28,007 research outputs found
Control of Networked Multiagent Systems with Uncertain Graph Topologies
Multiagent systems consist of agents that locally exchange information
through a physical network subject to a graph topology. Current control methods
for networked multiagent systems assume the knowledge of graph topologies in
order to design distributed control laws for achieving desired global system
behaviors. However, this assumption may not be valid for situations where graph
topologies are subject to uncertainties either due to changes in the physical
network or the presence of modeling errors especially for multiagent systems
involving a large number of interacting agents. Motivating from this
standpoint, this paper studies distributed control of networked multiagent
systems with uncertain graph topologies. The proposed framework involves a
controller architecture that has an ability to adapt its feed- back gains in
response to system variations. Specifically, we analytically show that the
proposed controller drives the trajectories of a networked multiagent system
subject to a graph topology with time-varying uncertainties to a close
neighborhood of the trajectories of a given reference model having a desired
graph topology. As a special case, we also show that a networked multi-agent
system subject to a graph topology with constant uncertainties asymptotically
converges to the trajectories of a given reference model. Although the main
result of this paper is presented in the context of average consensus problem,
the proposed framework can be used for many other problems related to networked
multiagent systems with uncertain graph topologies.Comment: 14 pages, 2 figure
The Current State of Normative Agent-Based Systems
Recent years have seen an increase in the application of ideas from the social sciences to computational systems. Nowhere has this been more pronounced than in the domain of multiagent systems. Because multiagent systems are composed of multiple individual agents interacting with each other many parallels can be drawn to human and animal societies. One of the main challenges currently faced in multiagent systems research is that of social control. In particular, how can open multiagent systems be configured and organized given their constantly changing structure? One leading solution is to employ the use of social norms. In human societies, social norms are essential to regulation, coordination, and cooperation. The current trend of thinking is that these same principles can be applied to agent societies, of which multiagent systems are one type. In this article, we provide an introduction to and present a holistic viewpoint of the state of normative computing (computational solutions that employ ideas based on social norms.) To accomplish this, we (1) introduce social norms and their application to agent-based systems; (2) identify and describe a normative process abstracted from the existing research; and (3) discuss future directions for research in normative multiagent computing. The intent of this paper is to introduce new researchers to the ideas that underlie normative computing and survey the existing state of the art, as well as provide direction for future research.Norms, Normative Agents, Agents, Agent-Based System, Agent-Based Simulation, Agent-Based Modeling
Geometric Aspects of Multiagent Systems
Recent advances in Multiagent Systems (MAS) and Epistemic Logic within
Distributed Systems Theory, have used various combinatorial structures that
model both the geometry of the systems and the Kripke model structure of models
for the logic. Examining one of the simpler versions of these models,
interpreted systems, and the related Kripke semantics of the logic (an
epistemic logic with -agents), the similarities with the geometric /
homotopy theoretic structure of groupoid atlases is striking. These latter
objects arise in problems within algebraic K-theory, an area of algebra linked
to the study of decomposition and normal form theorems in linear algebra. They
have a natural well structured notion of path and constructions of path
objects, etc., that yield a rich homotopy theory.Comment: 14 pages, 1 eps figure, prepared for GETCO200
Coupled Replicator Equations for the Dynamics of Learning in Multiagent Systems
Starting with a group of reinforcement-learning agents we derive coupled
replicator equations that describe the dynamics of collective learning in
multiagent systems. We show that, although agents model their environment in a
self-interested way without sharing knowledge, a game dynamics emerges
naturally through environment-mediated interactions. An application to
rock-scissors-paper game interactions shows that the collective learning
dynamics exhibits a diversity of competitive and cooperative behaviors. These
include quasiperiodicity, stable limit cycles, intermittency, and deterministic
chaos--behaviors that should be expected in heterogeneous multiagent systems
described by the general replicator equations we derive.Comment: 4 pages, 3 figures,
http://www.santafe.edu/projects/CompMech/papers/credlmas.html; updated
references, corrected typos, changed conten
Inter-organizational Interoperability through integration of Multiagent, Web Service, and Semantic Web Technologies
This paper presents a software architecture for inter-organizational multiagent systems. The architecture integrates Web service technology into multiagent systems to overcome the technical interoperability problem of current multiagent systems in the fast growing service-oriented environments. We integrate Semantic Web technology to make multiagent systems semantically interoperable. We address the problem of interoperability regarding interfaces, messaging protocols, data exchanged, and security whilst considering a dynamic e-business environment. The proposed architecture enables service virtualization, secure service access across organizational boundaries, service-to-agent communication, and OWL reasoning within agents
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