14,903 research outputs found
Fuzzy Q-Learning Based Multi-Agent System for Intelligent Traffic Control by a Game Theory Approach
This paper introduces a multi-agent approach to adjust traffic lights based
on traffic situation in order to reduce average delay time. In the traffic
model, lights of each intersection are controlled by an autonomous agent. Since
decision of each agent affects neighbor agents, this approach creates a
classical non-stationary environment. Thus, each agent not only needs to learn
from the past experience but also has to consider decision of neighbors to
overcome dynamic changes of the traffic network. Fuzzy Q-learning and Game
theory are employed to make policy based on previous experiences and decision
of neighbor agents. Simulation results illustrate the advantage of the proposed
method over fixed time, fuzzy, Q-learning and fuzzy Q-learning control methods.Comment: 10 pages, 10 figure
Economics 2.0: The Natural Step towards A Self-Regulating, Participatory Market Society
Despite all our great advances in science, technology and financial
innovations, many societies today are struggling with a financial, economic and
public spending crisis, over-regulation, and mass unemployment, as well as lack
of sustainability and innovation. Can we still rely on conventional economic
thinking or do we need a new approach?
I argue that, as the complexity of socio-economic systems increases,
networked decision-making and bottom-up self-regulation will be more and more
important features. It will be explained why, besides the "homo economicus"
with strictly self-regarding preferences, natural selection has also created a
"homo socialis" with other-regarding preferences. While the "homo economicus"
optimizes the own prospects in separation, the decisions of the "homo socialis"
are self-determined, but interconnected, a fact that may be characterized by
the term "networked minds". Notably, the "homo socialis" manages to earn higher
payoffs than the "homo economicus".
I show that the "homo economicus" and the "homo socialis" imply a different
kind of dynamics and distinct aggregate outcomes. Therefore, next to the
traditional economics for the "homo economicus" ("economics 1.0"), a
complementary theory must be developed for the "homo socialis". This economic
theory might be called "economics 2.0" or "socionomics". The names are
justified, because the Web 2.0 is currently promoting a transition to a new
market organization, which benefits from social media platforms and could be
characterized as "participatory market society". To thrive, the "homo socialis"
requires suitable institutional settings such a particular kinds of reputation
systems, which will be sketched in this paper. I also propose a new kind of
money, so-called "qualified money", which may overcome some of the problems of
our current financial system.Comment: For related work see http://www.soms.ethz.ch and
http://www.futurict.e
Self-Organization in Traffic Lights: Evolution of Signal Control with Advances in Sensors and Communications
Traffic signals are ubiquitous devices that first appeared in 1868. Recent
advances in information and communications technology (ICT) have led to
unprecedented improvements in such areas as mobile handheld devices (i.e.,
smartphones), the electric power industry (i.e., smart grids), transportation
infrastructure, and vehicle area networks. Given the trend towards
interconnectivity, it is only a matter of time before vehicles communicate with
one another and with infrastructure. In fact, several pilots of such
vehicle-to-vehicle and vehicle-to-infrastructure (e.g. traffic lights and
parking spaces) communication systems are already operational. This survey of
autonomous and self-organized traffic signaling control has been undertaken
with these potential developments in mind. Our research results indicate that,
while many sophisticated techniques have attempted to improve the scheduling of
traffic signal control, either real-time sensing of traffic patterns or a
priori knowledge of traffic flow is required to optimize traffic. Once this is
achieved, communication between traffic signals will serve to vastly improve
overall traffic efficiency
When slower is faster
The slower is faster (SIF) effect occurs when a system performs worse as its
components try to do better. Thus, a moderate individual efficiency actually
leads to a better systemic performance. The SIF effect takes place in a variety
of phenomena. We review studies and examples of the SIF effect in pedestrian
dynamics, vehicle traffic, traffic light control, logistics, public transport,
social dynamics, ecological systems, and adaptation. Drawing on these examples,
we generalize common features of the SIF effect and suggest possible future
lines of research
Unconventional Arterial Intersection Designs under Connected and Automated Vehicle Environment: A Survey
Signalized intersections are major sources of traffic delay and collision
within the modern transportation system. Conventional signal optimization has
revealed its limitation in improving the mobility and safety of an
intersection. Unconventional arterial intersection designs (UAIDs) are able to
improve the performance of an intersection by reducing phases of a signal
cycle. Furthermore, they can fundamentally alter the number and the nature of
the conflicting points. However, the driver's confusion, as a result of the
unconventional geometric designs, remains one of the major barriers for the
widespread adoption of UAIDs. Connected and Automated Vehicle (CAV) technology
has the potential to overcome this barrier by eliminating the driver's
confusion of a UAID. Therefore, UAIDs can play a significant role in
transportation networks in the near future. In this paper, we surveyed UAID
studies and implementations. In addition, we present an overview of
intersection control schemes with the emergence of CAV and highlight the
opportunity rises for UAID with the CAV technology. It is believed that the
benefits gained from deploying UAIDs in conjunction with CAV are significant
during the initial rollout of CAV under low market penetration
Distributed Constraint Optimization Problems and Applications: A Survey
The field of Multi-Agent System (MAS) is an active area of research within
Artificial Intelligence, with an increasingly important impact in industrial
and other real-world applications. Within a MAS, autonomous agents interact to
pursue personal interests and/or to achieve common objectives. Distributed
Constraint Optimization Problems (DCOPs) have emerged as one of the prominent
agent architectures to govern the agents' autonomous behavior, where both
algorithms and communication models are driven by the structure of the specific
problem. During the last decade, several extensions to the DCOP model have
enabled them to support MAS in complex, real-time, and uncertain environments.
