Crowdsourcing techniques have been extensively explored in the past decade, including
task allocation, quality assessment, and so on. Most of professional crowdsourcing platforms …

We present a hierarchical reinforcement learning framework that formulates each task in the
hierarchy as a special type of Markov decision process for which the Bellman equation is …

Axiomatic fully probabilistic design (FPD) of optimal decision rules strictly extends the
decision making (DM) theory represented by Markov decision processes (MDP). This means …

We study the problem of selecting K arms with the highest expected rewards in a stochastic
n-armed bandit game. This problem has a wide range of applications, eg, A/B testing …

We study the problem of online learning in a class of Markov decision processes known
as\emphlinearly solvable MDPs. In the stationary version of this problem, a learner interacts …

Abstract The Thresholding Bandit Problem (TBP) aims to find the set of arms with mean
rewards greater than a given threshold. We consider a new setting of TBP, where in addition …

One of the fundamental issues in crowdsourcing is the trade-off between the number of
workers needed for high-accuracy aggregation and the budget to pay. To save cost, it is …

In the era of Big Data, data entries, even describing the same objects or events, can come
from a variety of sources. There are some sources that typically provide accurate …

摘要过去数十年间, 现代运筹学, 特别是优化理论, 方法和应用有了长足的发展.
本文就运筹与优化多个领域的一些背景知识, 前沿进展和相关技术做了尽可能详尽的概述 …

In this paper we introduce a novel model for computer-aided human-human dialogue. In this
context, the computer aims at improving the outcome of a human-human task-oriented …