Modelling spatio-temporal systems exhibiting multi-scale behaviour is a powerful tool in many branches of science, yet it still presents significant challenges. Here, we consider a general two-layer (agent-environment) modelling framework, where spatially distributed agents behave according to external inputs and internal computation; this behaviour may include influencing their immediate environment, creating a medium over which agent-agent interaction signals can be transmitted. We propose a novel simulation strategy based on a statistical abstraction of the agent layer, which is typically the most detailed component of the model and can incur significant computational cost in simulation. The abstraction makes use of Gaussian Processes, a powerful class of non-parametric regression techniques from Bayesian Machine Learning, to estimate the agent's behaviour given the environmental input. We show on two biological case studies how this technique can be used to speed up simulations and provide further insights into model behaviour.

Statistical abstraction for multi-scale spatio-temporal systems / Michaelides, M.; Hillston, J.; Sanguinetti, G.. - In: ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION. - ISSN 1049-3301. - 29:4(2019), pp. 1-29. [10.1145/3366023]

Statistical abstraction for multi-scale spatio-temporal systems

Sanguinetti G.
2019

Abstract

Modelling spatio-temporal systems exhibiting multi-scale behaviour is a powerful tool in many branches of science, yet it still presents significant challenges. Here, we consider a general two-layer (agent-environment) modelling framework, where spatially distributed agents behave according to external inputs and internal computation; this behaviour may include influencing their immediate environment, creating a medium over which agent-agent interaction signals can be transmitted. We propose a novel simulation strategy based on a statistical abstraction of the agent layer, which is typically the most detailed component of the model and can incur significant computational cost in simulation. The abstraction makes use of Gaussian Processes, a powerful class of non-parametric regression techniques from Bayesian Machine Learning, to estimate the agent's behaviour given the environmental input. We show on two biological case studies how this technique can be used to speed up simulations and provide further insights into model behaviour.
29
4
1
29
22
Michaelides, M.; Hillston, J.; Sanguinetti, G.
File in questo prodotto:
File Dimensione Formato  
Statistical_abstraction_for_multi_scale_MICHAELIDES_DOA04042019_AFV.pdf

non disponibili

Tipologia: Documento in Post-print
Licenza: Non specificato
Dimensione 3.93 MB
Formato Adobe PDF
3.93 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/117190
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 0
social impact