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Linked Data as Medium for Stigmergy-based Optimization and Coordination

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Software Technologies (ICSOFT 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1622))

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Abstract

Optimization through coordination of processes in complex systems is a classic challenge in AI research. A specific class of algorithms takes for this inspiration from biology. Such bio-inspired algorithms achieve coordination and optimization by transferring, for example, concepts of communication in insect swarms to typical planner problems in the AI domain. Among those bio-inspired algorithms, an often used concept is the concept of stigmergy. In a stigmergic system, actions carried out by members of the swarm (or, in AI domains, by single agents), leave traces in the environment that subsequently work as incentive for following agents. While there is a noticable uptake of stigmergy as coordination mechanism in AI, we see the common understanding of one core element of stigmergic systems still lacking: The notion of the shared digital stigmergic medium, in which agents carry out their actions, and in which traces left by these actions manifest. Given that the medium is in literature considered the element “that underlies the true power of stigmergy”, we believe that a well-defined, properly modelled, and technically sound digital medium is essential for correct, understandable, and transferable stigmergic algorithms. We therefore suggest the use of read-write Linked Data as underlying medium for decentralized stigmergic systems. We first derive a set of core requirements that we see crucial for stigmergic digital media from relevant literature. We then discuss read-write Linked Data as suitable choice by showing that it fulfills given the requirements. We conclude with two practical application examples from the domains of optimization and coordination respectively.

This work has been supported by the German Federal Ministry for Education and Research (BMBF) as part of the MOSAIK project (grant no. 01IS18070-C).

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Notes

  1. 1.

    RDF 1.1 Primer document (Jan. 2021): https://www.w3.org/TR/rdf11-primer/.

  2. 2.

    https://www.w3.org/standards/semanticweb/data.

  3. 3.

    W3C SPARQL 1.1 Query Language Recommendation (Apr. 2021): https://www.w3.org/TR/sparql11-query/.

  4. 4.

    SPARQL Federated Queries: (Apr.2021): https://www.w3.org/TR/sparql11-federated-query/.

  5. 5.

    https://www.w3.org/TR/rdf-sparql-query/#rdfDataset.

  6. 6.

    http://www.dcs.st-and.ac.uk/Ëœipg/challenge/.

  7. 7.

    https://schema.org.

  8. 8.

    https://schema.org/.

  9. 9.

    https://www.w3.org/2019/wot/td.

  10. 10.

    https://github.com/aantakli/AJAN-service.

  11. 11.

    https://github.com/aantakli/AJAN-editor.

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Authors and Affiliations

  1. German Research Center for Artificial Intelligence (DFKI), Saarland Informatics Campus D3 2, 66123, Saarbrücken, Germany

    Torsten Spieldenner & Melvin Chelli

  2. Saarbrücken Graduate School of Computer Science, Campus E1 3, 66123, Saarbrücken, Germany

    Torsten Spieldenner

Authors
  1. Torsten Spieldenner
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  2. Melvin Chelli
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Correspondence to Torsten Spieldenner .

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Editors and Affiliations

  1. Digitalization and Information Systems Group, Universität Fribourg, Fribourg, Switzerland

    Hans-Georg Fill

  2. Department of Computer Science, Information Systems Group, Enschede, The Netherlands

    Marten van Sinderen

  3. Institute of Business Informatics, Wrocław University of Economics, Wrocław, Poland

    Leszek A. Maciaszek

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Spieldenner, T., Chelli, M. (2022). Linked Data as Medium for Stigmergy-based Optimization and Coordination. In: Fill, HG., van Sinderen, M., Maciaszek, L.A. (eds) Software Technologies. ICSOFT 2021. Communications in Computer and Information Science, vol 1622. Springer, Cham. https://doi.org/10.1007/978-3-031-11513-4_1

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  • DOI: https://doi.org/10.1007/978-3-031-11513-4_1

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