1,316 research outputs found
Softpressure: A Schedule-Driven Backpressure Algorithm for Coping with Network Congestion
We consider the problem of minimizing the delay of jobs moving through a
directed graph of service nodes. In this problem, each node may have several
links and is constrained to serve one link at a time. As jobs move through the
network, they can pass through a node only after they have been serviced by
that node. The objective is to minimize the delay jobs incur sitting in queues
waiting to be serviced. Two distinct approaches to this problem have emerged
from respective work in queuing theory and dynamic scheduling: the backpressure
algorithm and schedule-driven control. In this paper, we present a hybrid
approach of those two methods that incorporates the stability of queuing theory
into a schedule-driven control framework. We then demonstrate how this hybrid
method outperforms the other two in a real-time traffic signal control problem,
where the nodes are traffic lights, the links are roads, and the jobs are
vehicles. We show through simulations that, in scenarios with heavy congestion,
the hybrid method results in 50% and 15% reductions in delay over
schedule-driven control and backpressure respectively. A theoretical analysis
also justifies our results.Comment: IJCAI 201
Work Capacity of Freelance Markets: Fundamental Limits and Decentralized Schemes
Crowdsourcing of jobs to online freelance markets is rapidly gaining
popularity. Most crowdsourcing platforms are uncontrolled and offer freedom to
customers and freelancers to choose each other. This works well for unskilled
jobs (e.g., image classification) with no specific quality requirement since
freelancers are functionally identical. For skilled jobs (e.g., software
development) with specific quality requirements, however, this does not ensure
that the maximum number of job requests is satisfied. In this work we determine
the capacity of freelance markets, in terms of maximum satisfied job requests,
and propose centralized schemes that achieve capacity. To ensure decentralized
operation and freedom of choice for customers and freelancers, we propose
simple schemes compatible with the operation of current crowdsourcing platforms
that approximately achieve capacity. Further, for settings where the number of
job requests exceeds capacity, we propose a scheme that is agnostic of that
information, but is optimal and fair in declining jobs without wait
Decentralized Q-Learning for Stochastic Teams and Games
There are only a few learning algorithms applicable to stochastic dynamic
teams and games which generalize Markov decision processes to decentralized
stochastic control problems involving possibly self-interested decision makers.
Learning in games is generally difficult because of the non-stationary
environment in which each decision maker aims to learn its optimal decisions
with minimal information in the presence of the other decision makers who are
also learning. In stochastic dynamic games, learning is more challenging
because, while learning, the decision makers alter the state of the system and
hence the future cost. In this paper, we present decentralized Q-learning
algorithms for stochastic games, and study their convergence for the weakly
acyclic case which includes team problems as an important special case. The
algorithm is decentralized in that each decision maker has access to only its
local information, the state information, and the local cost realizations;
furthermore, it is completely oblivious to the presence of other decision
makers. We show that these algorithms converge to equilibrium policies almost
surely in large classes of stochastic games.Comment: To appear in IEEE Trans. Automatic Contro
Enhanced Mobility With Connectivity and Automation: A Review of Shared Autonomous Vehicle Systems
Shared mobility can provide access to transportation on a custom basis
without vehicle ownership. The advent of connected and automated vehicle
technologies can further enhance the potential benefits of shared mobility
systems. Although the implications of a system with shared autonomous vehicles
have been investigated, the research reported in the literature has exhibited
contradictory outcomes. In this paper, we present a summary of the research
efforts in shared autonomous vehicle systems that have been reported in the
literature to date and discuss potential future research directions.Comment: 17 pages, 3 figures, IEEE Intelligent Transportation Systems
Magazine, 202
Decentralized Learning for Optimality in Stochastic Dynamic Teams and Games with Local Control and Global State Information
Stochastic dynamic teams and games are rich models for decentralized systems
and challenging testing grounds for multi-agent learning. Previous work that
guaranteed team optimality assumed stateless dynamics, or an explicit
coordination mechanism, or joint-control sharing. In this paper, we present an
algorithm with guarantees of convergence to team optimal policies in teams and
common interest games. The algorithm is a two-timescale method that uses a
variant of Q-learning on the finer timescale to perform policy evaluation while
exploring the policy space on the coarser timescale. Agents following this
algorithm are "independent learners": they use only local controls, local cost
realizations, and global state information, without access to controls of other
agents. The results presented here are the first, to our knowledge, to give
formal guarantees of convergence to team optimality using independent learners
in stochastic dynamic teams and common interest games
Learning in Multi-level Stochastic games with Delayed Information
Distributed decision-makers are modeled as players in a game with two levels.
