1,921 research outputs found
Cooperative Game Theory within Multi-Agent Systems for Systems Scheduling
Research concerning organization and coordination within multi-agent systems
continues to draw from a variety of architectures and methodologies. The work
presented in this paper combines techniques from game theory and multi-agent
systems to produce self-organizing, polymorphic, lightweight, embedded agents
for systems scheduling within a large-scale real-time systems environment.
Results show how this approach is used to experimentally produce optimum
real-time scheduling through the emergent behavior of thousands of agents.
These results are obtained using a SWARM simulation of systems scheduling
within a High Energy Physics experiment consisting of 2500 digital signal
processors.Comment: Fourth International Conference on Hybrid Intelligent Systems (HIS),
Kitakyushu, Japan, December, 200
A new model for solution of complex distributed constrained problems
In this paper we describe an original computational model for solving
different types of Distributed Constraint Satisfaction Problems (DCSP). The
proposed model is called Controller-Agents for Constraints Solving (CACS). This
model is intended to be used which is an emerged field from the integration
between two paradigms of different nature: Multi-Agent Systems (MAS) and the
Constraint Satisfaction Problem paradigm (CSP) where all constraints are
treated in central manner as a black-box. This model allows grouping
constraints to form a subset that will be treated together as a local problem
inside the controller. Using this model allows also handling non-binary
constraints easily and directly so that no translating of constraints into
binary ones is needed. This paper presents the implementation outlines of a
prototype of DCSP solver, its usage methodology and overview of the CACS
application for timetabling problems
Bio-inspired multi-agent systems for reconfigurable manufacturing systems
The current market’s demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an
alternative way to design this kind of system based on decentralized control using distributed,
autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions
provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually
do not consider true adaptation and re-configuration. Understanding how, in nature, complex things
are performed in a simple and effective way allows us to mimic nature’s insights and develop powerful
adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufactur-
ing systems. The paper provides an overview of some of the principles found in nature and biology and
analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to
solve complex engineering problems, especially in the manufacturing field. An industrial automation
case study is used to illustrate a bio-inspired method based on potential fields to dynamically route
pallets
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