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Agent Programmability Enhancement for Rambling over a Scientific Dataset

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Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection (PAAMS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12092))

Abstract

Agent-based modeling (ABM), while originally intended for micro-simulation of individual entities, (i.e., agents), has been adopted to operations research as biologically-inspired algorithms including ant colonial optimization and grasshopper optimization algorithm. Observing their successful use in traveling salesman problem and K-means clustering, we promote this trend in ABM to distributed data analysis. Our approach is to populate reactive agents on a distributed, structured dataset and to have them discover the dataset’s attributes (e.g., the shortest routes and the best cluster centroids) through agent migration and interaction. We implemented this agent-based approach with the multi-agent spatial simulation (MASS) library and identified programming features for agents to best achieve data discovery. Of importance is ease of describing when and how to have agents traverse a graph, ramble over an array, and share the on-going computational states. We have responded to this question with two agent-descriptivity enhancements: (1) event-driven agent behavioral execution and (2) direct inter-agent broadcast. The former automatically schedules agent actions before and after agent migration, whereas the latter informs all agents of up-to-date global information, (e.g., the best slate of centroids so far). This paper presents our design, implementation, and evaluation of these two agent descriptivity enhancements.

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Notes

  1. 1.

    http://hadoop.apache.org, http://spark.apache.org, http://storm.apache.org.

  2. 2.

    https://www.unidata.ucar.edu/software/netcdf/.

  3. 3.

    http://spark.apache.org/graphx.

  4. 4.

    http://tez.apache.org.

  5. 5.

    https://github.com/Unidata/netcdf4-python.

  6. 6.

    https://support.hdfgroup.org/HDF5/.

  7. 7.

    https://hazelcast.com/.

References

  1. Blum, C.: Ant colony optimization: introduction and recent trends. Phys. Life Rev. 2(4), 353–373 (2005)

    Article  Google Scholar 

  2. Buck, J., et al.: SciHadoop: array-based Query Processing in Hadoop. In: Proceedings of SC 2011 (2011). https://doi.org/10.1145/2063384.2063473

  3. FLAME. http://www.flame.ac.uk

  4. Fukuda, M., Gordon, C., Mert, U., Sell, M.: Agent-based computational framework for distributed analysis. IEEE Comput. 53(3), 16–25 (2020). https://doi.org/10.1109/MC.2019.2932964

    Article  Google Scholar 

  5. Gordon, C., Mert, U., Sell, M., Fukuda, M.: Implementation techniques to parallelize agent-based graph analysis. In: De La Prieta, F., et al. (eds.) PAAMS 2019. CCIS, vol. 1047, pp. 3–14. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24299-2_1

    Chapter  Google Scholar 

  6. Kennedy, J., et al.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks IV, pp. 1942–1948 (1995)

    Google Scholar 

  7. Kipps, M., et al.: Agent and spatial based parallelization of biological network motif search. In: 17th IEEE International Conference on HPCC, New York, pp. 786–791 (2015)

    Google Scholar 

  8. Lin, J., et al.: Data-Intensive Text Processing with MapReduce. Morgan & Claypool Publishers, San Rafael (2010)

    Book  Google Scholar 

  9. Low, Y., et al.: Distributed GraphLab: a framework for machine learning and data mining in the cloud. In: Proceedings of the 38th International Conference on Very Large Data Bases, Istanbul, Turkey, vol. 5(8), pp. 716–727, August 2012

    Google Scholar 

  10. Oracle: Java Platform, Standard Edition, Java Shell User’s Guide, Release 9. Technical report E87478–01 (2017)

    Google Scholar 

  11. Parsian, M.: Data Algorithms: Recipes for Scaling Up with Hadoop and Spark. O’Reilly, Sebastopol (2015)

    Google Scholar 

  12. RepastHPC. https://repast.github.io/repast_hpc.html

  13. Saremi, S., Mirjalili, S., Lewis, A.: Grasshopper optimization algorithm: theory and application. Adv. Eng. Softw. 105, 30–47 (2017)

    Article  Google Scholar 

  14. Shih, Y., et al.: Translation of string-and-pin-based shortest path search into data-scalable agent-based computational models. In: Proceedings of Winter Simulation Conference, Gothenburg, Sweden, pp. 881–892, December 2018

    Google Scholar 

  15. Woodring, J., et al.: A multi-agent parallel approach to analyzing large climate data sets. In: 37th IEEE ICDCS, Atlanta, GA, pp. 1639–1648, June 2017

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computing and Software Systems, University of Washington Bothell, Bothell, WA, 98011, USA

    Matthew Sell & Munehiro Fukuda

Authors
  1. Matthew Sell
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  2. Munehiro Fukuda
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Corresponding author

Correspondence to Munehiro Fukuda .

Editor information

Editors and Affiliations

  1. Centre National de la Recherche Scientifique, Grenoble, France

    Yves Demazeau

  2. Catholic University of Leuven, Heverlee, Belgium

    Tom Holvoet

  3. University of Salamanca, Salamanca, Spain

    Juan M. Corchado

  4. University of L’Aquila, L'Aquila, Italy

    Stefania Costantini

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Sell, M., Fukuda, M. (2020). Agent Programmability Enhancement for Rambling over a Scientific Dataset. In: Demazeau, Y., Holvoet, T., Corchado, J., Costantini, S. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection. PAAMS 2020. Lecture Notes in Computer Science(), vol 12092. Springer, Cham. https://doi.org/10.1007/978-3-030-49778-1_20

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  • DOI: https://doi.org/10.1007/978-3-030-49778-1_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49777-4

  • Online ISBN: 978-3-030-49778-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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