CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a large-scale parallel ranking problem and study the joint decision-making task of order dispatching and fleet management in online ride-hailing platforms. This task brings unique challenges in the following four aspects. First, to facilitate a huge number of vehicles to act and learn efficiently and robustly, we treat each region cell as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to achieve long-term benefits, we leverage the geographical hierarchy of the region grids to perform hierarchical reinforcement learning. Third, to deal with the heterogeneous and variant action space for joint order dispatching and fleet management, we design the action as the ranking weight vector to rank and select the specific order or the fleet management destination in a unified formulation. Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multi-head attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale. The whole novel framework is named as CoRide. Extensive experiments based on multiple cities real-world data as well as analytic synthetic data demonstrate that CoRide provides superior performance in terms of platform revenue and user experience in the task of city-wide hybrid order dispatching and fleet management over strong baselines.Comment: CIKM 201

Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior

This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic causal model for predicting the behavior generated by modern percept-driven robot plans. PHAMs represent aspects of robot behavior that cannot be represented by most action models used in AI planning: the temporal structure of continuous control processes, their non-deterministic effects, several modes of their interferences, and the achievement of triggering conditions in closed-loop robot plans. The main contributions of this article are: (1) PHAMs, a model of concurrent percept-driven behavior, its formalization, and proofs that the model generates probably, qualitatively accurate predictions; and (2) a resource-efficient inference method for PHAMs based on sampling projections from probabilistic action models and state descriptions. We show how PHAMs can be applied to planning the course of action of an autonomous robot office courier based on analytical and experimental results

Matching Mechanisms to Situations Through the Wisdom of the Crowd

Designing a system often begins with matching existing solutions to current problems. Specifically, integration mechanisms are mapped onto situations. Novices are not good at this task, and experts are rare. Could crowdsourcing, that is, aggregating the suggestions of individuals working independently, be effective? Two experiments, one with design students in a classroom, and another with participants on the web, demonstrated that the crowd possesses wisdom about how to match mechanisms to situations. Participants also categorized situations, and those who name their categories were better at matching than those who didn’t. The results have pragmatic implications, suggesting it is possible to crowdsource design, and providing new ways of eliciting, testing, and training expertise. More generally, the paper suggests a new model for information system design based on analogical mapping

MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection

Accurate and reliable 3D detection is vital for many applications including autonomous driving vehicles and service robots. In this paper, we present a flexible and high-performance 3D detection framework, named MPPNet, for 3D temporal object detection with point cloud sequences. We propose a novel three-hierarchy framework with proxy points for multi-frame feature encoding and interactions to achieve better detection. The three hierarchies conduct per-frame feature encoding, short-clip feature fusion, and whole-sequence feature aggregation, respectively. To enable processing long-sequence point clouds with reasonable computational resources, intra-group feature mixing and inter-group feature attention are proposed to form the second and third feature encoding hierarchies, which are recurrently applied for aggregating multi-frame trajectory features. The proxy points not only act as consistent object representations for each frame, but also serve as the courier to facilitate feature interaction between frames. The experiments on large Waymo Open dataset show that our approach outperforms state-of-the-art methods with large margins when applied to both short (e.g., 4-frame) and long (e.g., 16-frame) point cloud sequences. Code is available at https://github.com/open-mmlab/OpenPCDet.Comment: Accepted by ECCV 202

Higher Education Exchange: 2008

This annual publication serves as a forum for new ideas and dialogue between scholars and the larger public. Essays explore ways that students, administrators, and faculty can initiate and sustain an ongoing conversation about the public life they share.The Higher Education Exchange is founded on a thought articulated by Thomas Jefferson in 1820: "I know no safe depository of the ultimate powers of the society but the people themselves; and if we think them not enlightened enough to exercise their control with a wholesome discretion, the remedy is not to take it from them, but to inform their discretion by education."In the tradition of Jefferson, the Higher Education Exchange agrees that a central goal of higher education is to help make democracy possible by preparing citizens for public life. The Higher Education Exchange is part of a movement to strengthen higher education's democratic mission and foster a more democratic culture throughout American society.Working in this tradition, the Higher Education Exchange publishes interviews, case studies, analyses, news, and ideas about efforts within higher education to develop more democratic societies

How mutable is the future: Can long futures be adaptively transformed by choices and decisions in the face of indomitable challenges?

