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We illustrate an architecture for a conversational agent based on a modular knowledge representation. This solution provides intelligent conversational agents with a dynamic and flexible behavior. The modularity of the architecture allows a concurrent and synergic use of different techniques, making it possible to use the most adequate methodology for the management of a specific characteristic of the domain, of the dialogue, or of the user behavior. We show the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation techniques and capable to differently manage conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, whose task is to choose, time by time, the most adequate chatbot knowledge section to activate.
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Giovanni Pilato
Institute for High Performance Computing and Networking
Agnese Augello
Institute for High Performance Computing and Networking
Salvatore Gaglio
Institute for High Performance Computing and Networking
University of Palermo
Indian Council of Agricultural Research
Institute for High Performance Computing and Networking
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Pilato et al. (Thu,) studied this question.
synapsesocial.com/papers/6a11e94f0db2e61b4b8e1aac — DOI: https://doi.org/10.1109/icsc.2011.68