Effect of Social Structuring in Self-Organizing Systems

2016 ◽  
Vol 138 (4) ◽  
Author(s):  
Newsha Khani ◽  
James Humann ◽  
Yan Jin
Keyword(s):  
Adaptive Systems ◽  
Systems Approach ◽  
Task Complexity ◽  
Multi Agent Systems ◽  
The Face ◽  
Multi Agent ◽  
Varying Environments ◽  
And Performance ◽  
Social Structuring ◽  
Self Organizing

Dealing with unforeseeable changing situations, often seen in exploratory and hazardous task domains, requires systems that can adapt to changing tasks and varying environments. The challenge for engineering design researchers and practitioners is how to design such adaptive systems. Taking advantage of the flexibility of multi-agent systems, a self-organizing systems approach has been proposed, in which mechanical cells or agents organize themselves as the environment and tasks change based on a set of predefined rules. To enable self-organizing systems to perform more realistic tasks, a two-field framework is introduced to capture task complexity and agent behaviors, and a rule-based social structuring mechanism is proposed to facilitate self-organizing for better performance. Computer simulation-based case studies were carried out to investigate how social structuring among agents, together with the size of agent population, can influence self-organizing system performance in the face of increasing task complexity. The simulation results provide design insights into task-driven social structures and their effect on the behavior and performance of self-organizing systems.

Building Complex Adaptive Systems

2008 ◽  
pp. 229-256 ◽  
Author(s):  
Jan Sudeikat ◽  
Wolfgang Renz
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Software Systems ◽  
Computational Techniques ◽  
Multi Agent Systems ◽  
Software Applications ◽  
Agent Oriented Software Engineering ◽  
Complex Adaptive ◽  
Multi Agent ◽  
Self Organizing

Agent Oriented Software-Engineering (AOSE) proposes the design of distributed software systems as collections of autonomous and pro-active actors, so-called agents. Since software applications results from agent interplay in Multi-Agent Systems (MAS), this design approach facilitates the construction of software applications that exhibit self-organizing and emergent dynamics. In this chapter, we examine the relation between self-organizing MAS and Complex Adaptive Systems (CAS), highlighting the resulting challenges for engineering approaches. We argue that AOSE developers need to be aware of the possible causes of complex system dynamics, which result from underlying feedback loops. In this respect current approaches to develop SO-MAS are analyzed, leading to a novel classification scheme of typically applied computational techniques. To relieve development efforts and bridge the gap between top-down engineering and bottom-up emerging phenomena, we discuss how multi-level analysis, so-called mesoscopic modeling, can be used to comprehend MAS dynamics and guide agent design, respectively iterative redesign.

OMACS

2009 ◽  
pp. 76-104 ◽  
Author(s):  
Scott A. DeLoach
Keyword(s):  
Systems Engineering ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Multi Agent Systems ◽  
Organization Model ◽  
Policy Specification ◽  
Complex Adaptive ◽  
The Face ◽  
Multi Agent ◽  
Computational Systems

This chapter introduces a suite of technologies for building complex, adaptive systems. It is based in the multi-agent systems paradigm and uses the Organization Model for Adaptive Computational Systems (OMACS). OMACS defines the knowledge needed about a system’s structure and capabilities to allow it to reorganize at runtime in the face of a changing environment and its agent’s capabilities. However, the OMACS model is only useful if it is supported by a set of methodologies, techniques, and architectures that allow it to be implemented effectively on a wide variety of systems. To this end, this chapter presents a suite of technologies including (1) the Organization-based Multiagent Systems Engineering (O-MaSE) methodology, (2) a set of policy specification techniques that allow an OMACS system to remain flexible while still providing guidance, and (3) a set of architectures and algorithms used to implement OMACSbased systems. The chapter also includes the presentation of a small OMACS-based system.

A Behavior Based Approach to Cellular Self-Organizing Systems Design

2011 ◽  
Author(s):  
Chang Chen ◽  
Yan Jin
Keyword(s):  
Adaptive Systems ◽  
Systems Design ◽  
Functional Requirements ◽  
Multi Agent Systems ◽  
Multi Agent ◽  
Behavior Design ◽  
Behavior Based ◽  
High Level ◽  
And Control ◽  
Self Organizing

Multi-agent systems (MAS) have been considered a potential solution for developing adaptive systems. The design of MAS however is difficult because the global effect emerges from local actions and interactions that can be hard to specify and control. In order to achieve high level resilience and robustness of MAS and retain the capability of specifying desired global effects, we propose a cellular self-organizing (CSO) system framework and a biologically inspired behavior based design approach (BDA) and a field based regulative control mechanism (FBR). The BDA approach links global functional requirements with the local behavior design of a CSO system. FBR is a real-time, dynamical, distributed mechanism that regulates the emergence process for CSOs to self-organize and self-reconfigure in complex operation environments. BDA and FBR together extend the system adaptability without imposing global control over local agents. This paper describes the models of CSO, BDA and FBR and demonstrates their effectiveness by presenting simulation based case studies in which CSO agents explore an unknown environment and move an object to designated locations.

