18,683 research outputs found
Avoiding Braess' Paradox through Collective Intelligence
In an Ideal Shortest Path Algorithm (ISPA), at each moment each router in a
network sends all of its traffic down the path that will incur the lowest cost
to that traffic. In the limit of an infinitesimally small amount of traffic for
a particular router, its routing that traffic via an ISPA is optimal, as far as
cost incurred by that traffic is concerned. We demonstrate though that in many
cases, due to the side-effects of one router's actions on another routers
performance, having routers use ISPA's is suboptimal as far as global aggregate
cost is concerned, even when only used to route infinitesimally small amounts
of traffic. As a particular example of this we present an instance of Braess'
paradox for ISPA's, in which adding new links to a network decreases overall
throughput. We also demonstrate that load-balancing, in which the routing
decisions are made to optimize the global cost incurred by all traffic
currently being routed, is suboptimal as far as global cost averaged across
time is concerned. This is also due to "side-effects", in this case of current
routing decision on future traffic.
The theory of COllective INtelligence (COIN) is concerned precisely with the
issue of avoiding such deleterious side-effects. We present key concepts from
that theory and use them to derive an idealized algorithm whose performance is
better than that of the ISPA, even in the infinitesimal limit. We present
experiments verifying this, and also showing that a machine-learning-based
version of this COIN algorithm in which costs are only imprecisely estimated (a
version potentially applicable in the real world) also outperforms the ISPA,
despite having access to less information than does the ISPA. In particular,
this COIN algorithm avoids Braess' paradox.Comment: 28 page
Using Collective Intelligence to Route Internet Traffic
A COllective INtelligence (COIN) is a set of interacting reinforcement
learning (RL) algorithms designed in an automated fashion so that their
collective behavior optimizes a global utility function. We summarize the
theory of COINs, then present experiments using that theory to design COINs to
control internet traffic routing. These experiments indicate that COINs
outperform all previously investigated RL-based, shortest path routing
algorithms.Comment: 7 page
Enabling Disaster Resilient 4G Mobile Communication Networks
The 4G Long Term Evolution (LTE) is the cellular technology expected to
outperform the previous generations and to some extent revolutionize the
experience of the users by taking advantage of the most advanced radio access
techniques (i.e. OFDMA, SC-FDMA, MIMO). However, the strong dependencies
between user equipments (UEs), base stations (eNBs) and the Evolved Packet Core
(EPC) limit the flexibility, manageability and resiliency in such networks. In
case the communication links between UEs-eNB or eNB-EPC are disrupted, UEs are
in fact unable to communicate. In this article, we reshape the 4G mobile
network to move towards more virtual and distributed architectures for
improving disaster resilience, drastically reducing the dependency between UEs,
eNBs and EPC. The contribution of this work is twofold. We firstly present the
Flexible Management Entity (FME), a distributed entity which leverages on
virtualized EPC functionalities in 4G cellular systems. Second, we introduce a
simple and novel device-todevice (D2D) communication scheme allowing the UEs in
physical proximity to communicate directly without resorting to the
coordination with an eNB.Comment: Submitted to IEEE Communications Magazin
Reactive scheduling using a multi-agent model: the SCEP framework
Multi-agent systems have been successfully applied to the scheduling problem for some time. However, their use often leads to poorly unsatisfactory disappointing results. A new multi-agent model, called supervisor, customers, environment, producers (SCEP), is suggested in this paper. This model, developed for all types of planning activities, introduces a dialogue between two communities of agents leading to a high level of co-operation. Its two main interests are the following: first it provides a more efficient control of the consequences generated by the local decisions than usual systems to each agent, then the adopted architecture and behaviour permit an easy co-operation between the different SCEP models, which can represent different production functions such as manufacturing, supply management, maintenance or different workshops. As a consequence, the SCEP model can be adapted to a great variety of scheduling/planning problems. This model is applied to the basic scheduling problem of flexible manufacturing systems, andit permits a natural co-habitation between infinite capacity scheduling processes, performedby the manufacturing orders, and finite capacity scheduling processes, performed by the machines. It also provides a framework in order to react to the disturbances occurring at different levels of the workshop
Evolution of swarming behavior is shaped by how predators attack
Animal grouping behaviors have been widely studied due to their implications
for understanding social intelligence, collective cognition, and potential
applications in engineering, artificial intelligence, and robotics. An
important biological aspect of these studies is discerning which selection
pressures favor the evolution of grouping behavior. In the past decade,
researchers have begun using evolutionary computation to study the evolutionary
effects of these selection pressures in predator-prey models. The selfish herd
hypothesis states that concentrated groups arise because prey selfishly attempt
to place their conspecifics between themselves and the predator, thus causing
an endless cycle of movement toward the center of the group. Using an
evolutionary model of a predator-prey system, we show that how predators attack
is critical to the evolution of the selfish herd. Following this discovery, we
show that density-dependent predation provides an abstraction of Hamilton's
original formulation of ``domains of danger.'' Finally, we verify that
density-dependent predation provides a sufficient selective advantage for prey
to evolve the selfish herd in response to predation by coevolving predators.
Thus, our work corroborates Hamilton's selfish herd hypothesis in a digital
evolutionary model, refines the assumptions of the selfish herd hypothesis, and
generalizes the domain of danger concept to density-dependent predation.Comment: 25 pages, 11 figures, 5 tables, including 2 Supplementary Figures.
Version to appear in "Artificial Life
From supply chains to demand networks. Agents in retailing: the electrical bazaar
A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version
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