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GLP: A Grassroots, Multiagent, Concurrent, Logic Programm...
[Submitted on 17 Oct 2025 (v1), last revised 2 Jul 2026 (this ve · 2025-10-17 · via cs.DC updates on arXiv.org

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Abstract:A grassroots platform is a multiagent distributed system in which multiple independent instances can form and operate independently of each other and of any global resource, yet may coalesce into ever larger instances, possibly resulting in a single global instance. Grassroots platforms aim to offer an egalitarian/democratic alternative to centralised/autocratic and decentralised/plutocratic global platforms.
Here, we present Grassroots Logic Programs (GLP), a multiagent concurrent logic programming language designed for the implementation of grassroots platforms: we recall the standard operational semantics of logic programs; introduce the concurrent operational semantics of GLP as its restriction; recall multiagent atomic transactions; use them to introduce a multiagent operational semantics of GLP; and prove multiagent GLP to be grassroots. The grassroots social graph -- the foundational grassroots platform on which all others are based -- serves as a GLP programming example.
These mathematical foundations are being used by AI to implement GLP as well as to program in GLP: a workstation-based implementation of concurrent GLP in Dart was derived from the concurrent operational semantics of GLP; a multiagent smartphone-based implementation of GLP in Dart/Flutter is being developed based on the multiagent operational semantics of GLP; a moded type system for GLP was designed (and implemented by AI in Dart) to facilitate collaborative human-AI development of GLP programs, where AI derives working GLP programs from human-approved type definitions and declarations; GLP implementations of grassroots platforms for the social graph, social networks, currencies and bonds, and more, have been derived by AI from mathematical specifications written as volitional multiagent atomic transactions.

Submission history

From: Ehud Shapiro [view email]
[v1] Fri, 17 Oct 2025 15:34:27 UTC (95 KB)
[v2] Sun, 8 Feb 2026 14:07:50 UTC (51 KB)
[v3] Sun, 7 Jun 2026 20:02:43 UTC (156 KB)
[v4] Thu, 2 Jul 2026 20:41:11 UTC (146 KB)