14,283 research outputs found
The dynamics of iterated transportation simulations
Iterating between a router and a traffic micro-simulation is an increasibly
accepted method for doing traffic assignment. This paper, after pointing out
that the analytical theory of simulation-based assignment to-date is
insufficient for some practical cases, presents results of simulation studies
from a real world study. Specifically, we look into the issues of uniqueness,
variability, and robustness and validation. Regarding uniqueness, despite some
cautionary notes from a theoretical point of view, we find no indication of
``meta-stable'' states for the iterations. Variability however is considerable.
By variability we mean the variation of the simulation of a given plan set by
just changing the random seed. We show then results from three different
micro-simulations under the same iteration scenario in order to test for the
robustness of the results under different implementations. We find the results
encouraging, also when comparing to reality and with a traditional assignment
result.
Keywords: dynamic traffic assignment (DTA); traffic micro-simulation;
TRANSIMS; large-scale simulations; urban planningComment: 24 pages, 7 figure
Towards a Testbed for Dynamic Vehicle Routing Algorithms
Since modern transport services are becoming more flexible, demand-responsive, and energy/cost efficient, there is a growing demand for large-scale microscopic simulation platforms in order to test sophisticated routing algorithms. Such platforms have to simulate in detail, not only the dynamically changing demand and supply of the relevant service, but also traffic flow and other relevant transport services. This paper presents the DVRP extension to the open-source MATSim simulator. The extension is designed to be highly general and customizable to simulate a wide range of dynamic rich vehicle routing problems. The extension allows plugging in of various algorithms that are responsible for continuous re-optimisation of routes in response to changes in the system. The DVRP extension has been used in many research and commercial projects dealing with simulation of electric and autonomous taxis, demand-responsive transport, personal rapid transport, free-floating car sharing and parking search
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Fast community structure local uncovering by independent vertex-centred process
This paper addresses the task of community detection and proposes a local
approach based on a distributed list building, where each vertex broadcasts
basic information that only depends on its degree and that of its neighbours. A
decentralised external process then unveils the community structure. The
relevance of the proposed method is experimentally shown on both artificial and
real data.Comment: 2015 IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining, Aug 2015, Paris, France. Proceedings of the 2015
IEEE/ACM International Conference on Advances in Social Networks Analysis and
Minin
Design choices for agent-based control of AGVs in the dough making process
In this paper we consider a multi-agent system (MAS) for the logistics control of Automatic Guided Vehicles (AGVs) that are used in the dough making process at an industrial bakery. Here, logistics control refers to constructing robust schedules for all transportation jobs. The paper discusses how alternative MAS designs can be developed and compared using cost, frequency of messages between agents, and computation time for evaluating control rules as performance indicators. Qualitative design guidelines turn out to be insufficient to select the best agent architecture. Therefore, we also use simulation to support decision making, where we use real-life data from the bakery to evaluate several alternative designs. We find that architectures in which line agents initiate allocation of transportation jobs, and AGV agents schedule multiple jobs in advance, perform best. We conclude by discussing the benefits of our MAS systems design approach for real-life applications
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning
Cooperative multi-agent reinforcement learning (MARL) has made prominent
progress in recent years. For training efficiency and scalability, most of the
MARL algorithms make all agents share the same policy or value network.
However, in many complex multi-agent tasks, different agents are expected to
possess specific abilities to handle different subtasks. In those scenarios,
sharing parameters indiscriminately may lead to similar behavior across all
agents, which will limit the exploration efficiency and degrade the final
performance. To balance the training complexity and the diversity of agent
behavior, we propose a novel framework to learn dynamic subtask assignment
(LDSA) in cooperative MARL. Specifically, we first introduce a subtask encoder
to construct a vector representation for each subtask according to its
identity. To reasonably assign agents to different subtasks, we propose an
ability-based subtask selection strategy, which can dynamically group agents
with similar abilities into the same subtask. In this way, agents dealing with
the same subtask share their learning of specific abilities and different
subtasks correspond to different specific abilities. We further introduce two
regularizers to increase the representation difference between subtasks and
stabilize the training by discouraging agents from frequently changing
subtasks, respectively. Empirical results show that LDSA learns reasonable and
effective subtask assignment for better collaboration and significantly
improves the learning performance on the challenging StarCraft II
micromanagement benchmark and Google Research Football
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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