Publications

@inproceedings{augelloModularFrameworkVersatile2011,

title = {A Modular Framework for Versatile Conversational Agent Building},

author = { Agnese Augello and Mario Scriminaci and Salvatore Gaglio and Giovanni Pilato},

doi = {10.1109/CISIS.2011.95},

isbn = {978-0-7695-4373-4},

year  = {2011},

date = {2011-01-01},

booktitle = {Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011},

pages = {577--582},

abstract = {This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible. textcopyright 2011 IEEE.},

keywords = {Conversational Agents, Intelligent Agents, Knowledge Representation, Ontologies, Semantic Computing, Semantic Spaces},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{pilatoModularArchitectureAdaptive2011,

title = {A Modular Architecture for Adaptive ChatBots},

author = { Giovanni Pilato and Agnese Augello and Salvatore Gaglio},

doi = {10.1109/ICSC.2011.68},

isbn = {978-0-7695-4492-2},

year  = {2011},

date = {2011-01-01},

booktitle = {Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011},

pages = {177--180},

abstract = {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. textcopyright 2011 IEEE.},

keywords = {Chatbots, Conversational Agents, Intelligent Agents, Knowledge Representation, Semantic Computing},

pubstate = {published},

tppubtype = {inproceedings}

}