and mental state of the operator through different sensing modalities (e.g., facial expressions, body language, voice, or physiological signals) and to adapt its behaviour accordingly. The development of socially intelligent collaborative robots in the manufacturing sector can lead to a symbiotic human-robot collaboration, arising several research challenges that still need to be addressed. The goals of this paper are the following: (i) providing an overview of affective computing implementation in HRI; (ii) analyzing the state-of-art on this topic in different application contexts (e.g., healthcare, service applications, and manufacturing); (iii) highlighting research challenges for the manufacturing sector

What makes a social robot good at interacting with humans?

This paper discusses the nuances of a social robot, how and why social robots are becoming increasingly significant, and what they are currently being used for. This paper also reflects on the current design of social robots as a means of interaction with humans and also reports potential solutions about several important questions around the futuristic design of these robots. The specific questions explored in this paper are: “Do social robots need to look like living creatures that already exist in the world for humans to interact well with them?”; “Do social robots need to have animated faces for humans to interact well with them?”; “Do social robots need to have the ability to speak a coherent human language for humans to interact well with them?” and “Do social robots need to have the capability to make physical gestures for humans to interact well with them?”. This paper reviews both verbal as well as nonverbal social and conversational cues that could be incorporated into the design of social robots, and also briefly discusses the emotional bonds that may be built between humans and robots. Facets surrounding acceptance of social robots by humans and also ethical/moral concerns have also been discussed

What makes a social robot good at interacting with humans?

This paper discusses the nuances of a social robot, how and why social robots are becoming increasingly significant, and what they are currently being used for. This paper also reflects on the current design of social robots as a means of interaction with humans and also reports potential solutions about several important questions around the futuristic design of these robots. The specific questions explored in this paper are: “Do social robots need to look like living creatures that already exist in the world for humans to interact well with them?”; “Do social robots need to have animated faces for humans to interact well with them?”; “Do social robots need to have the ability to speak a coherent human language for humans to interact well with them?” and “Do social robots need to have the capability to make physical gestures for humans to interact well with them?”. This paper reviews both verbal as well as nonverbal social and conversational cues that could be incorporated into the design of social robots, and also briefly discusses the emotional bonds that may be built between humans and robots. Facets surrounding acceptance of social robots by humans and also ethical/moral concerns have also been discussed