This article presents DemaBot: a low-code solution to create chatbots designed to automate decision making. Examples of these chatbots range from COVID-19 screening to first-line incident support,
amongst others. Using DemaBot, the developer only needs to model the decision that the chatbot will
automate using DMN and, optionally, customize the utterances that the chatbot will use to interact with
the user. From this information, DemaBot generates automatically the complete set of components that
implement a ready-to-use chatbot. Furthermore, it provides help to guide users during the conversation,
and optimizes the conversation flow, being able to recognize several parameters in a single turn and
asking only for those that are indispensable for the decision.Ministerio de Ciencia e Innovación OPHELIA (RTI2018101204-B-C22)Junta de Andalucía EKIPMENT-PLUS (P18-FR-2895

Estrada Torres, Bedilia

Resinas Arias de Reyna, Manuel

Río Ortega, Adela del

English

idUS. Depósito de Investigación Universidad de Sevilla

DemaBot: a Tool to Automatically GenerateDecision-Support Chatbots.Bedilia Estrada-Torresa, Adela del-Río-Ortegab and Manuel ResinasbaUniversidad de Sevilla, Seville, SpainbSCORE Lab, I3US Institute. Universidad de Sevilla, Seville, SpainAbstractThis article presents DemaBot: a low-code solution to create chatbots designed to automate decision-making. Examples of these chatbots range from COVID-19 screening to first-line incident support,amongst others. Using DemaBot, the developer only needs to model the decision that the chatbot willautomate using DMN and, optionally, customize the utterances that the chatbot will use to interact withthe user. From this information, DemaBot generates automatically the complete set of components thatimplement a ready-to-use chatbot. Furthermore, it provides help to guide users during the conversation,and optimizes the conversation flow, being able to recognize several parameters in a single turn andasking only for those that are indispensable for the decision.KeywordsDecisions-support chatbot, DMN, Natural language understanding1. IntroductionIn recent years, a large number of organizations have implemented virtual assistants that provideinformation, solve doubts or help in the achievement of a specific task [1]. Often these virtualassistants take the form of chatting bots, also known as chatbots, which are tools designedto interact with users through friendly conversations using natural language in a way thatsimulates interaction with a human [2, 1], and in some scenarios, they can act as teammates ofhuman workers in a collaborative way in complex processes [3].Although chatbot development platforms have abstracted away many details related to thenatural language processing of a chatbot through the automated recognition of user intentsand have provided a framework in which the conversation flow can be defined, a chatbotdeveloper is still required to implement a specific conversation flow, to deal with many low-level details about parameters or entities, to provide a set of training phrases for each of theconversation steps, and to provide a generic set of fallback options that guides users who doProceedings of the Demonstration & Resources Track, Best BPM Dissertation Award, and Doctoral Consortium at BPM2021 co-located with the 19th International Conference on Business Process Management, BPM 2021, Rome, Italy,September 6-10, 2021Envelope-Open iestrada@us.es (B. Estrada-Torres); adeladelrio@us.es (A. del-Río-Ortega); resinas@us.es (M. Resinas)GLOBE https://personal.us.es/iestrada/ (B. Estrada-Torres)Orcid 0000-0001-7943-276X (B. Estrada-Torres); 0000-0003-3089-4431 (A. del-Río-Ortega); 0000-0003-1575-406X(M. Resinas)© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).CEURWorkshopProceedingshttp://ceur-ws.orgISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)not know the capabilities the chatbot has, amongst others. Implementing these tasks is usuallytime-consuming and error-prone even for simple chatbots [4].In this paper, we focus on decision-support chatbots, which can be defined as task-orientedchatbots whose purpose is to help or guide users to make decisions. DemaBot is a web tool thatprovides a low-code solution to create decision-support chatbots1,2,3. To this end, the developeronly needs to model the decision that the chatbot will automate using a DMN [5] model. DMN(Decision Model and Notation) is a standard that provides constructors for modeling decisionsand decoupling them from process models. Based on the DMN model, DemaBot generatesautomatically a ready-to-use chatbot, thus reducing the implementation time of a chatbot andthe possibility of implementation errors. The generated chatbot leverages the natural languageunderstanding (NLU) platform, Dialogflow4. DemaBot also optimizes the conversation flowwith the user to recognize several parameters in a single conversation turn and asks only forthe parameters essential to make the decision, automatically generates training phrases using adataset generator for NLU models, provides