Integrating Data Mining and Agent Based Modeling and Simulation

Baqueiro, Omar; Wang, Yanbo J.; McBurney, Peter; Coenen, Frans

doi:10.1007/978-3-642-03067-3_18

Cited by 20 publications

(11 citation statements)

References 29 publications

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“…Baqueiro et al [87] suggest that applications of data science within ABMS may be separated into two broad categories: endogenous and exogenous. Endogenous strategies involve the use of data science by the agents in the simulation to improve their capabilities, thereby effectively increasing their intelligence and the complexity of the model.…”

Section: Integration With Abmsmentioning

confidence: 99%

“…Improving the intelligence of agents in the simulation can be achieved by incorporating methods such as regression analysis or classifier systems into the decision models of agents [87,88]. Dogra et al [89] apply this technique to allow agents to change their individual processes as a result of environmental dynamics, thus retaining the validity of their ABM over time.…”

Section: Integration With Abmsmentioning

confidence: 99%

“…Once an ABM has been calibrated and validated, it can be used to generate a set of quasi-real data of the appropriate size and quality for specific data mining tasks [87]. This is particularly valuable for organisations that may have only recently begun to gather datasets concerning their processes and so do not have a wealth of historical data to rely on [95].…”

Section: Integration With Abmsmentioning

confidence: 99%

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Case Study for the Return on Investment of Internet of Things Using Agent-Based Modelling and Data Science

Houston

Gooberman-Hill

Mathie

et al. 2017

Systems

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Abstract:As technology advances towards new paradigms such as the Internet of Things, there is a desire among business leaders for a reliable method to determine the value of supporting these ventures. Traditional simulation and analysis techniques cannot model the complex systems inherent in fields such as infrastructure asset management, or suffer from a lack of data on which to build a prediction. Agent-based modelling, through an integration with data science, presents an attractive simulation method to capture these underlying complexities and provide a solution. The aim of this work is to investigate this integration as a refined process for answering practical business questions. A specific case study is addressed to assess the return on investment of installing condition monitoring sensors on lift assets in a London Underground station. An agent-based model is developed for this purpose, supported by analysis from historical data. The simulation results demonstrate how returns can be achieved and highlight features induced as a result of stochasticity in the model. Suggestions of future research paths are additionally outlined.

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Section: Integration With Abmsmentioning

confidence: 99%

Section: Integration With Abmsmentioning

confidence: 99%

Section: Integration With Abmsmentioning

confidence: 99%

See 1 more Smart Citation

Case Study for the Return on Investment of Internet of Things Using Agent-Based Modelling and Data Science

Houston

Gooberman-Hill

Mathie

et al. 2017

Systems

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“…In [14] the authors presented an overview of bi-directional integration of Data Mining (DM) and the agent-based modeling and simulation (ABMS) [application of DM in ABMS / application of ABMS in DM]. This study proposes a conceptual framework and a presentation of the advantages that can be provided by this integration.…”

Section: Related Workmentioning

confidence: 99%

How Data Mining Techniques Can Improve Simulation Studies

Saoud¹,

Boubetra²,

S³

2014

IJCTE

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Abstract-Researchers take years and even decades of observation in order to analyze socio-economic phenomenon. Whereas the agent-based modeling simulation (ABMS) provides a new issue by offering the possibility to create virtual societies in which individuals and organizations are directly represented with their observed interactions. As it is known simulation generates and consumes a large amount of data. The analysis of these data which may contain implicit and hidden information will always remain a very difficult phase in the ABMS. As a solution to this problematic, the use of data mining techniques can contribute to the right analysis of the phenomena that emerges in these systems. In this paper we aim the investigation of agent-based modeling simulation and data mining techniques.Index Terms-Agent-based modeling and simulation, data mining, analysis behaviors, emergence.

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“…In [Baqueiro et al, 2009] werden zwei Ansätze diskutiert: Einerseits können Lerntechniken in eine Agenten-Simulation integriert werden und andererseits können Simulationsergebnisse verwendet werden, um daraus Regeln zu lernen. Im Bereich der Adaptive Traffic Control Systems (ATCS) werden ML verwendet, um auf Repräsentationen von Verkehrssituationen -z.B.…”

Section: Verwandte Arbeitenunclassified

Akteursorientierte multimodale Straßenverkehrssimulation

Dallmeyer

2014

Simulation Des Straßenverkehrs in Der Großstadt

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Integrating Data Mining and Agent Based Modeling and Simulation

Cited by 20 publications

References 29 publications

Case Study for the Return on Investment of Internet of Things Using Agent-Based Modelling and Data Science

Case Study for the Return on Investment of Internet of Things Using Agent-Based Modelling and Data Science

How Data Mining Techniques Can Improve Simulation Studies

Akteursorientierte multimodale Straßenverkehrssimulation

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