Cognitively Inspired Components for Social Conversational Agents
Alex Clay Eduardo Alonso Esther Mondragón
Current conversational agents (CA) have seen improvement in conversational
quality in recent years due to the influence of large language models (LLMs)
like GPT3. However, two key categories of problem remain. Firstly there are the
unique technical problems resulting from the approach taken in creating the CA,
such as scope with retrieval agents and the often nonsensical answers of former
generative agents. Secondly, humans perceive CAs as social actors, and as a
result expect the CA to adhere to social convention. Failure on the part of the
CA in this respect can lead to a poor interaction and even the perception of
threat by the user. As such, this paper presents a survey highlighting a
potential solution to both categories of problem through the introduction of
cognitively inspired additions to the CA. Through computational facsimiles of
semantic and episodic memory, emotion, working memory, and the ability to
learn, it is possible to address both the technical and social problems
encountered by CAs.