Multi-agent Systems and Distributed Data Mining

2004 ◽  
pp. 1-15 ◽  
Author(s):  
Chris Giannella ◽  
Ruchita Bhargava ◽  
Hillol Kargupta
Keyword(s):  
Data Mining ◽  
Distributed Data Mining ◽  
Distributed Data ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

Agent mining approaches: an ontological view

2021 ◽  
Vol 36 ◽  
Author(s):  
Emmanuelle Grislin-Le Strugeon ◽  
Kathia Marcal de Oliveira ◽  
Dorsaf Zekri ◽  
Marie Thilliez
Keyword(s):  
Data Mining ◽  
Literature Review ◽  
Knowledge Discovery ◽  
Systematic Literature Review ◽  
Software Agent ◽  
Application Domain ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Agent Mining ◽  
Multi Agent

Abstract Introduced as an interdisciplinary area that combines multi-agent systems, data mining and knowledge discovery, agent mining is currently in practice. To develop agent mining applications involves a combination of different approaches (model, architecture, technique and so on) from software agent and data mining (DM) areas. This paper presents an investigation of the approaches used in the agent mining systems by deeply analyzing 121 papers resulting from a systematic literature review. An ontology was defined to capitalize the knowledge collected from this study. The ontology is organized according to seven main facets: the problem addressed, the application domain, the agent-related and the mining-related elements, the models, processes and algorithms. This ontology is aimed at providing support to decisions about agent mining application design.

A novel bipartite consensus tracking scheme for unknown nonlinear multi-agent systems: Theoretical analysis and applications

2021 ◽  
pp. 095965182110609
Author(s):  
Huarong Zhao ◽  
Li Peng ◽  
Peiliang Wu ◽  
Hongnian Yu
Keyword(s):  
Tracking Error ◽  
Data Driven ◽  
Small Range ◽  
Distributed Data ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Consensus Tracking ◽  
Multi Agent ◽  
Bipartite Consensus ◽  
Virtual Leader

This article proposes a novel distributed data-driven bipartite consensus tracking scheme for bipartite consensus tracking problems of multi-agent systems with bounded disturbances and coopetition networks. The proposed scheme only uses the input/output data of each agent without requiring the agents’ dynamics. We obtain the equivalent dynamic linearization data model for a controlled plant using the dynamic linearization technique based on the pseudo partial derivative. Considering the cooperative and competitive interactions among agents, the proposed method ensures that agents with adversarial relationships implement bipartite consensus tracking tasks even if only a subset of agents can access the information from the virtual leader. Moreover, the strict proof process of convergence properties reveals that the tracking error coverages to a small range around the origin. We also establish a set of software and hardware platform to demonstrate the effectiveness of the proposed distributed data-driven bipartite consensus tracking method.

Sign in / Sign up

Export Citation Format

Share Document