help phrases to guide users if necessary, and allowscustomization of questions and answers.2. DemaBot ArchitectureBefore introducing the DemaBot architecture, we introduce DMN decision tables and NLUchatbots. A DMN decision table, shown in Fig. 1a, “is a tabular representation of decision logic,based on a discretization of the possible values of the inputs of a decision, and organized intorules that map discretized input values onto discrete output values”[5]. A NLU chatbot, alsoknown as agent, contains a recognition engine that matches user inputs with intentions (intents)during a conversation turn. It also contains an execution component capable of executingactions for each intent, such as responses to the user. Intents are defined through trainingphrases, which are input examples that allow the recognition engine to identify the differentphrases that a user can utilize to express an intention. In each training phrase, concrete valuescan be recognized. These values are called parameters. The type of the parameters is defined bya specific structure called entity, which determines how data from a user input is extracted. NLUplatforms provide predefined entities that match many common types of data like numbers,dates, e-mail addresses, etc. but also allow custom entities to be defined for enumerated values.A custom entity is composed of a set of entries. Each entry is made up of a reference value anda set of synonyms for that reference. These components are reflected in Fig. 1b.The DemaBot architecture consists of 3 modules illustrated in Fig. 2:• Chatbot Designer supports the definition of the inputs of DemaBot, which are a DMNfile containing a DMN model, and configuration parameters including the customizationof the chatbot conversation and the credentials file associated with a Dialogflow projectwhere the generated chatbot components will be stored. Chatbot designer provides 31Tool is available at: http://resisa01.us.es2Tutorial available at: https://bit.ly/2SRZ0TY3Screencasts is available at: https://youtu.be/kP3X5gxkCSU4DialogFlow: https://dialogflow.cloud.google.com/(a) DMN decision table(b) Interaction between NLU chatbot componentsFigure 1: Examples of background conceptsfunctionalities: (i) the DMN editor integrated with Camunda5 fromwhich a DMNmodel (adecision table or a hierarchical structure of decision tables) can be generated, or an existingDMN file can be imported; (ii) the possibility to customize the chatbot conversation byconfiguring the questions and answers concerning an input in a conversation turn, and(iii) the generation of the chatbot, which requires loading the credentials file and providingthe project ID.• Engine. At design time, this module processes the inputs received from the Designerand interacts with Dialogflow to generate and store all the chatbot components. Enginealso connects with Chatito6 to build the phrases that will train the generated chatbot. Atruntime, Engine interacts with the Interface module and analyzes the inputs received fromthe user to optimize the conversation. Engine has three submodules described below.(i) DMN component extractor and classifier submodule identifies the DMN components(decisions, rules, inputs and outputs) and classifies the expected values and types for eachcomponent from the DMN file. At design time, this data, the credentials file and the projectID are sent to the Component mapper and generator. At run time, the data is sent to theConversation optimizer.(ii) Component mapper and generator submodule receives the classified DMN elementsand identifies the chatbot components to automatically generate for each of them, so ithas a generator for each component. Entities generator creates an entity for each type ofattribute not supported by the platform; Intents generator creates an intent for each DMNinput, as well as generating help intents to give additional information to the user, forexample the list of decisions available to be made, or the types of values expected for aninput; Actions generator specifies the actions to be performed, including the responses tobe sent to the user; Multiple decision manager establishes relationships between decisiontables in hierarchical structures when the output of one table is the input of another;and Training phrases generator uses the Chatito engine to build training phrases that willhelp the user interact with the chatbot. These phrases are automatically generated usingthe parameters extracted from the DMN file and optionally, from user-defined phraseconfiguration parameters.(iii) Conversation optimizer submodule interacts with the extractor and classifier submod-5https://camunda.com/6Chatito is available at: https://rodrigopivi.github.io/Chatito/Figure 2: DemaBot architectureule at runtime to communicate with the Dialogflow platform to recognize the intents,parameters and entities from the user utterances. It analyzes the information providedby DialogFlow to identify the required inputs to make a decision. It also applies anoptimization algorithm to ask for input values only if the input is required depending onthe values captured so far, and recognizes several parameters (inputs) received in a singleuser message, thus minimizing the number of interactions to produce the decision.