stigLD: Stigmergic Coordination in Linked Systems

René Schubotz, Torsten Spieldenner and Melvin Chelli
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René Schubotz: German Research Center for Artificial Intelligence, Saarland Informatics Campus D3 2, 66123 Saarbrücken, Germany
Torsten Spieldenner: German Research Center for Artificial Intelligence, Saarland Informatics Campus D3 2, 66123 Saarbrücken, Germany
Melvin Chelli: German Research Center for Artificial Intelligence, Saarland Informatics Campus D3 2, 66123 Saarbrücken, Germany

Mathematics, 2022, vol. 10, issue 7, 1-21

Abstract: While current Semantic Web technologies are well-suited for data publication and integration, the design and deployment of dynamic, autonomous and long-lived multi-agent systems (MAS) on the Web is still in its infancy. Following the vision of hypermedia MAS and Linked Systems, we propose to use a value-passing fragment of Milner’s Calculus to formally specify the generic hypermedia-driven behaviour of Linked Data agents and the Web as their embedding environment. We are specifically interested in agent coordination mechanisms based on stigmergic principles. When considering transient marker-based stigmergy, we identify the necessity of generating server-side effects during the handling of safe and idempotent agent-initiated resource requests. This design choice is oftentimes contested with an imprecise interpretation of HTTP semantics, or with rejecting environments as first-class abstractions in MAS. Based on our observations, we present a domain model and a SPARQL function library facilitating the design and implementation of stigmergic coordination between Linked Data agents on the Web. We demonstrate the efficacy our of modelling approach in a Make-to-Order fulfilment scenario involving transient stigmergy and negative feedback as well as by solving a problem instance from the (time constrained) Trucks World domain as presented in the fifth International Planning Competition.

Keywords: Linked Data; Semantic Web; multi-agent systems; stigmergy; nature inspired algorithm; RDF; SPARQL; biologically inspired computing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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