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.

翻译：基层逻辑程序（GLP）是一种并发逻辑编程语言，其变量被划分为成对的**读取器**与**写入器**，同时融合了线性逻辑与期货/承诺的概念：赋值通过唯一的写入器（承诺）至多产生一次，并通过其配对的读取器（期货）至多消费一次，且可包含额外的读取器和/或写入器，从而能够简洁地表达丰富的多向通信模式。GLP被设计为用于基层平台的语言——这类分布式系统包含多个可独立运行且不依赖任何全局资源的实例，并能合并形成更大的实例——其目标架构是进行点对点通信的智能手机。并发（单智能体）GLP与多智能体GLP（maGLP）的操作语义已分别通过转移系统/多智能体转移系统定义。本文阐述了为支持人工智能在Dart中实现基于工作站和智能手机的GLP所发展的数学基础。我们开发了dGLP——面向单智能体GLP的、可直接用于实现的确定性操作语义——并证明了其相对于并发GLP操作语义的正确性；dGLP被人工智能用作形式化规约，并基于此开发了工作站的GLP实现。我们开发了madGLP——面向maGLP的、可直接用于实现的多智能体操作语义——并证明了其相对于maGLP操作语义的正确性；madGLP在智能体层面是确定性的（在系统层面因通信异步性而非确定性），目前正被人工智能用作形式化规约，以开发基于智能手机的maGLP实现。