• Chatbot interface provides an environment for the communication between the userand the generated chatbot. It communicates with the Conversation optimizer to minimizeconversation turns with the user. The chatbot interface can also be used as a chatbot testweb environment to check the behavior of the chatbot while designing the model.3. MaturityDemaBot is the result of several iterations derived from its use in different scenarios and feed-back of a set of pilot users. DemaBot’s first version focused on the semi-automatic componentsgeneration of a fully functional chatbot. It was tested and validated with a set of 17 IT profes-sionals with a scenario based on bank credit decision making. The participants identified theimportance of the tool, and also helped to identify areas for improvement such as: the fullyautomatic generation of all components, the inclusion of hierarchical decision tables definition,the improvement of the definition of help intents to guide the user in the interaction with thechatbot, the optimization of aspects of the conversation so that unnecessary questions are notasked to make a decision. We considered these suggestions and are included in the currentversion. This version has been successfully applied to two scenarios. The first scenario helpsto choose the best design pattern for building Trello boards based on characteristics of thedomain [6]. The second scenario seeks to build a dashboard showing the results of processperformance indicators according to the characteristics of these indicators based on a decisiontree proposed in [7]. In a Master’s Thesis [8] at the University of Seville, a chatbot based on apart of this decision tree was implemented manually, consuming more than 50 hours of work,while with DemaBot it was done in less than an hour.4. Conclusions and Future WorkThis paper presented DemaBot, a web tool for the automatic generation of fully functionaldecision-support chatbots from DMN models. The automatic generation reduces the possibilityof introducing errors during the chatbot construction, as well as drastically reducing the timerequired for its generation. In addition, DemaBot provides additional features such as theautomatic generation of user help responses, the automatic generation of training phrasesfor the chatbot and the optional configuration of phrases to interact with the user, as wellas optimizing user interactions to try to minimize conversational turns and identify multipleinputs in a phrase. As future work we want to address multi-user interaction in conversationalturns for decision making as well as its integration with other collaborative tools.AcknowledgmentsThis work has been partially supported by projects RTI2018-101204-B-C22 (MCI/AEI/FEDER,UE) and P18-FR-2895 (Junta de Andalucía/FEDER, UE).We would like to thank Alejandro Gómez Caballero, member of the development team of theISA group, for his support in the development of DemaBot.References[1] S. Valtolina, B. R. Barricelli, S. D. Gaetano, P. Diliberto, Chatbots and conversational inter-faces: Three domains of use, in: Fifth International Workshop on Cultures of Participationin the Digital Age, volume 2101 of CEUR Workshop Proceedings, 2018, pp. 62–70.[2] S. Hussain, O. A. Sianaki, N. Ababneh, A Survey on Conversational Agents/ChatbotsClassification and Design Techniques, in: Workshops of the 33rd International Conferenceon Advanced Information Networking and Applications, volume 927, 2019, pp. 946–956.[3] I. Seeber, E. A. C. Bittner, R. O. Briggs, et al., Machines as teammates: A research agenda onai in team collaboration, Information & Management 57 (2020) 103174.[4] G. Daniel, J. Cabot, L. Deruelle, M. Derras, Multi-platform Chatbot Modeling and Deploy-ment with the Jarvis Framework, in: 31st Int. Conf. on Advanced Information SystemsEngineering (CAiSE 2019), volume 11483 of LNCS, Springer, 2019, pp. 177–193.[5] I. Object Management Group, Decision Model and Notation (DMN) specification. V1.3,https://www.omg.org/spec/DMN, 2019. Accessed: 03-03-2021.[6] J. Peña, A. Bravo, A. del-Río-Ortega, M. Resinas, A. Ruiz-Cortés, Design patterns for board-based collaborative work management tools, in: 33rd Int. Conf. Advanced InformationSystems Engineering (CAiSE 2021), volume 12751 of LNCS, Springer, 2021, pp. 177–192.[7] Ü. Aksu, A. del-Río-Ortega, M. Resinas, H. A. Reijers, An approach for the automatedgeneration of engaging dashboards, in: OTM, volume 11877 of LNCS, 2019, pp. 363–384.[8] M. D. Gil, Chatbots para cuadros de mando, Master’s thesis, E.T.S.I. Informática, 2020.Webhook Dialogflow: https://github.com/MrManoloDG/friday-chatbot.

DemaBot: a tool to automatically generate decision-support chatbots

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DemaBot: a tool to automatically generate decision-support chatbots

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