Explore/agent app/Hybrid AI for Explainable and Accurate Conversational Agents in eGovernment
H

Ilias Chalkidis, Vlad Paul Cosma, Søren Debois, Daniel Hershcovich, Thomas Hildebrandt, Hugo A. Lòpez, Amogh Raina, Konstantinos Varvoutas, Tilman Zuckmantel/Hybrid AI for Explainable and Accurate Conversational Agents in eGovernmentUnknown

We present a so-called Conversational Hybrid AI (CHAI) architecture for building explainable and accurate conversational agents for eGovernment. We exemplify the architecture with a running prototype of a Covid-19 Chatbot based on a governmental guideline directed to citizens. We also describe an ongoing case on case management for supplementary grants for students with disabilities. We use large language models (LLMs) as a bounded conversational interface to a rule-based (symbolic AI) controller that executes a logical model expressing the provisions and obligations of the law and/or guidelines. As logical modelling language we use Dynamic Condition Response (DCR) graphs, a symbolic declarative process-modeling language developed with the aim to be able to express both deontic, defeasible and temporal logic properties, making it suitable for expressing both the rules of the law and the steps of the legal case management processes.

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55 / 100
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70C+
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42C
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  • Learning agentic patterns

◎ Strengths

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  • Open source
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55
Score 55/100
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