DriftScript: A Domain-Specific Language for Programming Non-Axiomatic Reasoning Agents

TL;DR

DriftScript is a Lisp-like domain-specific language designed to improve usability for programming Non-Axiomatic Reasoning Agents, compiling to Narsese and enabling easier development of adaptive autonomous systems.

Contribution

It introduces a new readable, keyword-based syntax for NARS programming, with a compiler architecture and formal grammar, enhancing accessibility and integration capabilities.

Findings

Successfully compiles to Narsese with high fidelity

Improves code readability and maintainability

Achieves efficient compilation with a four-stage pipeline

Abstract

Non-Axiomatic Reasoning Systems (NARS) provide a framework for building adaptive agents that operate under insufficient knowledge and resources. However, the standard input language, Narsese, poses a usability barrier: its dense symbolic notation, overloaded punctuation, and implicit conventions make programs difficult to read, write, and maintain. We present DriftScript, a Lisp-like domain-specific language that compiles to Narsese. DriftScript provides source-level constructs covering the major sentence and term forms used in Non-Axiomatic Logic (NAL) levels 1 through 8, including inheritance, temporal implication, variable quantification, sequential conjunction, and operation invocation, while replacing symbolic syntax with readable keyword-based S-expressions. The compiler is a zero-dependency, four-stage pipeline implemented in 1,941 lines of C99. When used with the DriftNARS engine, DriftScript programs connect to external systems through four structured callback types and an HTTP operation registry, enabling a sense-reason-act loop for autonomous agents. We describe the language design and formal grammar, detail the compiler architecture, and evaluate the compiler through a 106-case test suite, equivalence testing against hand-written Narsese, a NAL coverage analysis, structural readability metrics, and compilation benchmarks. The source code is available at https://github.com/seamus-brady/DriftNARS. This paper focuses on the design and implementation of the DriftScript language and its embedding into DriftNARS, rather than on new inference algorithms for NARS itself.