scholarly journals Multi-agent Finite-time Ideal Convergence Algorithm Based on Bilinear Manifold

2018 ◽  
Vol 14 (04) ◽  
pp. 20
Author(s):  
Feng Yuan
Keyword(s):  
Sufficient Conditions ◽  
Network Control ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Ideal Convergence ◽  
Agent Systems ◽  
Convergence Process ◽  
Multi Agent ◽  
Necessary And Sufficient

<p class="17">Based on the traditional belief updating model, a concept of belief distance is put forward in this paper. On the basis of this concept, a new belief distance updating model for the multi-agent system is proposed, and the rationality of the model is proved. In this model, the belief distance updating process of the multi-agent system is described, and the linear system is used to describe the belief convergence process of the multi-agent system, which has simplified the complexity of the belief reachability analysis for the multi-agent system. On the basis of this model, the belief reachability for the multi-agent systems in the network control is analyzed, and the necessary and sufficient conditions for judging the belief reachability of the multi-agent system are given.</p>

Bounded consensus tracking control of second-order multi-agent systems with active leader and jointly connected topology

2016 ◽  
Vol 40 (2) ◽  
pp. 504-513 ◽  
Author(s):  
Lei Chen ◽  
Kaiyu Qin ◽  
Jiangping Hu
Keyword(s):  
Tracking Control ◽  
Sufficient Conditions ◽  
Interaction Network ◽  
Second Order ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Agent Systems ◽  
Strongly Connected ◽  
Multi Agent

In this paper, we investigate a tracking control problem for second-order multi-agent systems. Here, the leader is self-active and cannot be completely measured by all the followers. The interaction network associated with the leader–follower multi-agent system is described by a jointly connected topology, where the topology switches over time and is not strongly connected during each time subinterval. We consider a consensus control of the multi-agent system with or without time delay and propose two categories of neighbour-based control rules for every agent to track the leader, then provide sufficient conditions to ensure that all agents follow the leader, and meanwhile, the tracking errors can be estimated. Finally, some simulation results are presented to demonstrate our theoretical results.

Evolutionary Computation as a Paradigm for Engineering Emergent Behaviour in Multi-Agent Systems

2011 ◽  
pp. 118-136 ◽  
Author(s):  
Robert E. Smith ◽  
Claudio Bonacina
Keyword(s):  
Evolutionary Computation ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Future Directions ◽  
Agent Based ◽  
Agent Systems ◽  
Emergent Behaviour ◽  
Shaping System ◽  
Multi Agent

In the multi-agent system (MAS) context, the theories and practices of evolutionary computation (EC) have new implications, particularly with regard to engineering and shaping system behaviors. Thus, it is important that we consider the embodiment of EC in “real” agents, that is, agents that involve the real restrictions of time and space within MASs. In this chapter, we address these issues in three ways. First, we relate the foundations of EC theory to MAS and consider how general interactions among agents fit within this theory. Second, we introduce a platform independent agent system to assure that our EC methods work within the generic, but realistic, constraints of agents. Finally, we introduce an agent-based system of EC objects. Concluding sections discuss implications and future directions.

scholarly journals On Partially Controlled Multi-Agent Systems

1996 ◽  
Vol 4 ◽  
pp. 477-507 ◽  
Author(s):  
R. I. Brafman ◽  
M. Tennenholtz
Keyword(s):  
Expected Utility ◽  
The Other ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Direct Control ◽  
Agent System ◽  
Agent Systems ◽  
Multi Agent ◽  
Game Theoretic

Motivated by the control theoretic distinction between controllable and uncontrollable events, we distinguish between two types of agents within a multi-agent system: controllable agents, which are directly controlled by the system's designer, and uncontrollable agents, which are not under the designer's direct control. We refer to such systems as partially controlled multi-agent systems, and we investigate how one might influence the behavior of the uncontrolled agents through appropriate design of the controlled agents. In particular, we wish to understand which problems are naturally described in these terms, what methods can be applied to influence the uncontrollable agents, the effectiveness of such methods, and whether similar methods work across different domains. Using a game-theoretic framework, this paper studies the design of partially controlled multi-agent systems in two contexts: in one context, the uncontrollable agents are expected utility maximizers, while in the other they are reinforcement learners. We suggest different techniques for controlling agents' behavior in each domain, assess their success, and examine their relationship.

scholarly journals Multi-agent system for computer science and engineering education

2017 ◽  
Vol 58 ◽  
Author(s):  
Jaroslav Meleško ◽  
Eugenijus Kurilovas ◽  
Irina Krikun
Keyword(s):  
Learning Styles ◽  
Intelligent Agents ◽  
Educational Data Mining ◽  
Software Agent ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Agent Systems ◽  
Multi Agent ◽  
Identification Software

The paper aims to analyse application trends of intelligent multi-agent systems to personalise learning. First of all, systematic literature review was performed. Based on the systematic review analysis, the main trends on applying multi-agent systems to personalise learning were identified. Second, main requirements and components for an educational multi-agent system were formulated. Third, based on these components a model of intelligent personalized system is proposed. The system employs five intelligent agents: (1) learning styles identification software agent, (2) learner profile creation software agent, (3) pedagogical suitability software agent, (4) optimal learning units/scenarios creation software agent, and (5) learning analytics/educational data mining software agent.

Evolutionary Computation as a Paradigm for Engineering Emergent Behaviour in Multi-Agent Systems

2011 ◽  
pp. 2338-2350
Author(s):  
Robert E. Smith ◽  
Claudia Bonacina
Keyword(s):  
Evolutionary Computation ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Future Directions ◽  
Agent Based ◽  
Agent Systems ◽  
Emergent Behaviour ◽  
Shaping System ◽  
Multi Agent

In the multi-agent system (MAS) context, the theories and practices of evolutionary computation (EC) have new implications, particularly with regard to engineering and shaping system behaviors. Thus, it is important that we consider the embodiment of EC in “real” agents, that is, agents that involve the real restrictions of time and space within MASs. In this chapter, we address these issues in three ways. First, we relate the foundations of EC theory to MAS and consider how general interactions among agents fit within this theory. Second, we introduce a platform independent agent system to assure that our EC methods work within the generic, but realistic, constraints of agents. Finally, we introduce an agent-based system of EC objects. Concluding sections discuss implications and future directions.

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