Abstract
Chatbot applications are increasingly adopted in various domains such as e-commerce or customer services as a direct communication channel between companies and end-users. Multiple frameworks have been developed to ease their definition and deployment. They typically rely on existing cloud infrastructures and artificial intelligence techniques to efficiently process user inputs and extract conversation information. While these frameworks are efficient to design simple chatbot applications, they still require advanced technical knowledge to define complex conversations and interactions. In addition, the deployment of a chatbot application usually requires a deep understanding of the targeted platforms, increasing the development and maintenance costs. In this paper we introduce the Jarvis framework, that tackles these issues by providing a Domain Specific Language (DSL) to define chatbots in a platform-independent way, and a runtime engine that automatically deploys the chatbot application and manages the defined conversation logic. Jarvis is open source and fully available online.
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- 1.
In this article we focus on ML/NLP-based chatbots, but the approach could be extended to alternative recognition techniques.
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A graphical notation sharing the same metamodel is left as further work.
- 3.
DialogFlow uses a default lifespan value of 5 that allows 5 unrecognized user inputs before forgetting the conversation contexts.
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Acknowledgement
This work has been partially funded by the Electronic Component Systems for European Leadership Joint Undertaking under grant management No. 737494 (MegaMRt2 project) and the Spanish government (TIN2016-75944-R project).
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Daniel, G., Cabot, J., Deruelle, L., Derras, M. (2019). Multi-platform Chatbot Modeling and Deployment with the Jarvis Framework. In: Giorgini, P., Weber, B. (eds) Advanced Information Systems Engineering. CAiSE 2019. Lecture Notes in Computer Science(), vol 11483. Springer, Cham. https://doi.org/10.1007/978-3-030-21290-2_12
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