A chatbot architecture uses an LLM only to translate free text into typed inputs while a DCR rule graph controls the conversation and issues conclusions.
Declarative Event-Based Workflow as Distributed Dynamic Condition Response Graphs
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abstract
We present Dynamic Condition Response Graphs (DCR Graphs) as a declarative, event-based process model inspired by the workflow language employed by our industrial partner and conservatively generalizing prime event structures. A dynamic condition response graph is a directed graph with nodes representing the events that can happen and arrows representing four relations between events: condition, response, include, and exclude. Distributed DCR Graphs is then obtained by assigning roles to events and principals. We give a graphical notation inspired by related work by van der Aalst et al. We exemplify the use of distributed DCR Graphs on a simple workflow taken from a field study at a Danish hospital, pointing out their flexibility compared to imperative workflow models. Finally we provide a mapping from DCR Graphs to Buchi-automata.
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Hybrid AI for Explainable and Accurate Conversational Agents in eGovernment
A chatbot architecture uses an LLM only to translate free text into typed inputs while a DCR rule graph controls the conversation and issues conclusions.