High level decisions concern the game environment and determine the willingness
of the players to form a coalition (or group). Low level decisions involve the
actions to be implemented within the chosen environment. Coalition and action
strategies are determined by probability distributions, which are updated using
learning automata schemes. The payoffs are also probabilistic and there is
uncertainty in the state vector since information is delayed. The goal is to
reach equilibrium in both levels of decision making; the results show the
conditions for instability, based on the age of information.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
Optimal Routing for Delay-Sensitive Traffic in Overlay Networks
We design dynamic routing policies for an overlay network which meet delay
requirements of real-time traffic being served on top of an underlying legacy
network, where the overlay nodes do not know the underlay characteristics. We
pose the problem as a constrained MDP, and show that when the underlay
implements static policies such as FIFO with randomized routing, then a
decentralized policy, that can be computed efficiently in a distributed
fashion, is optimal. Our algorithm utilizes multi-timescale stochastic
approximation techniques, and its convergence relies on the fact that the
recursions asymptotically track a nonlinear differential equation, namely the
replicator equation. Extensive simulations show that the proposed policy indeed
outperforms the existing policies
A Survey and Taxonomy of Urban Traffic Management: Towards Vehicular Networks
Urban Traffic Management (UTM) topics have been tackled since long time,
mainly by civil engineers and by city planners. The introduction of new
communication technologies - such as cellular systems, satellite positioning
systems and inter-vehicle communications - has significantly changed the way
researchers deal with UTM issues. In this survey, we provide a review and a
classification of how UTM has been addressed in the literature. We start from
the recent achievements of "classical" approaches to urban traffic estimation
and optimization, including methods based on the analysis of data collected by
fixed sensors (e.g., cameras and radars), as well as methods based on
information provided by mobile phones, such as Floating Car Data (FCD).
Afterwards, we discuss urban traffic optimization, presenting the most recent
works on traffic signal control and vehicle routing control. Then, after
recalling the main concepts of Vehicular Ad-Hoc Networks (VANETs), we classify
the different VANET-based approaches to UTM, according to three categories
("pure" VANETs, hybrid vehicular-sensor networks and hybrid vehicular-cellular
networks), while illustrating the major research issues for each of them. The
main objective of this survey is to provide a comprehensive view on UTM to
researchers with focus on VANETs, in order to pave the way for the design and
development of novel techniques for mitigating urban traffic problems, based on
inter-vehicle communications
Efficient and Flexible Crowdsourcing of Specialized Tasks with Precedence Constraints
Many companies now use crowdsourcing to leverage external (as well as
internal) crowds to perform specialized work, and so methods of improving
efficiency are critical. Tasks in crowdsourcing systems with specialized work
have multiple steps and each step requires multiple skills. Steps may have
different flexibilities in terms of obtaining service from one or multiple
agents, due to varying levels of dependency among parts of steps. Steps of a
task may have precedence constraints among them. Moreover, there are variations
in loads of different types of tasks requiring different skill-sets and
availabilities of different types of agents with different skill-sets.
Considering these constraints together necessitates the design of novel schemes
to allocate steps to agents. In addition, large crowdsourcing systems require
allocation schemes that are simple, fast, decentralized and offer customers
(task requesters) the freedom to choose agents. In this work we study the
performance limits of such crowdsourcing systems and propose efficient
allocation schemes that provably meet the performance limits under these
additional requirements. We demonstrate our algorithms on data from a
crowdsourcing platform run by a non-profit company and show significant
improvements over current practice
A BCMP Network Approach to Modeling and Controlling Autonomous Mobility-on-Demand Systems
In this paper we present a queueing network approach to the problem of
routing and rebalancing a fleet of self-driving vehicles providing on-demand
mobility within a capacitated road network. We refer to such systems as
autonomous mobility-on-demand systems, or AMoD. We first cast an AMoD system
into a closed, multi-class BCMP queueing network model. Second, we present
analysis tools that allow the characterization of performance metrics for a
given routing policy, in terms, e.g., of vehicle availabilities, and first and
second order moments of vehicle throughput. Third, we propose a scalable method
for the synthesis of routing policies, with performance guarantees in the limit
of large fleet sizes. Finally, we validate our theoretical results on a case
study of New York City. Collectively, this paper provides a unifying framework
for the analysis and control of AMoD systems, which subsumes earlier Jackson
and network flow models, provides a quite large set of modeling options (e.g.,
the inclusion of road capacities and general travel time distributions), and
allows the analysis of second and higher-order moments for the performance
metrics.Comment: 18 pages, 3 figures. In preparation for conference submission. In
version 2, clarity is improved and some typos are removed with no changes to
the technical content of the pape
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