An earlier inquiry and exploration into the systems involved in large scale space travel revealed a compelling narrative of balancing forces and in- fluences, which were later combined with strategy and game theory, and designed into a cooperative multiplayer board game. Evaluations (play- tests) of a prototype board-based game revealed several intriguing dynamics affecting the probabilities of complex futures, one of which suggested that futures are not the outcomes of planned trajectories, but are continuously changing possibilities over time, capable of moving between directional dynamics such as continuation, discipline, collapse, and transformation. Player motivations, interactions, decisions, and actions, both initiated or in response to events, were the primary authors of a game’s progression. This research project further investigates the influence of active intervention into future outcomes by explicitly incorporating critical uncertainties into an updated version of the board game. Using the updated game as a framework for interaction, the project collects data from Design Action Research workshops to discover a rubric effective for measuring patterns of change based on the Dator 4 Futures framework

CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

How to optimally dispatch orders to vehicles and how to trade off between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a large-scale parallel ranking problem and study the joint decisionmaking task of order dispatching and fleet management in online ride-hailing platforms. This task brings unique challenges in the following four aspects. First, to facilitate a huge number of vehicles to act and learn efficiently and robustly, we treat each region cell as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to achieve long-term benefits, we leverage the geographical hierarchy of the region grids to perform hierarchical reinforcement learning. Third, to deal with the heterogeneous and variant action space for joint order dispatching and fleet management, we design the action as the ranking weight vector to rank and select the specific order or the fleet management destination in a unified formulation. Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multihead attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale. The whole novel framework is named as CoRide. Extensive experiments based on multiple cities real-world data as well as analytic synthetic data demonstrate that CoRide provides superior performance in terms of platform revenue and user experience in the task of citywide hybrid order dispatching and fleet management over strong baselines

RAP: Risk-Aware Prediction for Robust Planning

Robust planning in interactive scenarios requires predicting the uncertain future to make risk-aware decisions. Unfortunately, due to long-tail safety-critical events, the risk is often under-estimated by finite-sampling approximations of probabilistic motion forecasts. This can lead to overconfident and unsafe robot behavior, even with robust planners. Instead of assuming full prediction coverage that robust planners require, we propose to make prediction itself risk-aware. We introduce a new prediction objective to learn a risk-biased distribution over trajectories, so that risk evaluation simplifies to an expected cost estimation under this biased distribution. This reduces the sample complexity of the risk estimation during online planning, which is needed for safe real-time performance. Evaluation results in a didactic simulation environment and on a real-world dataset demonstrate the effectiveness of our approach.Comment: 23 pages, 14 figures, 3 tables. First two authors contributed equally. Conference on Robot Learning (CoRL) 2022 (oral

Advancing the knowledge of local health care policy through the growth machine thesis.

A disciplined-configurative case study design was carried out to explore whether a growth machine exists and shapes local healthcare policy in Louisville. A historical analysis first explored whether a growth machine existed in Louisville in the past and shaped healthcare policy. Second, a network analysis was used to identify the recent contours of the Louisville growth machine. Third, qualitative interviews were conducted with central individuals as indicated by the social network analysis to assess the degree to which local healthcare policy is shaped and driven by the growth machine. The results show that Louisville has consistently had a growth machine which has shaped the limited local healthcare politics and policy allowed by the higher levels of government. The Federal Government\u27s policy to require pluralistic boards of average consumers on public health agencies actually has detached the growth machine as the local power structure from those agencies. The growth machine has not always had consensus due to differing growth agendas as demonstrated by the construction of Southwest Hospital, the break up of the University of Louisville Hospital management consortium, and the reorganization of the Louisville Medical Center Development Corporation. Regardless the growth machine has generally been able to keep the general public out of such decisions and in vi turn find new ways to be unified in the name of growth. Local government in Louisville was and continues to be a supportive player versus dominant player in the growth machine in local healthcare politics and has not been the driver of local healthcare policy