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

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.

Cellular self-organizing systems: A field-based behavior regulation approach

2014 ◽  
Vol 28 (2) ◽  
pp. 115-128 ◽  
Author(s):  
Yan Jin ◽  
Chang Chen
Keyword(s):  
Adaptive Systems ◽  
Control Mechanism ◽  
Global Effect ◽  
Behavior Regulation ◽  
Simulation Based ◽  
Regulative Control ◽  
High Level ◽  
And Control ◽  
Regulation Approach ◽  
Self Organizing

AbstractMultiagent systems have been considered as a potential solution for developing adaptive systems. In this research, a cellular self-organizing (CSO) approach is proposed for developing such multiagent adaptive systems. The design of CSO systems however is difficult because the global effect emerges from local actions and interactions that are often hard to specify and control. In order to achieve high-level flexible and robustness of CSO systems and retain the capability of specifying desired global effects, we propose a field-based regulative control mechanism, called field-based behavior regulation (FBR). FBR is a real-time, dynamical, distributed mechanism that regulates the emergence process for CSO systems to self-organize and self-reconfigure in complex operation environments. FBR characterizes the task environment in terms of “fields” and extends the system flexibility and robustness without imposing global control over local cells or agents. This paper describes the model of CSO systems and FBR, and demonstrates their effectiveness through simulation-based case studies.

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.

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.

Role-Based Modeling for Designing Agent Behavior in Self-Organizing Multi-Agent Systems

2018 ◽  
Vol 28 (01) ◽  
pp. 79-96
Author(s):  
Kemas M. Lhaksmana ◽  
Yohei Murakami ◽  
Toru Ishida
Keyword(s):  
Autonomous Systems ◽  
Systems Design ◽  
Role Modeling ◽  
Modeling Method ◽  
Multi Agent Systems ◽  
Smart Transportation ◽  
Agent Systems ◽  
Multi Agent ◽  
Role Based ◽  
Self Organizing

Self-organization has been proposed to be implemented in complex systems which require the automation capabilities to govern itself and to adapt upon changes. Self-organizing systems can be modeled as multi-agent systems (MAS) since they share common characteristics in that they consist of multiple autonomous systems. However, most existing MAS engineering methodologies do not fully support self-organizing systems design since they require predefined goals and agent behaviors, which is not the case in self-organizing systems. Another feature that is currently not supported for designing self-organizing MAS is the separation between the design of agent behaviors and behavior adaptation, i.e. how agents adapt their behaviors to respond upon changes. To tackle these issues, this paper proposes a role modeling method, in which agent behaviors are represented as roles, to design how agents perform behavior adaptation at runtime by switching between roles. The applicability of the proposed role modeling method is evaluated in a case study of a self-organizing smart transportation system.

Dynamic Generation and Control of Optimally Persistent Formation for Multi-agent Systems

2014 ◽  
Vol 39 (9) ◽  
pp. 1431-1438 ◽  
Author(s):  
Xiao-Yuan LUO ◽  
Shi-Kai SHAO ◽  
Xin-Ping GUAN ◽  
Yuan-Jie ZHAO
Keyword(s):  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Dynamic Generation ◽  
And Control
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