Learning Observation Models for Dialogue POMDPs
نویسندگان
چکیده
The SmartWheeler project aims at developing an intelligent wheelchair for handicapped people. In this paper, we model the dialogue manager of SmartWheeler in MDP and POMDP frameworks using its collected dialogues. First, we learn the model components of the dialogue MDP based on our previous works. Then, we extend the dialogue MDP to a dialogue POMDP, by proposing two observation models learned from dialogues: one based on learned keywords and the other based on learned intentions. The subsequent keyword POMDP and intention POMDP are compared based on accumulated mean reward in simulation runs. Our experimental results show that the quality of the intention model is significantly higher than the keyword one.
منابع مشابه
Learning the Reward Model of Dialogue POMDPs from Data
Spoken language communication between human and machines has become a challenge in research and technology. In particular, enabling the health care robots with spoken language interface is of great attention. Recently due to uncertainty characterizing dialogues, there has been interest for modelling the dialogue manager of spoken dialogue systems using Partially Observable Markov Decision Proce...
متن کاملCombining POMDPs trained with User Simulations and Rule-based Dialogue Management in a Spoken Dialogue System
Over several years, we have developed an approach to spoken dialogue systems that includes rule-based and trainable dialogue managers, spoken language understanding and generation modules, and a comprehensive dialogue system architecture. We present a Reinforcement Learning-based dialogue system that goes beyond standard rule-based models and computes on-line decisions of the best dialogue move...
متن کاملDialogue Control Algorithm for Ambient Intelligence based on Partially Observable Markov Decision Processes
From the viewpoint of supporting users’ natural dialogue communication with conversational agents, their dialogue management has to determine any agent’s action, based on probabilistic methods derived from noisy data through sensors in the real world. We believe unique Partially Observable Markov Decision Processes (POMDPs) should be applied to such action control systems. The agents must flexi...
متن کاملEffects of user modeling on POMDP-based dialogue systems
Partially observable Markov decision processes (POMDPs) have gained significant interest in research on spoken dialogue systems, due to among many benefits its ability to naturally model the dialogue strategy selection problem under the unreliability in automated speech recognition. However, the POMDP approaches are essentially model-based, and as a result, the dialogue strategy computed from P...
متن کاملPoint-Based Value Iteration for Continuous POMDPs
We propose a novel approach to optimize Partially Observable Markov Decisions Processes (POMDPs) defined on continuous spaces. To date, most algorithms for model-based POMDPs are restricted to discrete states, actions, and observations, but many real-world problems such as, for instance, robot navigation, are naturally defined on continuous spaces. In this work, we demonstrate that the value fu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2012