We present evaluation results with human subjects for a novel data-driven approach to Natural Language Generation in spoken dialogue systems. We evaluate a trained Information Presentation (IP) strategy in a deployed tourist-information spoken dialogue system. The IP problem is formulated as statistical decision making under uncertainty using Reinforcement Learning, where both content planning ...

The strategies for interactive characters to select appropriate dialogues remain as an open issue in related research areas. In this paper we propose an approach based on reinforcement learning to learn the strategy of interrogation dialogue from one virtual agent toward another. The emotion variation of the suspect agent is modeled with a hazard function, and the detective agent must learn its...

This paper describes NJFun, a real-time spoken dialogue systemthat-provides users with information about things to d~ in New Jersey. NJFun automatically optimizes its dialogue strategy over time, by using a methodology for applying reinforcement learning to a working dialogue system with human

In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game “Settlers of Catan”. The comparison is based on human subjects playing games against artificial game-playing agents (‘bots’) which implement different negotiation dialogue strategies, using a chat dialogue interface to negotiate trades. Our results suggest that a negotiation ...

This paper reports our research concerning dialogue strategies suitable for adoption by a human computer debating system. In particular, we propose a set of strategic heuristics for a computer to adopt to enable it to function as a dialogue participant. We also consider means of constructing a set of computational agents operationalising the proposed strategy and means of analysing the agent-ge...

This paper reports on user evaluation for a spoken dialogue system and the subsequent redesign of the dialogue strategy. An automatic telephone directory system developed at Philips Research, PADIS, was evaluated using both objective metrics and subjective evaluation techniques. Duration and the occurrence of misunderstanding leading tòdead-ends', were found to be the key factors that kept user...

This paper describes the algorithm used by a multi-strategy dialogue manager (DM) for a speech-based dialogue system and presents the results of an evaluation of the new DM. From its different dialogue strategies the DM is capable of determining the most appropriate strategy based on a set of criteria and the corresponding algorithm. The DM can adopt a number of styles ranging from highly natur...

In this article, reinforcement learning is used to learn an optimal turn-taking strategy for vocal human-machine dialogue. The Orange Labs’ Majordomo dialogue system, which allows the users to have conversations within a smart home, has been upgraded to an incremental version. First, a user simulator is built in order to generate a dialogue corpus which thereafter is used to optimise the turn-t...

We describe a dialogue management approach that adapts dialogue strategies to a changing user model under the constraint that the approach be able to scale up rapidly for intelligent tutoring systems applications. Dialogue strategies are expressed as constraints on how to move through a student model that is composed of linked nodes. By orienting the dialogue to a linked model of the student’s ...

This paper reports on the creation of the multimodal NIMITEK corpus of affected behavior in human-machine interaction and its role in the development of the NIMITEK prototype system. The NIMITEK prototype system is a spoken dialogue system for supporting users while they solve problems in a graphics system. The central feature of the system is adaptive dialogue management. The system dynamicall...