9,624 research outputs found
Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs (Extended Version)
Many exact and approximate solution methods for Markov Decision Processes
(MDPs) attempt to exploit structure in the problem and are based on
factorization of the value function. Especially multiagent settings, however,
are known to suffer from an exponential increase in value component sizes as
interactions become denser, meaning that approximation architectures are
restricted in the problem sizes and types they can handle. We present an
approach to mitigate this limitation for certain types of multiagent systems,
exploiting a property that can be thought of as "anonymous influence" in the
factored MDP. Anonymous influence summarizes joint variable effects efficiently
whenever the explicit representation of variable identity in the problem can be
avoided. We show how representational benefits from anonymity translate into
computational efficiencies, both for general variable elimination in a factor
graph but in particular also for the approximate linear programming solution to
factored MDPs. The latter allows to scale linear programming to factored MDPs
that were previously unsolvable. Our results are shown for the control of a
stochastic disease process over a densely connected graph with 50 nodes and 25
agents.Comment: Extended version of AAAI 2016 pape
Different goals in multiscale simulations and how to reach them
In this paper we sum up our works on multiscale programs, mainly simulations.
We first start with describing what multiscaling is about, how it helps
perceiving signal from a background noise in a ?ow of data for example, for a
direct perception by a user or for a further use by another program. We then
give three examples of multiscale techniques we used in the past, maintaining a
summary, using an environmental marker introducing an history in the data and
finally using a knowledge on the behavior of the different scales to really
handle them at the same time
Scalable Planning and Learning for Multiagent POMDPs: Extended Version
Online, sample-based planning algorithms for POMDPs have shown great promise
in scaling to problems with large state spaces, but they become intractable for
large action and observation spaces. This is particularly problematic in
multiagent POMDPs where the action and observation space grows exponentially
with the number of agents. To combat this intractability, we propose a novel
scalable approach based on sample-based planning and factored value functions
that exploits structure present in many multiagent settings. This approach
applies not only in the planning case, but also in the Bayesian reinforcement
learning setting. Experimental results show that we are able to provide high
quality solutions to large multiagent planning and learning problems
A Methodology to Engineer and Validate Dynamic Multi-level Multi-agent Based Simulations
This article proposes a methodology to model and simulate complex systems,
based on IRM4MLS, a generic agent-based meta-model able to deal with
multi-level systems. This methodology permits the engineering of dynamic
multi-level agent-based models, to represent complex systems over several
scales and domains of interest. Its goal is to simulate a phenomenon using
dynamically the lightest representation to save computer resources without loss
of information. This methodology is based on two mechanisms: (1) the activation
or deactivation of agents representing different domain parts of the same
phenomenon and (2) the aggregation or disaggregation of agents representing the
same phenomenon at different scales.Comment: Presented at 3th International Workshop on Multi-Agent Based
Simulation, Valencia, Spain, 5th June 201
Consensus analysis of multiagent networks via aggregated and pinning approaches
This is the post-print version of of the Article - Copyright @ 2011 IEEEIn this paper, the consensus problem of multiagent nonlinear directed networks (MNDNs) is discussed in the case that a MNDN does not have a spanning tree to reach the consensus of all nodes. By using the Lie algebra theory, a linear node-and-node pinning method is proposed to achieve a consensus of a MNDN for all nonlinear functions satisfying a given set of conditions. Based on some optimal algorithms, large-size networks are aggregated to small-size ones. Then, by applying the principle minor theory to the small-size networks, a sufficient condition is given to reduce the number of controlled nodes. Finally, simulation results are given to illustrate the effectiveness of the developed criteria.This work was jointly supported by CityU under a research grant (7002355) and GRF funding (CityU 101109)
Evoplex: A platform for agent-based modeling on networks
Agent-based modeling and network science have been used extensively to
advance our understanding of emergent collective behavior in systems that are
composed of a large number of simple interacting individuals or agents. With
the increasing availability of high computational power in affordable personal
computers, dedicated efforts to develop multi-threaded, scalable and
easy-to-use software for agent-based simulations are needed more than ever.
Evoplex meets this need by providing a fast, robust and extensible platform for
developing agent-based models and multi-agent systems on networks. Each agent
is represented as a node and interacts with its neighbors, as defined by the
network structure. Evoplex is ideal for modeling complex systems, for example
in evolutionary game theory and computational social science. In Evoplex, the
models are not coupled to the execution parameters or the visualization tools,
and there is a user-friendly graphical interface which makes it easy for all
users, ranging from newcomers to experienced, to create, analyze, replicate and
reproduce the experiments.Comment: 6 pages, 5 figures; accepted for publication in SoftwareX [software
available at https://evoplex.org
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