13,502 research outputs found
Autonomic Resource Management in Virtual Networks
Virtualization enables the building of multiple virtual networks over a
shared substrate. One of the challenges to virtualisation is efficient resource
allocation. This problem has been found to be NP hard. Therefore, most
approaches to it have not only proposed static solutions, but have also made
many assumptions to simplify it. In this paper, we propose a distributed,
autonomic and artificial intelligence based solution to resource allocation.
Our aim is to obtain self-configuring, selfoptimizing, self-healing and context
aware virtual networksComment: Short Paper, 4 Pages, Summer School, PhD Work In Progress Workshop.
Scalable and Adaptive Internet Solutions (SAIL). June 201
MARL-FWC: Optimal Coordination of Freeway Traffic Control Measures
The objective of this article is to optimize the overall traffic flow on
freeways using multiple ramp metering controls plus its complementary Dynamic
Speed Limits (DSLs). An optimal freeway operation can be reached when
minimizing the difference between the freeway density and the critical ratio
for maximum traffic flow. In this article, a Multi-Agent Reinforcement Learning
for Freeways Control (MARL-FWC) system for ramps metering and DSLs is proposed.
MARL-FWC introduces a new microscopic framework at the network level based on
collaborative Markov Decision Process modeling (Markov game) and an associated
cooperative Q-learning algorithm. The technique incorporates payoff propagation
(Max-Plus algorithm) under the coordination graphs framework, particularly
suited for optimal control purposes. MARL-FWC provides three control designs:
fully independent, fully distributed, and centralized; suited for different
network architectures. MARL-FWC was extensively tested in order to assess the
proposed model of the joint payoff, as well as the global payoff. Experiments
are conducted with heavy traffic flow under the renowned VISSIM traffic
simulator to evaluate MARL-FWC. The experimental results show a significant
decrease in the total travel time and an increase in the average speed (when
compared with the base case) while maintaining an optimal traffic flow
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning
A crucial and time-sensitive task when any disaster occurs is to rescue
victims and distribute resources to the right groups and locations. This task
is challenging in populated urban areas, due to the huge burst of help requests
generated in a very short period. To improve the efficiency of the emergency
response in the immediate aftermath of a disaster, we propose a heuristic
multi-agent reinforcement learning scheduling algorithm, named as ResQ, which
can effectively schedule the rapid deployment of volunteers to rescue victims
in dynamic settings. The core concept is to quickly identify victims and
volunteers from social network data and then schedule rescue parties with an
adaptive learning algorithm. This framework performs two key functions: 1)
identify trapped victims and rescue volunteers, and 2) optimize the volunteers'
rescue strategy in a complex time-sensitive environment. The proposed ResQ
algorithm can speed up the training processes through a heuristic function
which reduces the state-action space by identifying the set of particular
actions over others. Experimental results showed that the proposed heuristic
multi-agent reinforcement learning based scheduling outperforms several
state-of-art methods, in terms of both reward rate and response times
A Conceptual Bio-Inspired Framework for the Evolution of Artificial General Intelligence
In this work, a conceptual bio-inspired parallel and distributed learning
framework for the emergence of general intelligence is proposed, where agents
evolve through environmental rewards and learn throughout their lifetime
without supervision, i.e., self-learning through embodiment. The chosen control
mechanism for agents is a biologically plausible neuron model based on spiking
neural networks. Network topologies become more complex through evolution,
i.e., the topology is not fixed, while the synaptic weights of the networks
cannot be inherited, i.e., newborn brains are not trained and have no innate
knowledge of the environment. What is subject to the evolutionary process is
the network topology, the type of neurons, and the type of learning. This
process ensures that controllers that are passed through the generations have
the intrinsic ability to learn and adapt during their lifetime in mutable
environments. We envision that the described approach may lead to the emergence
of the simplest form of artificial general intelligence.Comment: 7 pages, 2 figures, accepted to "The 3rd Special Session on
Biologically Inspired Parallel and Distributed Computing, Algorithms and
Solutions" (BICAS 2020
Revisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning
Many tasks in artificial intelligence require the collaboration of multiple
agents. We exam deep reinforcement learning for multi-agent domains. Recent
research efforts often take the form of two seemingly conflicting perspectives,
the decentralized perspective, where each agent is supposed to have its own
controller; and the centralized perspective, where one assumes there is a
larger model controlling all agents. In this regard, we revisit the idea of the
master-slave architecture by incorporating both perspectives within one
framework. Such a hierarchical structure naturally leverages advantages from
one another. The idea of combining both perspectives is intuitive and can be
well motivated from many real world systems, however, out of a variety of
possible realizations, we highlights three key ingredients, i.e. composed
action representation, learnable communication and independent reasoning. With
network designs to facilitate these explicitly, our proposal consistently
outperforms latest competing methods both in synthetic experiments and when
applied to challenging StarCraft micromanagement tasks
A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach
The simultaneous control of multiple coordinated robotic agents represents an
elaborate problem. If solved, however, the interaction between the agents can
lead to solutions to sophisticated problems. The concept of swarming, inspired
by nature, can be described as the emergence of complex system-level behaviors
from the interactions of relatively elementary agents. Due to the effectiveness
of solutions found in nature, bio-inspired swarming-based control techniques
are receiving a lot of attention in robotics. One method, known as swarm
shepherding, is founded on the sheep herding behavior exhibited by sheepdogs,
where a swarm of relatively simple agents are governed by a shepherd (or
shepherds) which is responsible for high-level guidance and planning. Many
studies have been conducted on shepherding as a control technique, ranging from
the replication of sheep herding via simulation, to the control of uninhabited
vehicles and robots for a variety of applications. We present a comprehensive
review of the literature on swarm shepherding to reveal the advantages and
potential of the approach to be applied to a plethora of robotic systems in the
future.Comment: Copyright 2020 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
future media, including reprinting/republishing this material for advertising
or promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other work
A Framework for learning multi-agent dynamic formation strategy in real-time applications
Formation strategy is one of the most important parts of many multi-agent
systems with many applications in real world problems. In this paper, a
framework for learning this task in a limited domain (restricted environment)
is proposed. In this framework, agents learn either directly by observing an
expert behavior or indirectly by observing other agents or objects behavior.
