Adaptive agent abstractions to speed up spatial agent-based simulations

Shirazi, Abbas Sarraf; Davison, Timothy; Mammen, Sebastian von; Denzinger, Jörg; Jacob, Christian

doi:10.1016/j.simpat.2013.09.001

Cited by 22 publications

(5 citation statements)

References 19 publications

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“…Of these, ABM provides an instinctive approach for describing many complex systems, as agents are regularly a suitable match to the individual and heterogeneous objects composing these systems. The local interactions of these objects, as well as their individual and adaptive behavior, are often critical for understanding global system response [4,5]. ABMs are commonly implemented as a stochastic process, and thus require multiple runs (observations) with distinct pseudo-random number generator (PRNG) seeds in order to have appropriate sample sizes for testing hypotheses and differentiating multiple scenarios under distinct parameterizations [6].…”

Section: Introductionmentioning

confidence: 99%

Model-independent comparison of simulation output

Fachada¹,

Lopes²,

Martins³

et al. 2017

Simulation Modelling Practice and Theory

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Computational models of complex systems are usually elaborate and sensitive to implementation details, characteristics which often affect their verification and validation. Model replication is a possible solution to this issue. It avoids biases associated with the language or toolkit used to develop the original model, not only promoting its verification and validation, but also fostering the credibility of the underlying conceptual model. However, different model implementations must be compared to assess their equivalence. The problem is, given two or more implementations of a stochastic model, how to prove that they display similar behavior? In this paper, we present a model comparison technique, which uses principal component analysis to convert simulation output into a set of linearly uncorrelated statistical measures, analyzable in a consistent, model-independent fashion. It is appropriate for ascertaining distributional equivalence of a model replication with its original implementation. Besides model-independence, this technique has three other desirable properties: a) it automatically selects output features that best explain implementation differences; b) it does not depend on the distributional properties of simulation output; and, c) it simplifies the modelers' work, as it can be used directly on simulation outputs. The proposed technique is shown to produce similar results to the manual or empirical selection of output features when applied to a well-studied reference model.

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Section: Introductionmentioning

confidence: 99%

Model-independent comparison of simulation output

Fachada¹,

Lopes²,

Martins³

et al. 2017

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

show abstract

“…For instance, Sarraf Shirazi et al (2014) introduce an abstraction technique for speeding up ABSs, which clusters agents with similar behaviors in different groups, and represents each group by a meta-agent. In a similar way, the current work also uses only one agent (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…However, these works do not pay attention to efficiency of such systems. On the other hand, the works related to the efficiency of ABSs such as the one of Sarraf Shirazi et al (2014) do not define whole development processes. Thus, to the best of authors' knowledge, there is not any previous work that is devoted to the definition of a development process specially aimed at constructing efficient ABSs.…”

Section: Introductionmentioning

confidence: 99%

PEABS: A Process for developing Efficient Agent-Based Simulators

García‐Magariño

Gómez-Rodríguez

Moreno

et al. 2015

Engineering Applications of Artificial Intelligence

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“…For example, while we present GPM as a useful learning framework, other supervised learning techniques such as artificial neural networks and support vector machines could be implemented in a similar fashion, serving as efficient approximations to standard physics-based simulations [39,40,64,65]. Furthermore, several other methods related to fidelity optimization and applications to data mining exist within the literature [66][67][68][69], and could be leveraged in conjunction with the presented framework. Also, when standard linear dimensionality reduction techniques such as principal component analysis (PCA) and nonlinear dimensionality reduction techniques such as kernel PCA are used to pre-condition the input feature space prior to supervised learning [70], computational time for model training can be further reduced.…”

Section: Study Limitations and Future Directionsmentioning

confidence: 99%

Enhancing physiologic simulations using supervised learning on coarse mesh solutions

Kolandaivelu

O’Brien

Shazly

et al. 2015

J. R. Soc. Interface.

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Computational modelling of physical and biochemical processes has emerged as a means of evaluating medical devices, offering new insights that explain current performance, inform future designs and even enable personalized use. Yet resource limitations force one to compromise with reduced order computational models and idealized assumptions that yield either qualitative descriptions or approximate, quantitative solutions to problems of interest. Considering endovascular drug delivery as an exemplary scenario, we used a supervised machine learning framework to process data generated from low fidelity coarse meshes and predict high fidelity solutions on refined mesh configurations. We considered two models simulating drug delivery to the arterial wall: (i) two-dimensional drug-coated balloons and (ii) three-dimensional drug-eluting stents. Simulations were performed on computational mesh configurations of increasing density. Supervised learners based on Gaussian process modelling were constructed from combinations of coarse mesh setting solutions of drug concentrations and nearest neighbourhood distance information as inputs, and higher fidelity mesh solutions as outputs. These learners were then used as computationally inexpensive surrogates to extend predictions using low fidelity information to higher levels of mesh refinement. The cross-validated, supervised learner-based predictions improved fidelity as compared with computational simulations performed at coarse level meshes—a result consistent across all outputs and computational models considered. Supervised learning on coarse mesh solutions can augment traditional physics-based modelling of complex physiologic phenomena. By obtaining efficient solutions at a fraction of the computational cost, this framework has the potential to transform how modelling approaches can be applied in the evaluation of medical technologies and their real-time administration in an increasingly personalized fashion.

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Adaptive agent abstractions to speed up spatial agent-based simulations

Cited by 22 publications

References 19 publications

Model-independent comparison of simulation output

Model-independent comparison of simulation output

PEABS: A Process for developing Efficient Agent-Based Simulators

Enhancing physiologic simulations using supervised learning on coarse mesh solutions

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