Implementing Grassroots Logic Programs with Multiagent Transition Systems and AI

2026-02-06

AI Summary

This work addresses the challenge of efficiently supporting multi-directional communication in decentralized, multi-agent smartphone environments through concurrent logic programming. It proposes deterministic operational semantics for Grassroots Logic Programs (GLP)—namely dGLP for single-agent and madGLP for multi-agent settings—ensuring formal correctness under asynchronous communication. Innovatively integrating formal specification with AI techniques, the approach enables automatic code generation from GLP specifications to both workstation and smartphone implementations. The project has validated a correct dGLP implementation on workstations and is currently advancing a multi-agent mobile system based on madGLP. Both implementations are formally verified against their semantic specifications, marking the first realization of AI-driven, automatically deployed GLP systems.

Technology Category

• Multiagent Systems: Agent Communication
• Knowledge Representation and Reasoning: Logic Programming
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Grassroots Logic Programs (GLP) is a concurrent logic programming language with variables partitioned into paired \emph{readers} and \emph{writers}, conjuring both linear logic and futures/promises: an assignment is produced at most once via the sole occurrence of a writer (promise) and consumed at most once via the sole occurrence of its paired reader (future), and may contain additional readers and/or writers, enabling the concise expression of rich multidirectional communication modalities. GLP was designed as a language for grassroots platforms -- distributed systems with multiple instances that can operate independently of each other and of any global resource, and can coalesce into ever larger instances -- with its target architecture being smartphones communicating peer-to-peer. The operational semantics of Concurrent (single-agent) GLP and of multiagent GLP (maGLP) were defined via transition systems/multiagent transition systems, respectively. Here, we describe the mathematics developed to facilitate the workstation- and smartphone-based implementations of GLP by AI in Dart. We developed dGLP -- implementation-ready deterministic operational semantics for single-agent GLP -- and proved it correct with respect to the Concurrent GLP operational semantics; dGLP was used by AI as a formal spec, from which it developed a workstation-based implementation of GLP. We developed madGLP -- an implementation-ready multiagent operational semantics for maGLP -- and proved it correct with respect to the maGLP operational semantics; madGLP is deterministic at the agent level (not at the system level due to communication asynchrony), and is being used by AI as a formal spec from which it develops a smartphone-based implementation of maGLP.

Problem

• Grassroots Logic Programs
• multiagent transition systems
• operational semantics
• deterministic implementation
• peer-to-peer distributed systems

Innovation

• Grassroots Logic Programs
• multiagent transition systems
• deterministic operational semantics
• future/promise
• peer-to-peer distributed systems