The dynamic vehicle dispatching problem corresponds to deciding which
vehicles to assign to requests that arise stochastically over time and space.
It emerges in diverse areas, such as in the assignment of trucks to loads to be
transported; in emergency systems; and in ride-hailing services. In this paper,
we model the problem as a semi-Markov decision process, which allows us to
treat time as continuous. In this setting, decision epochs coincide with
discrete events whose time intervals are random. We argue that an event-based
approach substantially reduces the combinatorial complexity of the decision
space and overcomes other limitations of discrete-time models often proposed in
the literature. In order to test our approach, we develop a new discrete-event
simulator and use double deep q-learning to train our decision agents.
Numerical experiments are carried out in realistic scenarios using data from
New York City. We compare the policies obtained through our approach with
heuristic policies often used in practice. Results show that our policies
exhibit better average waiting times, cancellation rates and total service
times, with reduction in average waiting times of up to 50% relative to the
other tested heuristic policies.

本文提出了一种基于半马尔可夫决策过程和离散事件模拟的动态车辆调度问题解决方法，并在纽约市的真实场景下进行了数值实验，结果显示相对于其他启发式策略，该方法的平均等待时间减少了 50%。

基于事件的深度强化学习应用于动态车辆调度问题

Deep reinforcement learning for the dynamic vehicle dispatching problem:  An event-based approach

We present Q-Cogni, an algorithmically integrated causal reinforcement
learning framework that redesigns Q-Learning with an autonomous causal
structure discovery method to improve the learning process with causal
inference. Q-Cogni achieves optimal learning with a pre-learned structural
causal model of the environment that can be queried during the learning process
to infer cause-and-effect relationships embedded in a state-action space. We
leverage on the sample efficient techniques of reinforcement learning, enable
reasoning about a broader set of policies and bring higher degrees of
interpretability to decisions made by the reinforcement learning agent. We
apply Q-Cogni on the Vehicle Routing Problem (VRP) and compare against
state-of-the-art reinforcement learning algorithms. We report results that
demonstrate better policies, improved learning efficiency and superior
interpretability of the agent's decision making. We also compare this approach
with traditional shortest-path search algorithms and demonstrate the benefits
of our causal reinforcement learning framework to high dimensional problems.
Finally, we apply Q-Cogni to derive optimal routing decisions for taxis in New
York City using the Taxi & Limousine Commission trip record data and compare
with shortest-path search, reporting results that show 85% of the cases with an
equal or better policy derived from Q-Cogni in a real-world domain.

Q-Cogni 是一种算法集成的因果强化学习框架，可通过自主因果关系发现方法重新设计 Q-Learning，从而在具有状态 / 动作空间的机器学习环境中实现最优学习和推理，并提高强化学习代理的决策可解释性。在应用 Q-Cogni 于车辆路径问题（VRP）中，我们将其与最先进的强化学习算法进行比较，并报告结果，表明该框架具有更好的策略、改进的学习效率和更高的代理决策的可解释性。同时与传统最短路径搜索算法进行比较，并且基于纽约市的出租车和豪华轿车委员会旅行记录数据应用 Q-Cogni 求解最佳路径选择问题，得出的结果表明在真实世界的情况下，其结果相比于最短路径搜索得到的结果有 85% 以上的数据是一样或更好的。

Q-Cogni: 一种融合因果关系强化学习框架

Q-Cogni: An Integrated Causal Reinforcement Learning Framework

The demand for e-hailing services is growing rapidly, especially in large
cities. Uber is the first and popular e-hailing company in the United Stated
and New York City. A comparison of the demand for yellow-cabs and Uber in NYC
in 2014 and 2015 shows that the demand for Uber has increased. However, this
demand may not be distributed uniformly either spatially or temporally. Using
spatio-temporal time series models can help us to better understand the demand
for e-hailing services and to predict it more accurately. This paper analyzes
the prediction performance of one temporal model (vector autoregressive (VAR))
and two spatio-temporal models (Spatial-temporal autoregressive (STAR); least
absolute shrinkage and selection operator applied on STAR (LASSO-STAR)) and for
different scenarios (based on the number of time and space lags), and applied
to both rush hours and non-rush hours periods. The results show the need of
considering spatial models for taxi demand.

本文探究了骑乘服务的需求和推断模式。结果表明，需考虑的空间模式为出租车需求在纽约市场上具有重要影响。

利用时空建模预测短期 Uber 需求：纽约市案例研究

Predicting Short-Term Uber Demand Using Spatio-Temporal Modeling: A New  York City Case Study

This document describes the contributions of the 2016 Applications of Logic
Programming Workshop (AppLP), which was held on October 17 and associated with
the International Conference on Logic Programming (ICLP) in Flushing, New York
City.

2016 年 Applications of Logic Programming Workshop 会议在纽约市 Flushing 举行，与国际逻辑编程会议（ICLP）相关，文章介绍了会议的主要贡献。