7,989 research outputs found
From process models to chatbots
The effect of digital transformation in organizations needs to go beyond automation, so that human capabilities are also augmented. A possibility in this direction is to make formal representations of processes more accessible for the actors involved. On this line, this paper presents a methodology to transform a formal process description into a conversational agent, which can guide a process actor through the required steps in a user-friendly conversation. The presented system relies on dialog systems and natural language processing and generation techniques, to automatically build a chatbot from a process model. A prototype tool – accessible online – has been developed to transform a process model in BPMN into a chatbot, defined in Artificial Intelligence Marking Language (AIML), which has been evaluated over academic and industrial professionals, showing potential into improving the gap between process understanding and execution.Peer ReviewedPostprint (author's final draft
Conversational Process Modelling: State of the Art, Applications, and Implications in Practice
Chatbots such as ChatGPT have caused a tremendous hype lately. For BPM
applications, it is often not clear how to apply chatbots to generate business
value. Hence, this work aims at the systematic analysis of existing chatbots
for their support of conversational process modelling as process-oriented
capability. Application scenarios are identified along the process life cycle.
Then a systematic literature review on conversational process modelling is
performed. The resulting taxonomy serves as input for the identification of
application scenarios for conversational process modelling, including
paraphrasing and improvement of process descriptions. The application scenarios
are evaluated for existing chatbots based on a real-world test set from the
higher education domain. It contains process descriptions as well as
corresponding process models, together with an assessment of the model quality.
Based on the literature and application scenario analyses, recommendations for
the usage (practical implications) and further development (research
directions) of conversational process modelling are derived
Dialog-based Automation of Decision Making in Processes
The use of chatbots has spread, generating great
interest in the industry for the possibility of automating
tasks within the execution of their processes. The
implementation of chatbots, however simple, is a
complex endeavor that involves many low-level details,
which makes it a time-consuming and error-prone task.
In this paper we aim at facilitating the development
of decision-support chatbots that guide users or
help knowledge workers to make decisions based
on interactions between different process participants,
aiming at decreasing the workload of human workers,
for example, in healthcare to identify the first
symptoms of a disease. Our work concerns a
methodology to systematically build decision-support
chatbots, semi-automatically, from existing DMN
models. Chatbots are designed to leverage natural
language understanding platforms, such as Dialogflow
or LUIS. We implemented Dialogflow chatbot prototypes
based on our methodology and performed a pilot test
that revealed insights into the usability and appeal of
the chatbots developed
Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time
Crowd-powered conversational assistants have been shown to be more robust
than automated systems, but do so at the cost of higher response latency and
monetary costs. A promising direction is to combine the two approaches for high
quality, low latency, and low cost solutions. In this paper, we introduce
Evorus, a crowd-powered conversational assistant built to automate itself over
time by (i) allowing new chatbots to be easily integrated to automate more
scenarios, (ii) reusing prior crowd answers, and (iii) learning to
automatically approve response candidates. Our 5-month-long deployment with 80
participants and 281 conversations shows that Evorus can automate itself
without compromising conversation quality. Crowd-AI architectures have long
been proposed as a way to reduce cost and latency for crowd-powered systems;
Evorus demonstrates how automation can be introduced successfully in a deployed
system. Its architecture allows future researchers to make further innovation
on the underlying automated components in the context of a deployed open domain
dialog system.Comment: 10 pages. To appear in the Proceedings of the Conference on Human
Factors in Computing Systems 2018 (CHI'18
A virtual diary companion
Chatbots and embodied conversational agents show turn based conversation behaviour. In current research we almost always assume that each utterance of a human conversational partner should be followed by an intelligent and/or empathetic reaction of chatbot or embodied agent. They are assumed to be alert, trying to please the user. There are other applications which have not yet received much attention and which require a more patient or relaxed attitude, waiting for the right moment to provide feedback to the human partner. Being able and willing to listen is one of the conditions for being successful. In this paper we have some observations on listening behaviour research and introduce one of our applications, the virtual diary companion
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