11,347 research outputs found
Action Selection for Interaction Management: Opportunities and Lessons for Automated Planning
The central problem in automated planning---action selection---is also a
primary topic in the dialogue systems research community, however, the
nature of research in that community is significantly different from that
of planning, with a focus on end-to-end systems and user evaluations. In
particular, numerous toolkits are available for developing speech-based
dialogue systems that include not only a method for representing states and
actions, but also a mechanism for reasoning and selecting the actions,
often combined with a technical framework designed to simplify the task of
creating end-to-end systems. We contrast this situation with that of
automated planning, and argue that the dialogue systems community could
benefit from some of the directions adopted by the planning community, and
that there also exist opportunities and lessons for automated planning
A tractable DDN-POMDP Approach to Affective Dialogue Modeling for General Probabilistic Frame-based Dialogue Systems
We propose a new approach to developing a tractable affective dialogue model for general probabilistic frame-based dialogue systems. The dialogue model, based on the Partially Observable Markov Decision Process (POMDP) and the Dynamic Decision Network (DDN) techniques, is composed of two main parts, the slot level dialogue manager and the global dialogue manager. Our implemented dialogue manager prototype can handle hundreds of slots; each slot might have many values. A first evaluation of the slot level dialogue manager (1-slot case) showed that with a 95% confidence level the DDN-POMDP dialogue strategy outperforms three simple handcrafted dialogue strategies when the user's action error is induced by stress
Introduction for speech and language for interactive robots
This special issue includes research articles which apply spoken language processing to robots that interact with human users through speech, possibly combined with other modalities. Robots that can listen to human speech, understand it, interact according to the conveyed meaning, and respond represent major research and technological challenges. Their common aim is to equip robots with natural interaction abilities. However, robotics and spoken language processing are areas that are typically studied within their respective communities with limited communication across disciplinary boundaries. The articles in this special issue represent examples that address the need for an increased multidisciplinary exchange of ideas
Model-based Bayesian Reinforcement Learning for Dialogue Management
Reinforcement learning methods are increasingly used to optimise dialogue
policies from experience. Most current techniques are model-free: they directly
estimate the utility of various actions, without explicit model of the
interaction dynamics. In this paper, we investigate an alternative strategy
grounded in model-based Bayesian reinforcement learning. Bayesian inference is
used to maintain a posterior distribution over the model parameters, reflecting
the model uncertainty. This parameter distribution is gradually refined as more
data is collected and simultaneously used to plan the agent's actions. Within
this learning framework, we carried out experiments with two alternative
formalisations of the transition model, one encoded with standard multinomial
distributions, and one structured with probabilistic rules. We demonstrate the
potential of our approach with empirical results on a user simulator
constructed from Wizard-of-Oz data in a human-robot interaction scenario. The
results illustrate in particular the benefits of capturing prior domain
knowledge with high-level rules
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