Development of Conversational Deliberative Agents Driven by Personality via Fuzzy Outranking Relations

Abstract

Currently, there are many types of conversational agents whose goal is to emulate human behavior. These agents offer more believable conversations when their responses come from a deliberative process that mimics individuals’ character. Conversational agents are mainly used for response selection linguistics and context situated strategies. These approaches usually build rules to find answers in dialogues; however, this is not the best alternative when the communicative intentions are not literal and context dependent. Deliberative Agents can solve these issues and improve their selection process through the integration of preferences and personality in their cognitive process. Hence, this work investigates how to drive the expression of dialogues of a Conversational Deliberative Agent (CDA) through personality and fuzzy outranking relations; for this, it proposes the characterization of context, and corpus through speech acts theory, and also a selection process based on fuzzy outranking relations to compare corpus phrases and context to choose the best response. The main contributions of this work are (1) the agent architecture that integrates preferences and personality of an individual in the response selection cognitive process; (2) a characterization model of speech acting through criteria based on belief, desires, and intentions to define a more human behavior expression; and (3) the use of fuzzy outranking relations to select phrases from a corpus to match dialogue intentions. An experimental design demonstrated the aptitude of the developed CDA to offer quality responses on a tutor application. Also, the results showed the capacity of speech acts to handle contexts in dialogues.