A Modular Framework for Versatile Conversational Agent Building

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.

[1]  T. Landauer,et al.  A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. , 1997 .

[2]  Zuhair Bandar,et al.  A Conversational Agent Framework using Semantic Analysis , 2010 .

[3]  Ulrich Thiel,et al.  Can proactive behavior turn chatterbots into conversational agents? , 2005, IEEE/WIC/ACM International Conference on Intelligent Agent Technology.

[4]  Roberto Pirrone,et al.  Awareness Mechanisms for an Intelligent Tutoring System , 2008, AAAI Fall Symposium: Biologically Inspired Cognitive Architectures.

[5]  George Siemens Connectivism: A Learning Theory for the Digital Age , 2004 .

[6]  Giovanni Pilato,et al.  A sub-symbolic approach to word modelling for domain specific speech recognition , 2005, Seventh International Workshop on Computer Architecture for Machine Perception (CAMP'05).

[7]  Giovanni Pilato,et al.  Sub-symbolic Mapping of Cyc Microtheories in Data-Driven "Conceptual" Spaces , 2007, KES.

[8]  Giovanni Pilato,et al.  LSA for Intuitive Chat-Agents Tutoring System , 2005 .

[9]  Flávia de Almeida Barros,et al.  iAIML: a Mechanism to Treat Intentionality in AIML Chatterbots , 2006, 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06).