Subjectivity and Cognitive Biases Modeling for a Realistic and Efficient Assisting Conversational Agent

Conversational agents are a promising way to provide assistance to novice users. After a semantic analysis, natural language requests are transformed into a formal representation the agent is using in conjunction with a model of the application to define the most appropriated reaction. But heuristics associating behaviors to patterns of semantically similar requests often fail to provide a reaction both efficient and realistic when they are only based on purely rational decisions. Therefore, we propose here an architecture for assisting conversational agents based on two notions: heuristics taking into account both rational and subjective parameters (based on a psychological model of the agent), and biases used to model deep personality contraints the agent can´t modify (implemented as modifiers over the messages transmitted by the agent). We illustrate its functioning with typical requests extracted from a corpus of requests to an assisting agent.