Jianfeng Mao

Lei Miao, Jianfeng Mao, Christos G. Cassandras

It has been shown that using appropriate channel coding schemes in wireless environments, transmission energy can be significantly reduced by controlling the packet transmission rate. This paper seeks optimal solutions for downlink transmission control problems, motivated by this observation and by the need to minimize energy consumption in real-time wireless networks. Our problem formulation deals with a more general setting than the paper authored by Gamal et. al., in which the MoveRight algorithm is proposed. The MoveRight algorithm is an iterative algorithm that converges to the optimal solution. We show that even under the more general setting, the optimal solution can be efficiently obtained through an approach decomposing the optimal sample path through certain "critical tasks" which in turn can be efficiently identified. We include simulation results showing that our algorithm is significantly faster than the MoveRight algorithm. We also discuss how to utilize our results and receding horizon control to perform on-line transmission scheduling where future task information is unknown.

Bo Zhang, Qingsheng Cai, Jianfeng Mao et al.

Uncertainty plays a central role in spoken dialogue systems. Some stochastic models like Markov decision process (MDP) are used to model the dialogue manager. But the partially observable system state and user intention hinder the natural representation of the dialogue state. MDP-based system degrades fast when uncertainty about a user's intention increases. We propose a novel dialogue model based on the partially observable Markov decision process (POMDP). We use hidden system states and user intentions as the state set, parser results and low-level information as the observation set, domain actions and dialogue repair actions as the action set. Here the low-level information is extracted from different input modals, including speech, keyboard, mouse, etc., using Bayesian networks. Because of the limitation of the exact algorithms, we focus on heuristic approximation algorithms and their applicability in POMDP for dialogue management. We also propose two methods for grid point selection in grid-based approximation algorithms.