1,756 research outputs found
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
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