🤖 AI Summary
This work addresses the limitations of centralized or oligarchic control architectures by proposing a decentralized, egalitarian approach to multi-agent system design. The core contribution is the design and implementation of GLP, a grassroots autonomous concurrent logic programming language that formally integrates the principle of grassroots autonomy into logic programming for the first time. GLP introduces a novel concurrent operational semantics for multi-agent systems that supports dynamic aggregation without global dependencies and is proven to satisfy key properties of grassroots systems. Building upon the standard semantics of logic programs, the framework incorporates concurrency and atomic multi-agent transactions to enable a scalable distributed AI architecture. The feasibility and correctness of GLP in realizing truly self-organizing, distributed AI platforms are validated through a grassroots social graph case study.
📝 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 graphâthe foundational grassroots platform on which all others are basedâserves as a GLP programming example.