Software agents mobility using process migration mechanism in distributed Erlang

Piotrowski, Michał; Turek, Wojciech

doi:10.1145/2505305.2505312

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…Transitions between states are triggered by changes in the knowledge-base facts or by external messages. The original version of eXAT does not support agent migrations, however there is a version supporting this functionality [25].…”

Section: Large-scale Agent-based Systemsmentioning

confidence: 99%

Massively concurrent agent-based evolutionary computing

Krzywicki¹,

Turek²,

Byrski³

et al. 2015

Journal of Computational Science

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The fusion of the multi-agent paradigm with evolutionary computation yielded promising results in many optimization problems. Evolutionary multi-agent system (EMAS) are more similar to biological evolution than classical evolutionary algorithms. However, technological limitations prevented the use of fully asynchronous agents in previous EMAS implementations. In this paper we present a new algorithm for agent-based evolutionary computations. The individuals are represented as fully autonomous and asynchronous agents. An efficient implementation of this algorithm was possible through the use of modern technologies based on functional languages (namely Erlang and Scala), which natively support lightweight processes and asynchronous communication. Our experiments show that such an asynchronous approach is both faster and more efficient in solving common optimization problems.

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Section: Large-scale Agent-based Systemsmentioning

confidence: 99%

Massively concurrent agent-based evolutionary computing

Krzywicki¹,

Turek²,

Byrski³

et al. 2015

Journal of Computational Science

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“…Functional multi‐agent systems address the issue mentioned above (ie, an inefficient use of multi‐core CPUs in the shared‐memory concurrency model). The most mature example of such a system (platform) is eXAT ( erlang eXperimental Agent Tool ) (see the works of Di Stefano and Santoro and Piotrowski and Turek). eXAT overcomes the basic limitations of popular Java‐based solutions as its agents are based on Erlang lightweight processes, and millions of such processes can be created on a single computer.…”

Section: Related Workmentioning

confidence: 99%

Concurrent agent‐based evolutionary computations as adaptive dataflows

Krzywicki

Faber

Debski

2018

Concurrency and Computation

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Summary This paper introduces a new formal description of the execution model for agent‐based computing systems in the form of an adaptive dataflow decoupled from the domain‐specific semantics of the computation. We show that the execution models studied in previous work can be unified in this common model. The parameters of the model such as queuing policies and granularity of the data in the flow are analyzed. Several queueing alternatives are benchmarked to demonstrate how they affect the efficiency of the computation. Using the example of a multi‐agent evolutionary optimisation problem solver, the new approach is shown to outperform the classic one. This proposed model is well suited to functional languages and can be easily mapped onto different classes of hardware – from simple single‐core computers to distributed environments.

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