This survey aims at providing an overview of the DCOP model, giving a
classification of its multiple extensions and addressing both resolution
methods and applications that find a natural mapping within each class of
DCOPs. The proposed classification suggests several future perspectives for
DCOP extensions, and identifies challenges in the design of efficient
resolution algorithms, possibly through the adaptation of strategies from
different areas
A Survey on Traffic Signal Control Methods
Traffic signal control is an important and challenging real-world problem,
which aims to minimize the travel time of vehicles by coordinating their
movements at the road intersections. Current traffic signal control systems in
use still rely heavily on oversimplified information and rule-based methods,
although we now have richer data, more computing power and advanced methods to
drive the development of intelligent transportation. With the growing interest
in intelligent transportation using machine learning methods like reinforcement
learning, this survey covers the widely acknowledged transportation approaches
and a comprehensive list of recent literature on reinforcement for traffic
signal control. We hope this survey can foster interdisciplinary research on
this important topic.Comment: 32 page
A General Methodology for Designing Self-Organizing Systems
Our technologies complexify our environments. Thus, new technologies need to
deal with more and more complexity. Several efforts have been made to deal with
this complexity using the concept of self-organization. However, in order to
promote its use and understanding, we must first have a pragmatic understanding
of complexity and self-organization. This paper presents a conceptual framework
for speaking about self-organizing systems. The aim is to provide a methodology
useful for designing and controlling systems developed to solve complex
problems. First, practical notions of complexity and self-organization are
given. Then, starting from the agent metaphor, a conceptual framework is
presented. This provides formal ways of speaking about "satisfaction" of
elements and systems. The main premise of the methodology claims that reducing
the "friction" or "interference" of interactions between elements of a system
will result in a higher "satisfaction" of the system, i.e. better performance.
The methodology discusses different ways in which this can be achieved. A case
study on self-organizing traffic lights illustrates the ideas presented in the
paper.Comment: Related work at http://homepages.vub.ac.be/~cgershen/sos
An Introduction to Collective Intelligence
This paper surveys the emerging science of how to design a ``COllective
INtelligence'' (COIN). A COIN is a large multi-agent system where:
(i) There is little to no centralized communication or control; and
(ii) There is a provided world utility function that rates the possible
histories of the full system.
In particular, we are interested in COINs in which each agent runs a
reinforcement learning (RL) algorithm. Rather than use a conventional modeling
approach (e.g., model the system dynamics, and hand-tune agents to cooperate),
we aim to solve the COIN design problem implicitly, via the ``adaptive''
character of the RL algorithms of each of the agents. This approach introduces
an entirely new, profound design problem: Assuming the RL algorithms are able
to achieve high rewards, what reward functions for the individual agents will,
when pursued by those agents, result in high world utility? In other words,
what reward functions will best ensure that we do not have phenomena like the
tragedy of the commons, Braess's paradox, or the liquidity trap?
Although still very young, research specifically concentrating on the COIN
design problem has already resulted in successes in artificial domains, in
particular in packet-routing, the leader-follower problem, and in variants of
Arthur's El Farol bar problem. It is expected that as it matures and draws upon
other disciplines related to COINs, this research will greatly expand the range
of tasks addressable by human engineers. Moreover, in addition to drawing on
them, such a fully developed scie nce of COIN design may provide much insight
into other already established scientific fields, such as economics, game
theory, and population biology.Comment: 88 pages, 10 figs, 297 ref
Control of Connected and Automated Vehicles: State of the Art and Future Challenges
Autonomous driving technology pledges safety, convenience, and energy
efficiency. Challenges include the unknown intentions of other road users:
communication between vehicles and with the road infrastructure is a possible
approach to enhance awareness and enable cooperation. Connected and automated
vehicles (CAVs) have the potential to disrupt mobility, extending what is
possible with driving automation and connectivity alone. Applications include
real-time control and planning with increased awareness, routing with
micro-scale traffic information, coordinated platooning using traffic signals
information, eco-mobility on demand with guaranteed parking. This paper
introduces a control and planning architecture for CAVs, and surveys the state
of the art on each functional block therein; the main focus is on techniques to
improve energy efficiency. We provide an overview of existing algorithms and
their mutual interactions, we present promising optimization-based approaches
to CAVs control and identify future challenges
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