234,495 research outputs found
Models of Consensus for Multiple Agent Systems
Models of consensus are used to manage multiple agent systems in order to
choose between different recommendations provided by the system. It is assumed
that there is a central agent that solicits recommendations or plans from other
agents. That agent the n determines the consensus of the other agents, and
chooses the resultant consensus recommendation or plan. Voting schemes such as
this have been used in a variety of domains, including air traffic control.
This paper uses an analytic model to study the use of consensus in multiple
agent systems. The binomial model is used to study the probability that the
consensus judgment is correct or incorrect. That basic model is extended to
account for both different levels of agent competence and unequal prior odds.
The analysis of that model is critical in the investigation of multiple agent
systems, since the model leads us to conclude that in some cases consensus
judgment is not appropriate. In addition, the results allow us to determine how
many agents should be used to develop consensus decisions, which agents should
be used to develop consensus decisions and under which conditions the consensus
model should be used.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
Multi-Agent Consensus Seeking via Large Language Models
Multi-agent systems driven by large language models (LLMs) have shown
promising abilities for solving complex tasks in a collaborative manner. This
work considers a fundamental problem in multi-agent collaboration: consensus
seeking. When multiple agents work together, we are interested in how they can
reach a consensus through inter-agent negotiation. To that end, this work
studies a consensus-seeking task where the state of each agent is a numerical
value and they negotiate with each other to reach a consensus value. It is
revealed that when not explicitly directed on which strategy should be adopted,
the LLM-driven agents primarily use the average strategy for consensus seeking
although they may occasionally use some other strategies. Moreover, this work
analyzes the impact of the agent number, agent personality, and network
topology on the negotiation process. The findings reported in this work can
potentially lay the foundations for understanding the behaviors of LLM-driven
multi-agent systems for solving more complex tasks. Furthermore, LLM-driven
consensus seeking is applied to a multi-robot aggregation task. This
application demonstrates the potential of LLM-driven agents to achieve
zero-shot autonomous planning for multi-robot collaboration tasks. Project
website: westlakeintelligentrobotics.github.io/ConsensusLLM/
Novel Fuzzy Systems for Human-Autonomous Agent Teaming
University of Technology Sydney. Faculty of Engineering and Information Technology.The Multi-agent Teaming (MAT) systems that have been widely applied in many fields provide a novel method for establishing models, conducting the analysis, implementing complex tasks and so on. The agents in an MAT system can be defined as intelligent agents, machine agents and human agents based on a particular task to exhibit flexible behaviours. This research investigates various fuzzy models to resolve the problems of designing MAT systems. The main contributions are as follows. 1) For multiple-agent coordination, a hierarchical fuzzy system is proposed and applied to navigation and simultaneous arrival of mobile agents. 2) Explainable fuzzy systems are proposed. We developed an interpretable fuzzy model for human agents to understand the decision rules learned by machine agents and a fuzzy rule information visualisation framework for machine agents to understand human cognitive states. 3) Finally, the distributed fuzzy system is proposed to resolve the data privacy and high-dimensional data in designing MAT systems. A novel consensus learning is developed for the distributed fuzzy system to learn antecedent and consequent components
Opinion dynamics: models, extensions and external effects
Recently, social phenomena have received a lot of attention not only from
social scientists, but also from physicists, mathematicians and computer
scientists, in the emerging interdisciplinary field of complex system science.
Opinion dynamics is one of the processes studied, since opinions are the
drivers of human behaviour, and play a crucial role in many global challenges
that our complex world and societies are facing: global financial crises,
global pandemics, growth of cities, urbanisation and migration patterns, and
last but not least important, climate change and environmental sustainability
and protection. Opinion formation is a complex process affected by the
interplay of different elements, including the individual predisposition, the
influence of positive and negative peer interaction (social networks playing a
crucial role in this respect), the information each individual is exposed to,
and many others. Several models inspired from those in use in physics have been
developed to encompass many of these elements, and to allow for the
identification of the mechanisms involved in the opinion formation process and
the understanding of their role, with the practical aim of simulating opinion
formation and spreading under various conditions. These modelling schemes range
from binary simple models such as the voter model, to multi-dimensional
continuous approaches. Here, we provide a review of recent methods, focusing on
models employing both peer interaction and external information, and
emphasising the role that less studied mechanisms, such as disagreement, has in
driving the opinion dynamics. [...]Comment: 42 pages, 6 figure
Bounded Distributed Flocking Control of Nonholonomic Mobile Robots
There have been numerous studies on the problem of flocking control for
multiagent systems whose simplified models are presented in terms of point-mass
elements. Meanwhile, full dynamic models pose some challenging problems in
addressing the flocking control problem of mobile robots due to their
nonholonomic dynamic properties. Taking practical constraints into
consideration, we propose a novel approach to distributed flocking control of
nonholonomic mobile robots by bounded feedback. The flocking control objectives
consist of velocity consensus, collision avoidance, and cohesion maintenance
among mobile robots. A flocking control protocol which is based on the
information of neighbor mobile robots is constructed. The theoretical analysis
is conducted with the help of a Lyapunov-like function and graph theory.
Simulation results are shown to demonstrate the efficacy of the proposed
distributed flocking control scheme
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