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Bing Tian Dai

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4 papers
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IJCAI Conference 2024 Conference Paper

Fuel-Saving Route Planning with Data-Driven and Learning-Based Approaches – A Systematic Solution for Harbor Tugs

  • Shengming Wang
  • Xiaocai Zhang
  • Jing Li
  • Xiaoyang Wei
  • Hoong Chuin Lau
  • Bing Tian Dai
  • Binbin Huang
  • Zhe Xiao

In recent years, there are trends toward cleaner port environments through enforcement by imposed legislation. Transit optimisation of fuel-based port service boats like harbour tugs has emerged as a critical task to reduce fuel consumption and carbon emission. In this paper, an innovative learning-based method, comprising a Reinforcement Learning (RL) model together with a fuel consumption prediction model, was proposed to formulate fuel-saving transit routes. Firstly, an ensemble model is established by combining a Long Short-Term Memory (LSTM) model with a Multilayer Perceptron (MLP) model, predicting fuel use based on tugboat movement and environment factors. Subsequently, an innovative RL based on Deep Deterministic Policy Gradient (DDPG) framework is developed considering the characteristics and obstructions of waterway in Singapore as well as the environmental factors to learn the optimal transit strategy that minimizes fuel consumption. We also demonstrate the efficacy of the solution to generate routes from origin to destination terminals, exhibiting significantly reduced fuel consumption in comparison to real-world transit scenarios.

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AAAI Conference 2022 System Paper

MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers

  • Yihuai Lan
  • Lei Wang
  • Qiyuan Zhang
  • Yunshi Lan
  • Bing Tian Dai
  • Yan Wang
  • Dongxiang Zhang
  • Ee-Peng Lim

While Math Word Problem (MWP) solving has emerged as a popular field of study and made great progress in recent years, most existing methods are benchmarked solely on one or two datasets and implemented with different configurations. In this paper, we introduce the first open-source library for solving MWPs called MWPToolkit, which provides a unified, comprehensive, and extensible framework for the research purpose. Specifically, we deploy 17 deep learningbased MWP solvers and 6 MWP datasets in our toolkit. These MWP solvers are advanced models for MWP solving, covering the categories of Seq2seq, Seq2Tree, Graph2Tree, and Pre-trained Language Models. And these MWP datasets are popular datasets that are commonly used as benchmarks in existing work. Our toolkit is featured with highly modularized and reusable components, which can help researchers quickly get started and develop their own models. We have released the code and documentation of MWPToolkit in https: //github. com/LYH-YF/MWPToolkit.

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AAAI Conference 2019 Conference Paper

Template-Based Math Word Problem Solvers with Recursive Neural Networks

  • Lei Wang
  • Dongxiang Zhang
  • Jipeng Zhang
  • Xing Xu
  • Lianli Gao
  • Bing Tian Dai
  • Heng Tao Shen

The design of automatic solvers to arithmetic math word problems has attracted considerable attention in recent years and a large number of datasets and methods have been published. Among them, Math23K is the largest data corpus that is very helpful to evaluate the generality and robustness of a proposed solution. The best performer in Math23K is a seq2seq model based on LSTM to generate the math expression. However, the model suffers from performance degradation in large space of target expressions. In this paper, we propose a template-based solution based on recursive neural network for math expression construction. More specifically, we first apply a seq2seq model to predict a tree-structure template, with inferred numbers as leaf nodes and unknown operators as inner nodes. Then, we design a recursive neural network to encode the quantity with Bi-LSTM and self attention, and infer the unknown operator nodes in a bottom-up manner. The experimental results clearly establish the superiority of our new framework as we improve the accuracy by a wide margin in two of the largest datasets, i. e. , from 58. 1% to 66. 9% in Math23K and from 62. 8% to 66. 8% in MAWPS.

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