Building Complex Adaptive Systems

2011 ◽  
pp. 767-787 ◽  
Author(s):  
Jan Sudeikat ◽  
Wolfgang Renz
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Software Systems ◽  
Computational Techniques ◽  
Multi Agent Systems ◽  
Software Applications ◽  
Agent Oriented Software Engineering ◽  
Complex Adaptive ◽  
Multi Agent ◽  
Self Organizing

Agent Oriented Software-Engineering (AOSE) proposes the design of distributed software systems as collections of autonomous and pro-active actors, so-called agents. Since software applications results from agent interplay in Multi-Agent Systems (MAS), this design approach facilitates the construction of software applications that exhibit self-organizing and emergent dynamics. In this chapter, we examine the relation between self-organizing MAS and Complex Adaptive Systems (CAS), highlighting the resulting challenges for engineering approaches. We argue that AOSE developers need to be aware of the possible causes of complex system dynamics, which result from underlying feedback loops. In this respect current approaches to develop SO-MAS are analyzed, leading to a novel classification scheme of typically applied computational techniques. To relieve development efforts and bridge the gap between top-down engineering and bottom-up emerging phenomena, we discuss how multi-level analysis, so-called mesoscopic modeling, can be used to comprehend MAS dynamics and guide agent design, respectively iterative redesign.

scholarly journals A Coalitional Distributed Model Predictive Control Perspective for a Cyber-Physical Multi-Agent Application

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4041
Author(s):  
Anca Maxim ◽  
Constantin-Florin Caruntu
Keyword(s):  
Model Predictive Control ◽  
Communication Networks ◽  
Predictive Control ◽  
Technological Development ◽  
Distributed Model ◽  
Multi Agent Systems ◽  
Test Scenario ◽  
Distributed Model Predictive Control ◽  
Multi Agent ◽  
And Performance

Following the current technological development and informational advancement, more and more physical systems have become interconnected and linked via communication networks. The objective of this work is the development of a Coalitional Distributed Model Predictive Control (C- DMPC) strategy suitable for controlling cyber-physical, multi-agent systems. The motivation behind this endeavour is to design a novel algorithm with a flexible control architecture by combining the advantages of classical DMPC with Coalitional MPC. The simulation results were achieved using a test scenario composed of four dynamically coupled sub-systems, connected through an unidirectional communication topology. The obtained results illustrate that, when the feasibility of the local optimization problem is lost, forming a coalition between neighbouring agents solves this shortcoming and maintains the functionality of the entire system. These findings successfully prove the efficiency and performance of the proposed coalitional DMPC method.

A Two-Layer Approach to Developing Self-Adaptive Multi-Agent Systems in Open Environment

2014 ◽  
Vol 6 (1) ◽  
pp. 65-85 ◽  
Author(s):  
Xinjun Mao ◽  
Menggao Dong ◽  
Haibin Zhu
Keyword(s):  
Reinforcement Learning ◽  
Adaptive Systems ◽  
Multi Agent Systems ◽  
Uncertain Environments ◽  
Implementation Framework ◽  
Fine Grain ◽  
Adaptation Mechanisms ◽  
Multi Agent ◽  
Self Adaptation ◽  
Self Adaptive

Development of self-adaptive systems situated in open and uncertain environments is a great challenge in the community of software engineering due to the unpredictability of environment changes and the variety of self-adaptation manners. Explicit specification of expected changes and various self-adaptations at design-time, an approach often adopted by developers, seems ineffective. This paper presents an agent-based approach that combines two-layer self-adaptation mechanisms and reinforcement learning together to support the development and running of self-adaptive systems. The approach takes self-adaptive systems as multi-agent organizations and enables the agent itself to make decisions on self-adaptation by learning at run-time and at different levels. The proposed self-adaptation mechanisms that are based on organization metaphors enable self-adaptation at two layers: fine-grain behavior level and coarse-grain organization level. Corresponding reinforcement learning algorithms on self-adaptation are designed and integrated with the two-layer self-adaptation mechanisms. This paper further details developmental technologies, based on the above approach, in establishing self-adaptive systems, including extended software architecture for self-adaptation, an implementation framework, and a development process. A case study and experiment evaluations are conducted to illustrate the effectiveness of the proposed approach.

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