First, a group of algorithms for learning formation strategy based on limited
features will be presented. Due to distributed and complex nature of many
multi-agent systems, it is impossible to include all features directly in the
learning process; thus, a modular scheme is proposed in order to reduce the
number of features. In this method, some important features have indirect
influence in learning instead of directly involving them as input features.
This framework has the ability to dynamically assign a group of positions to a
group of agents to improve system performance. In addition, it can change the
formation strategy when the context changes. Finally, this framework is able to
automatically produce many complex and flexible formation strategy algorithms
without directly involving an expert to present and implement such complex
algorithms.Comment: 27 pages, 9 figure
ALAN: Adaptive Learning for Multi-Agent Navigation
In multi-agent navigation, agents need to move towards their goal locations
while avoiding collisions with other agents and static obstacles, often without
communication with each other. Existing methods compute motions that are
optimal locally but do not account for the aggregated motions of all agents,
producing inefficient global behavior especially when agents move in a crowded
space. In this work, we develop methods to allow agents to dynamically adapt
their behavior to their local conditions. We accomplish this by formulating the
multi-agent navigation problem as an action-selection problem, and propose an
approach, ALAN, that allows agents to compute time-efficient and collision-free
motions. ALAN is highly scalable because each agent makes its own decisions on
how to move using a set of velocities optimized for a variety of navigation
tasks. Experimental results show that the agents using ALAN, in general, reach
their destinations faster than using ORCA, a state-of-the-art collision
avoidance framework, the Social Forces model for pedestrian navigation, and a
Predictive collision avoidance model.Comment: Submitted to the Autonomous Robots Journal, Special Issue on
Distributed Robot
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity
The key challenge in multiagent learning is learning a best response to the
behaviour of other agents, which may be non-stationary: if the other agents
adapt their strategy as well, the learning target moves. Disparate streams of
research have approached non-stationarity from several angles, which make a
variety of implicit assumptions that make it hard to keep an overview of the
state of the art and to validate the innovation and significance of new works.
This survey presents a coherent overview of work that addresses
opponent-induced non-stationarity with tools from game theory, reinforcement
learning and multi-armed bandits. Further, we reflect on the principle
approaches how algorithms model and cope with this non-stationarity, arriving
at a new framework and five categories (in increasing order of sophistication):
ignore, forget, respond to target models, learn models, and theory of mind. A
wide range of state-of-the-art algorithms is classified into a taxonomy, using
these categories and key characteristics of the environment (e.g.,
observability) and adaptation behaviour of the opponents (e.g., smooth,
abrupt). To clarify even further we present illustrative variations of one
domain, contrasting the strengths and limitations of each category. Finally, we
discuss in which environments the different approaches yield most merit, and
point to promising avenues of future research.Comment: 64 pages, 7 figures. Under review since November 201
The Morphospace of Consciousness
We construct a complexity-based morphospace to study systems-level properties
of conscious & intelligent systems. The axes of this space label 3 complexity
types: autonomous, cognitive & social. Given recent proposals to synthesize
consciousness, a generic complexity-based conceptualization provides a useful
framework for identifying defining features of conscious & synthetic systems.
Based on current clinical scales of consciousness that measure cognitive
awareness and wakefulness, we take a perspective on how contemporary
artificially intelligent machines & synthetically engineered life forms measure
on these scales. It turns out that awareness & wakefulness can be associated to
computational & autonomous complexity respectively. Subsequently, building on
insights from cognitive robotics, we examine the function that consciousness
serves, & argue the role of consciousness as an evolutionary game-theoretic
strategy. This makes the case for a third type of complexity for describing
consciousness: social complexity. Having identified these complexity types,
allows for a representation of both, biological & synthetic systems in a common
morphospace. A consequence of this classification is a taxonomy of possible
conscious machines. We identify four types of consciousness, based on
embodiment: (i) biological consciousness, (ii) synthetic consciousness, (iii)
group consciousness (resulting from group interactions), & (iv) simulated
consciousness (embodied by virtual agents within a simulated reality). This
taxonomy helps in the investigation of comparative signatures of consciousness
across domains, in order to highlight design principles necessary to engineer
conscious machines. This is particularly relevant in the light of recent
developments at the crossroads of cognitive neuroscience, biomedical
engineering, artificial intelligence & biomimetics.Comment: 23 pages, 3 figure
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