Virtual conversational agents, or Chatbots, are quickly becoming a ubiquitous element of human-computer interaction, customer-business communications, and entertainment. Natural Language Processing techniques often utilize Machine Learning methodology to train a Chatbot on datasets of English language and dialogue, allowing what seem like natural English responses to emerge from the agent. The dialogue that arises from interactions with a user is rooted in the domain of the dialogue system. Is it possible for a Chatbot to handle conversing in the domain of human-like casual conversation?
I am designing a framework for the construction of Chatbot characters capable of human-like casual conversation that integrates rule-based reasoning and human conversational behaviors into the dialogue management aspects of the agent. This framework operates using an information-state-based dialogue manager called Forward Looking, Reward Seeking (FLoReS). Chatbot characters created using this framework possess conversational goals and behaviors as defined by the author, allowing for these characters to have unique personalities. Integrated into this framework is an explicit representation of memory and knowledge, also defined by the Chatbot author, which allows for the Chatbot character to “remember” information from previous interactions with a user. Chatbot characters built around human conversational abilities can provide a more natural means of interaction with a user when equipped with this “memory.” Interactions with these Chatbot characters can contribute to improved human-computer communication and consumer interaction, and provide alternative and unexplored avenues for storytelling through character dialogue and behavioral design. The paper portion of this project examines background work in conversational agents as well as the motivations and reasoning behind the framework presented. The interactive portion of this project will allow a user to converse with a Chatbot character created using this framework.
