Abstract
The paper illustrates a system that implements a framework, which is oriented to the development of a modular knowledge base for a conversational agent. This solution improves the flexibility of intelligent conversational agents in managing conversations. The modularity of the system grants a concurrent and synergic use of different knowledge representation techniques. According to this choice, it is possible to use the most adequate methodology for managing a conversation for a specific domain, taking into account particular features of the dialogue or the user behavior. We illustrate the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation methodologies and capable of managing different conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, that selects in real time the most adequate chatbot knowledge module to activate.
Highlights
Each module is automatically triggered through a component, named corpus callosum, that selects in real time the most adequate chatbot knowledge module to activate
Research on intelligent systems in last years has been characterized by the growth of the Artificial General Intelligence (AGI) paradigm [1]
In the field of conversational agents, two different knowledge representation approaches are generally used by intelligent systems to extract and manage semantics in natural language: symbolic and subsymbolic
Summary
Research on intelligent systems in last years has been characterized by the growth of the Artificial General Intelligence (AGI) paradigm [1]. Particular interest has been addressed in the development of functional and accessible interfaces between intelligent systems and their users, in order to obtain a satisfactory man-machine interaction In this context, an ambitious goal is the creation of intelligent systems with conversational skills. A particular module, named “corpus callosum,” is dedicated to dynamically switch the different modules It manages their mutual interaction, in order to activate different cognitive skills of the chatbot. This solution provides intelligent conversational agents with a dynamic and flexible behavior that better fits the context of the dialogue [7]. The remaining of the paper is organized as follows: Section 2 gives an overview of related works; Section 3 illustrates the modular architecture; in Section 4 a case of study is described; in Section 5 dialogue examples are reported; Section 6 contains the conclusions
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