scholarly journals Progress in simulating human geography: Assemblage theory and the practice of multi-agent artificial intelligence modeling

2021 ◽  
pp. 030913252110595
Author(s):  
F. LeRon Shults
Keyword(s):  
Artificial Intelligence ◽  
Computer Modeling ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Assemblage Theory ◽  
Material Systems ◽  
Complex Adaptive ◽  
Multi Agent ◽  
Intelligence Modeling ◽  
Philosophical Understanding

Over the last few years, there has been an explosion of interest in assemblage theory among human geographers. During this same period, a growing number of scholars in the field have utilized computational methodologies to simulate the complex adaptive systems they study. However, very little attention has been paid to the connections between these two developments. This article outlines those connections and argues that more explicitly integrating assemblage theory and computer modeling can encourage a more robust philosophical understanding of both and facilitate progress in scientific research on the ways in which complex socio-material systems form and transform.

scholarly journals Norm-Based Behavior Regulating Technique for Multi-Agent in Complex Adaptive Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 126662-126678 ◽  
Author(s):  
Moamin A. Mahmoud ◽  
Mohd Sharifuddin Ahmad ◽  
Salama A. Mostafa
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Complex Adaptive ◽  
Multi Agent

WHITHER CYBERNETICS: PAST ACHIEVEMENTS AND FUTURE PROSPECTS

Kybernetes ◽  
1980 ◽  
Vol 9 (4) ◽  
pp. 295-299 ◽  
Author(s):  
A.M. ANDREW
Keyword(s):  
Artificial Intelligence ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Current Work ◽  
Central Theme ◽  
Thought Processes ◽  
Future Prospects ◽  
Verbal Thought ◽  
Complex Adaptive ◽  
Fundamental Limitation

The meaning and connotations of cybernetics are reviewed with the conclusion that complex adaptive systems are the central theme. Formal and informal approaches are contrasted and shown to be compatible. The most spectacular achievements of cybernetics are in the area of artificial intelligence, but current work under that heading is subject to a fundamental limitation. Simulation of non‐verbal thought processes can only be achieved by studying the transition from non‐succinct to succinct information representation in cybernetic systems. Attention must be given to the evolution and operation of the meta‐goal of succinct representation of information.

A Complex Adaptive Systems-Based Enterprise Knowledge Sharing Model

2011 ◽  
pp. 35-59
Author(s):  
Cynthia T. Small ◽  
Andrew P. Sage
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Knowledge Sharing ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Knowledge Workers ◽  
Emergent Behavior ◽  
Agent Simulation ◽  
Complex Adaptive ◽  
Multi Agent

This paper describes a complex adaptive systems (CAS)-based enterprise knowledge-sharing (KnS) model. The CAS-based enterprise KnS model consists of a CAS-based KnS framework and a multi-agent simulation model. Enterprise knowledge sharing is modeled as the emergent behavior of knowledge workers interacting with the KnS environment and other knowledge workers. The CAS-based enterprise KnS model is developed to aid knowledge management (KM) leadership and other KnS researchers in gaining an enhanced understanding of KnS behavior and its influences. A premise of this research is that a better understanding of KnS influences can result in enhanced decision-making of KnS interventions that can result in improvements in KnS behavior.

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.

A Complex Adaptive Systems-Based Enterprise Knowledge Sharing Model

2009 ◽  
pp. 281-300
Author(s):  
Andrew P. Sage ◽  
Cynthia T. Small
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Knowledge Sharing ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Knowledge Workers ◽  
Emergent Behavior ◽  
Agent Simulation ◽  
Complex Adaptive ◽  
Multi Agent

This chapter describes a complex adaptive systems (CAS)-based enterprise knowledge-sharing (KnS) model. The CAS-based enterprise KnS model consists of a CAS-based KnS framework and a multi-agent simulation model. Enterprise knowledge sharing is modeled as the emergent behavior of knowledge workers interacting with the KnS environment and other knowledge workers. The CAS-based enterprise KnS model is developed to aid Knowledge Management (KM) leadership and other KnS researchers in gaining an enhanced understanding of KnS behavior and its influences. A premise of this research is that a better understanding of KnS influences can result in enhanced decision-making of KnS interventions that can result in improvements in KnS behavior.

A Complex Adaptive Systems-Based Enterprise Knowledge Sharing Model

2010 ◽  
pp. 831-849
Author(s):  
Cynthia T. Small ◽  
Andrew P. Sage
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Knowledge Sharing ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Knowledge Workers ◽  
Emergent Behavior ◽  
Agent Simulation ◽  
Complex Adaptive ◽  
Multi Agent

This paper describes a complex adaptive systems (CAS)-based enterprise knowledge-sharing (KnS) model. The CAS-based enterprise KnS model consists of a CAS-based KnS framework and a multi-agent simulation model. Enterprise knowledge sharing is modeled as the emergent behavior of knowledge workers interacting with the KnS environment and other knowledge workers. The CAS-based enterprise KnS model is developed to aid knowledge management (KM) leadership and other KnS researchers in gaining an enhanced understanding of KnS behavior and its influences. A premise of this research is that a better understanding of KnS influences can result in enhanced decision-making of KnS interventions that can result in improvements in KnS behavior.

Artificial Intelligence Applied

2019 ◽  
pp. 115-144
Author(s):  
Gaetano Bruno Ronsivalle ◽  
Arianna Boldi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Resources ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Information Technologies ◽  
Qualitative Data ◽  
Effective Management ◽  
For Profit ◽  
Complex Adaptive

The purpose of the chapter is to present some real applications of the most advanced information technologies in complex adaptive systems like for-profit companies and organizations. In particular, the authors present the application of machine learning and artificial intelligence to support some of the activities that are strategic for an effective management of human resources. The tools have been applied to analyze the professional profiles (competencies, skills, knowledge, and activities), to evaluate the candidates for hiring and selection, to assess the competences in order to obtain a certification, or to prove the results of a training course. For each project, the authors provide a description of 1) the context, 2) the problem, 3) the solution implemented, 4) an analysis of the advantages and the limits of the solution. All these cases offer quantitative and qualitative data to sustain the thesis: artificial intelligence is a tool that can help humans managing the complexity levels of the so-called Anthropocene era we live in.

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.

Research on Modeling and Simulation of Complex Adaptive Systems Based on Multi-Agent

2013 ◽  
Vol 756-759 ◽  
pp. 4497-4501
Author(s):  
Shan Liang Liao ◽  
Jia Zhe Lai
Keyword(s):  
Modeling And Simulation ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Complex Adaptive System ◽  
Rapid Development ◽  
Simulation Methods ◽  
Agent Modeling ◽  
Modeling Process ◽  
Complex Adaptive ◽  
Multi Agent

With the rapid development of complex adaptive systems, traditional modeling and simulation methods have been unable to meet the need of accurate modeling of complex adaptive systems. Therefore, we need a way to accurately describe the characteristics of complex adaptive systems modeling and simulation. Multi-Agent modeling and simulation methods focus on the study of the interaction between multiple subjects. It is an effective way to study complex adaptive system. This paper analyzes the multi-agent modeling process, sets up a rumor spreading model of a small social network through multi-agent modeling and simulation methods,. The simulation results verify the correctness and accuracy of the model.

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