PANDORA: a multi-agent system using paraconsistent logic

Angelotti, Elaini Simoni; Scalabrin, Edson Emílio; Avila, Braulio C.

doi:10.1109/iccima.2001.970493

Cited by 10 publications

(9 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RepastHPC [12], a distributed version of Repast Symphony, uses the Repast's concepts of projections and contexts and adapts them for distributed environments. Pandora [2] is close to RepastHPC and automatically generates the code required for inter-server communications. GridABM [17] is based on Repast Symphony but takes another approach and proposes to the programmer general templates to be adapted to the communication topology of his simulation.…”

Section: Related Workmentioning

confidence: 99%

Distributed Agent-Based Traffic Simulations

Mastio

Zargayouna

Scemama

et al. 2018

IEEE Intell. Transport. Syst. Mag.

View full text Add to dashboard Cite

Modeling and simulation play an important role in transportation networks analysis. With the widespread of personalized real-time information sources, it is relevant for the simulation model to be individual-centered. The agent-based simulation is the most promising paradigm in this context. However, representing the movements of realistic numbers of travelers within reasonable execution times requires significant computational resources. It also requires relevant methods, architectures and algorithms that respect the characteristics of transportation networks. In this paper, we tackle the problem of using high-performance computing for agent-based traffic simulations. To do so, we define two generic agent-based simulation models, representing the existing sequential agent-based traffic simulations. The first model is macroscopic, in which travelers do not interact directly and use a fundamental diagram of traffic flow to continuously compute their speeds. The second model is microscopic, in which travelers interact with their neighbors to adapt their speeds to their surrounding environment. We define patterns to distribute these simulations in a high-performance environment. The first distributes agents equally between available computation units. The second pattern splits the environment over the different units. We finally propose a diffusive method to dynamically balance the load between units during execution. The results show that agent-based distribution is more efficient with macroscopic simulations, with a speedup of 6 compared to the sequential version, while environmentbased distribution is more efficient with microscopic simulations, with a speedup of 14. Our diffusive load-balancing algorithm improves further the performance of the environment based approach by 150%.

show abstract

Section: Related Workmentioning

confidence: 99%

Distributed Agent-Based Traffic Simulations

Mastio

Zargayouna

Scemama

et al. 2018

IEEE Intell. Transport. Syst. Mag.

View full text Add to dashboard Cite

show abstract

“…RepastHPC [17], a distributed version of Repast Simphony, uses the Repast's concepts of projections and contexts and adapts them for distributed environments. Pandora [2] is close to RepastHPC and automatically generates the code required for inter-server communications. GridABM [25] is based on Repast Simphony but takes another approach and proposes to the programmer general templates to be adapted to the communication topology of her simulation.…”

Section: Distributing Traffic Simulationmentioning

confidence: 99%

Two distribution methods for multiagent traffic simulations

Mastio

Zargayouna

Scemama

et al. 2018

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

Modeling and simulation play an important role in transportation networks analysis. With the widespread use of personalized real-time information sources, the behavior of the simulation depends heavily on individual travelers reactions to the received information. As a consequence, it is relevant for the simulation model to be individual-centered, and multiagent simulation is the most promising paradigm in this context. However, representing the movements of realistic numbers of travelers within reasonable execution times requires significant computational resources. It also requires relevant methods, architectures and algorithms that respect the characteristics of transportation networks. In this paper, we define two multiagent simulation models representing the existing sequential multiagent traffic simulations. The first model is fundamental diagram-based model, in which travelers do not interact directly and use a fundamental diagram of traffic flow to continuously compute their speeds. The second model is car-following based, in which travelers interact with their neighbors to adapt their speeds to their surrounding environment. Then we define patterns to distribute these simulations in a high-performance environment. The first is agent-based and distributes agents equally between available computation units. The second pattern is environment-based and partitions the environment over the different units. The results show that agent-based distribution is more efficient with fundamental diagram-based model simulations while environment-based distribution is more efficient with car following-based simulations.

show abstract

“…Parallel and distributed multi‐agent platforms (PDMAS) exist that facilitate the implementation of parallel models. We can cite RepastHPC, D‐Mason, Pandora, and Flame . All these platforms provide not only a native support for parallel execution of multi‐agent models but also important mechanisms of distribution, migration, and load balancing for the simulation run.…”

Section: Context and Related Workmentioning

confidence: 99%

Nested graphs: A model to efficiently distribute multi‐agent systems on HPC clusters

Rousset

Herrmann

Lang

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

Summary Computational simulation is becoming increasingly important in numerous research fields. Depending on the modeled system, several methods such as differential equations or Monte‐Carlo simulations may be used to represent the system behavior. The amount of computation and memory needed to run a simulation depends on its size and precision, and large simulations usually lead to long runs, thus requiring to adapt the model to a parallel system. Complex systems are often simulated using multi‐agent systems (MASs). While linear system based models benefit from a large set of tools to take advantage of parallel resources, multi‐agent systems suffer from a lack of platforms that ease the use of such resources. In this paper, we propose the use of Nested Graphs for a new modeling approach that allows the design of large, complex, and multi‐scale multi‐agent models, which can efficiently be distributed on parallel resources. Nested Graphs are formally defined and are illustrated on the well‐known predator‐prey model. We also introduce PDMAS (parallel and distributed multi‐agent system): a platform that implements the Nested Graph modeling approach to ease the distribution of multi‐agent models on High Performance Computing clusters. Performance results are presented to validate the efficiency of the resulting models.

show abstract

PANDORA: a multi-agent system using paraconsistent logic

Cited by 10 publications

References 1 publication

Distributed Agent-Based Traffic Simulations

Distributed Agent-Based Traffic Simulations

Two distribution methods for multiagent traffic simulations

Nested graphs: A model to efficiently distribute multi‐agent systems on HPC clusters

Contact Info

Product

Resources

About