An architecture for fluid real-time conversational agents: Integrating incremental output generation and input processing

Kopp S, van Welbergen H, Yaghoubzadeh R, Buschmeier H. An architecture for fluid real-time conversational agents: Integrating incremental output generation and input processing. Journal on Multimodal User Interfaces. 2014;8:97-108.Embodied conversational agents still do not achieve the fluidity and smoothness of natural conversational interaction. One main reason is that current system often respond with big latencies and in inflexible ways. We argue that to overcome these problems, real-time conversational agents need to be based on an underlying architecture that provides two essential features for fast and fluent behavior adaptation: a close bi-directional coordination between input processing and output generation, and incrementality of processing at both stages. We propose an architectural framework for conversational agents [Artificial Social Agent Platform (ASAP)] providing these two ingredients for fluid real-time conversation. The overall architectural concept is described, along with specific means of specifying incremental behavior in BML and technical implementations of different modules. We show how phenomena of fluid real- time conversation, like adapting to user feedback or smooth turn-keeping, can be realized with ASAP and we describe in detail an example real-time interaction with the implemented system

Generating context-sensitive ECA responses to user barge-in interruptions

We present an Embodied Conversational Agent(ECA) that incorporates a context-sensitive mechanism for handling user barge-in. The affective ECA engages the user in social conversation, and is fully implemented. We will use actual examples of system behaviour to illustrate. The ECA is designed to recognise and be empathetic to the emotional state of the user. It is able to detect, react quickly to, and then follow up with considered responses to different kinds of user interruptions. The design of the rules which enable the ECA to respond intelligently to different types of interruptions was informed by manually analysed real data from human–human dialogue. The rules represent recoveries from interruptions as two-part structures: an address followed by a resumption. The system is robust enough to man- age long, multi-utterance turns by both user and system, which creates good opportunities for the user to interrupt while the ECA is speaking