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Zehong Hu

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9 papers
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9

IJCAI Conference 2019 Conference Paper

CRSRL: Customer Routing System Using Reinforcement Learning

  • Chong Long
  • Zining Liu
  • Xiaolu Lu
  • Zehong Hu
  • Yafang Wang

Allocating resources to customers in the customer service is a difficult problem, because designing an optimal strategy to achieve an optimal trade-off between available resources and customers' satisfaction is non-trivial. In this paper, we formalize the customer routing problem, and propose a novel framework based on deep reinforcement learning (RL) to address this problem. To make it more practical, a demo is provided to show and compare different models, which visualizes all decision process, and in particular, the system shows how the optimal strategy is reached. Besides, our demo system also ships with a variety of models that users can choose based on their needs.

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AAMAS Conference 2019 Conference Paper

Fraud Regulating Policy for E-Commerce via Constrained Contextual Bandits

  • Zehong Hu
  • Zhen Wang
  • Zhao Li
  • Shichang Hu
  • Shasha Ruan
  • Jie Zhang

Fraud sellers in e-commerce often promote themselves via fake visits or purchases to increase sales, jeopardizing the business environment of the platform. How to regulate the exposure of these sellers to buyers without affecting normal online business remains a challenging problem, since blocking them entirely without discrimination may kill the normal transactions and could potentially decrease the total transactions of the platform. To address this problem, we introduce a regulating valve which blocks fraud sellers with a certain probability. To learn the optimal blocking policy, we model the regulating valve as a contextual bandit problem with a constraint on the total transaction decline. Since existing bandit algorithms are unable to incorporate the transaction constraint, we propose a novel bandit algorithm, which decides the policy based on a set of neural networks and iteratively updates the neural networks with online observations and the constraint. Experiments on synthetic data and one of the largest e-commerce platforms in the world both show that our algorithm effectively and efficiently outperforms existing bandit algorithms by a large margin.

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AAAI Conference 2019 Conference Paper

General Robustness Evaluation of Incentive Mechanism against Bounded Rationality Using Continuum-Armed Bandits

  • Zehong Hu
  • Jie Zhang
  • Zhao Li

Incentive mechanisms that assume agents to be fully rational, may fail due to the bounded rationality of agents in practice. It is thus crucial to evaluate to what extent mechanisms can resist agents’ bounded rationality, termed robustness. In this paper, we propose a general empirical framework for robustness evaluation. One novelty of our framework is to develop a robustness formulation that is generally applicable to different types of incentive mechanisms and bounded rationality models. This formulation considers not only the incentives to agents but also the performance of mechanisms. The other novelty lies in converting the empirical robustness computation into a continuum-armed bandit problem, and then developing an efficient solver that has theoretically guaranteed error rate upper bound. We also conduct extensive experiments using various mechanisms to verify the advantages and practicability of our robustness evaluation framework.

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IJCAI Conference 2018 Conference Paper

A Novel Strategy for Active Task Assignment in Crowd Labeling

  • Zehong Hu
  • Jie Zhang

Active learning strategies are often used in crowd labeling to improve task assignment. However, these strategies require prohibitive computation time yet still cannot improve the assignment to the utmost, because they simply evaluate each possible assignment and then greedily select the optimal one. In this paper, we first derive an efficient algorithm for assignment evaluation. Then, to overcome the uncertainty of labels, we develop a novel strategy that modulates the scope of the greedy task assignment with posterior uncertainty and keeps the evaluation optimistic. The experiments on two popular worker models and four MTurk datasets show that our strategy achieves the best performance and highest computation efficiency.

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NeurIPS Conference 2018 Conference Paper

Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing

  • Zehong Hu
  • Yitao Liang
  • Jie Zhang
  • Zhao Li
  • Yang Liu

Incentive mechanisms for crowdsourcing are designed to incentivize financially self-interested workers to generate and report high-quality labels. Existing mechanisms are often developed as one-shot static solutions, assuming a certain level of knowledge about worker models (expertise levels, costs for exerting efforts, etc. ). In this paper, we propose a novel inference aided reinforcement mechanism that acquires data sequentially and requires no such prior assumptions. Specifically, we first design a Gibbs sampling augmented Bayesian inference algorithm to estimate workers' labeling strategies from the collected labels at each step. Then we propose a reinforcement incentive learning (RIL) method, building on top of the above estimates, to uncover how workers respond to different payments. RIL dynamically determines the payment without accessing any ground-truth labels. We theoretically prove that RIL is able to incentivize rational workers to provide high-quality labels both at each step and in the long run. Empirical results show that our mechanism performs consistently well under both rational and non-fully rational (adaptive learning) worker models. Besides, the payments offered by RIL are more robust and have lower variances compared to existing one-shot mechanisms.

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AAMAS Conference 2017 Conference Paper

Automatic Construction of Agent-based Simulation Using Business Process Diagrams and Ontology-based Models

  • Donghun Kang
  • Zhenchao C. Bing
  • Wen Song
  • Zehong Hu
  • Shuo Chen
  • Jie Zhang
  • Hui Xi

In this paper, we present a tool for the business users to analyze different business scenarios using business process diagrams and ontology-based models. The business scenarios involve different types of entities where the business process diagrams are suitable for describing entities’ behaviors. The ontology-based model is proposed to capture entities’ attributes and their relations in a hierarchical manner. The tool can automatically construct agent-based simulation models, which can be executed instantly on the agent-based simulation engine without the help of software developers.

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IJCAI Conference 2017 Conference Paper

Optimal Posted-Price Mechanism in Microtask Crowdsourcing

  • Zehong Hu
  • Jie Zhang

Posted-price mechanisms are widely-adopted to decide the price of tasks in popular microtask crowdsourcing. In this paper, we propose a novel posted-price mechanism which not only outperforms existing mechanisms on performance but also avoids their need of a finite price range. The advantages are achieved by converting the pricing problem into a multi-armed bandit problem and designing an optimal algorithm to exploit the unique features of microtask crowdsourcing. We theoretically show the optimality of our algorithm and prove that the performance upper bound can be achieved without the need of a prior price range. We also conduct extensive experiments using real price data to verify the advantages and practicability of our mechanism.

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AAAI Conference 2016 Conference Paper

Efficient Computation of Emergent Equilibrium in Agent-Based Simulation

  • Zehong Hu
  • Meng Sha
  • Moath Jarrah
  • Jie Zhang
  • Hui Xi

In agent-based simulation, emergent equilibrium describes the macroscopic steady states of agents’ interactions. While the state of individual agents might be changing, the collective behavior pattern remains the same in macroscopic equilibrium states. Traditionally, these emergent equilibriums are calculated using Monte Carlo methods. However, these methods require thousands of repeated simulation runs, which are extremely time-consuming. In this paper, we propose a novel three-layer framework to efficiently compute emergent equilibriums. The framework consists of a macro-level pseudo-arclength equilibrium solver (PAES), a micro-level simulator (MLS) and a macro-micro bridge (MMB). It can adaptively explore parameter space and recursively compute equilibrium states using the predictor-corrector scheme. We apply the framework to the popular opinion dynamics and labour market models. The experimental results show that our framework outperformed Monte Carlo experiments in terms of computation efficiency while maintaining the accuracy.

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