A two-tier user simulation model for reinforcement learning of adaptive referring expression generation policies

Within the broad field of spoken dialogue systems, the application of machine-learning approaches to dialogue management strategy design is a rapidly growing research area. The main motivation is the hope of building systems that learn through trial-and-error interaction what constitutes a good dialogue strategy. Training of such systems could in theory be done using human users or using corpora of human–computer dialogue, but in practice the typically vast space of possible dialogue states and strategies cannot be explored without the use of automatic user simulation tools.This requirement for training statistical dialogue models has created an interesting new application area for predictive statistical user modelling and a variety of different techniques for simulating user behaviour have been presented in the literature ranging from simple Markov models to Bayesian networks. The development of reliable user simulation tools is critical to further progress on automatic dialogue management design but it holds many challenges, some of which have been encountered in other areas of current research on statistical user modelling, such as the problem of ‘concept drift’, the problem of combining content-based and collaboration-based modelling techniques, and user model evaluation. The latter topic is of particular interest, because simulation-based learning is currently one of the few applications of statistical user modelling that employs both direct ‘accuracy-based’ and indirect ‘utility-based’ evaluation techniques.In this paper, we briefly summarize the role of the dialogue manager in a spoken dialogue system, give a short introduction to reinforcement-learning of dialogue management strategies and review the literature on user modelling for simulation-based strategy learning. We further describe recent work on user model evaluation and discuss some of the current research issues in simulation-based learning from a user modelling perspective.

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A STUDY OF ROUTING METHODS IN URBAN WIRELESS MESH NETWORKS BASED ON REINFORCEMENT LEARNING

Труды НИИР ◽

10.34832/niir.2021.5.2.001 ◽

2021 ◽

Author(s):

В.Д. ФАМ ◽

Р.В. КИРИЧЕК ◽

А.С. БОРОДИН

Keyword(s):

Artificial Intelligence ◽

Reinforcement Learning ◽

Simulation Model ◽

Network Traffic ◽

Mesh Networks ◽

Mesh Network ◽

Queueing Model ◽

Traffic Routing ◽

Wireless Mesh ◽

Routing Methods

Приведены результаты исследования методов маршрутизации на основе обучения с подкреплением с помощью имитационной модели. Рассмотрена задача маршрутизации сетевого трафика для фрагмента ячеистой сети городского масштаба, управляемой на основе технологий искусственного интеллекта. Представлена модель системы массового обслуживания для изучения процесса маршрутизации, а также обучения выбора маршрута. Имитационная модель фрагмента ячеистой сети разработана в пакете Anylogic и обучается на основе платформы Microsoft Bonsai. The results of the study of network traffic routing methods based on reinforcement learning using a simulation model are presented. The problem of network traffic routing for a fragment of a city-scale mesh network, controlled on the basis of artificial intelligence technologies, is considered. The article presents a queueing model for studying the routing process, as well as learning how to choose a route. The mesh network fragment simulation model was developed in the Anylogic package and is trained on the basis of the Microsoft Bonsai platform.

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