MAS Architectural Model for Dialog Systems with Advancing Conversations

Abstract

Recent handcrafts on dialog manager in task-oriented dialog systems (TODS) offer great promises on handling conversations. However, most tend to be shortsighted in handling advancing conversations. Modelling the future direction on conversations is crucial for TODS that can be scaled across multi-domain. This paper proposes a novel architectural model for the dialog manager, (MAS_DM). In this model, the dialog manager is a MAS. The architecture consists of multiple intelligent interacting agents, namely, state agent, master agent, and dialog agents. Each agent performs a set of tasks to achieve the overall goal of advancing the conversation within a topic. In this paper, the particular component of the Dialogue Manager, and Strategy selection has been discussed in detail. The notion of learning is essential, since it is intended to provide a means to which the agents will adapt to their environment. We show how to combine MAS and RL to enable agents learn a topic of interest and support an advancing conversation on the same. This will enable the realization of advancing conversations between a human and the TODS on a given topic.