The distributed simulation of multiagent systems

Logan, Brian; Theodoropoulos, Georgios

doi:10.1109/5.910853

Cited by 128 publications

(85 citation statements)

References 26 publications

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“…Different types of external event will have different effects on the shared state. The sphere of influence of an event is defined as the set of shared state variables read or written by the event [11]. The sphere of influences of an LP p i over time interval ([t 1 , t 2 ]), s(p i ) is the union of spheres of influence of events generated by p i .…”

Section: The Pdes-mas Frameworkmentioning

confidence: 99%

“…In [11], we propose that the simulation initially starts with a single centralised CLP marked as CLP 0 (see figure 1). As the simulation execution proceeds, CLP 0 may become a bottleneck due to the increasing load of managing SSVs.…”

Section: Figure 1 Illustrating the Pdes-mas Frameworkmentioning

confidence: 99%

“…The SoI describe the access pattern of the simulation models on the shared state in a given time interval. In more recent work we have realised this approach in the PDES-MAS framework [11].…”

Section: Introductionmentioning

confidence: 99%

“…In [9,11] distributed simulation technology has been identified as an effective and flexible approach for designers to investigate MAS. Decentralized, event-driven distributed simulation is particularly suitable for modelling with inherent asynchronous parallelism, such as MAS.…”

Section: Introductionmentioning

confidence: 99%

“…In an effort to address this problem and provide a generic approach for dynamic load balancing and interest management in distributed simulation of MAS, in [11,19] we introduced the notion of Spheres of Influence (SoI) as an approach to dynamically decompose and distribute the shared state. The SoI describe the access pattern of the simulation models on the shared state in a given time interval.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An adaptive load management mechanism for distributed simulation of multi-agent systems

Oguara

Chen

Theodoropoulos

et al. 2005

Ninth IEEE International Symposium on Distributed Simulation and Real-Time Applications

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Section: The Pdes-mas Frameworkmentioning

confidence: 99%

Section: Figure 1 Illustrating the Pdes-mas Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An adaptive load management mechanism for distributed simulation of multi-agent systems

Oguara

Chen

Theodoropoulos

et al. 2005

Ninth IEEE International Symposium on Distributed Simulation and Real-Time Applications

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Analysing probabilistically constrained optimism

Lees

Logan

Theodoropoulos

2009

Concurrency and Computation

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In previous work we presented the DTRD algorithm, an optimistic synchronization algorithm for parallel discrete event simulation of multi‐agent systems, and showed that it outperforms Time Warp and time windows on a range of test cases. DTRD uses a decision‐theoretic model of rollback to derive an optimal time to delay read event so as to maximize the rate of LVT progression. The algorithm assumes that the inter‐arrival times (both virtual and real) of events are normally distributed. In this paper we present a more detailed evaluation of the DTRD algorithm, and specifically how the performance of the algorithm is affected when the inter‐arrival times do not follow the assumed distributions. Our analysis suggests that the performance of the algorithm is relatively insensitive to events whose inter‐arrival times are not normally distributed. However, as the variance of event inter‐arrival times increases, its performance degrades to that of Time Warp. The evaluation approach we present is generally applicable, and we sketch how a similar analysis may be performed for two other decision‐theoretic optimistic synchronization algorithms. Copyright © 2009 John Wiley & Sons, Ltd.

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Efficient environment management for distributed simulation of large‐scale situated multi‐agent systems

Cicirelli

Giordano

Nigro

2014

Concurrency and Computation

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SummaryMulti‐agent systems have been proven very effective for the modelling and simulation (M&S) of complex systems like those related to biology, engineering, social sciences and so forth. The intrinsic spatial character of many such systems leads to the definition of a situated agent. A situated agent owns spatial coordinates and acts and interacts with its peers in a hosting territory. In the context of parallel/distributed simulation of situated agent models, the territory represents a huge shared variable that requires careful handling. Frequent access by agents to territory information easily becomes a bottleneck degrading system performance and scalability. This paper proposes an original approach to modelling and distributed simulation of large‐scale situated multi‐agent systems. Time management is exploited for resolving conflicts and achieving data consistency while accessing the environment. The approach allows a simplification of the M&S tasks by making the modeller unaware of distribution concerns while ensuring the achievement of good scalability and performance during the distributed simulation. Practical aspects of the approach are demonstrated through some modelling examples based on Tileworld. Copyright © 2014 John Wiley & Sons, Ltd.

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The distributed simulation of multiagent systems

Cited by 128 publications

References 26 publications

An adaptive load management mechanism for distributed simulation of multi-agent systems

An adaptive load management mechanism for distributed simulation of multi-agent systems

Analysing probabilistically constrained optimism

Efficient environment management for distributed simulation of large‐scale situated multi‐agent systems

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