A Mimamsa Inspired Framework For Instruction Sequencing In AI Agents

TL;DR

This paper introduces a formal framework inspired by Mimamsa philosophy for sequencing instructions in AI agents, formalizing mechanisms for temporal, functional, and iterative dependencies to improve task planning and robotics.

Contribution

It extends the MIRA formalism with new sequencing mechanisms and provides formal verification of instruction correctness, enhancing AI task planning reliability.

Findings

Formalizes instruction sequencing with three mechanisms: Srutikrama, Arthakrama, Pravrittikrama.

Extends MIRA formalism with explicit deduction rules for sequencing.

Proves soundness and completeness of the sequencing framework.

Abstract

This paper presents a formal framework for sequencing instructions in AI agents, inspired by the Indian philosophical system of Mimamsa. The framework formalizes sequencing mechanisms through action object pairs in three distinct ways: direct assertion (Srutikrama) for temporal precedence, purpose driven sequencing (Arthakrama) for functional dependencies, and iterative procedures (Pravrittikrama) for distinguishing between parallel and sequential execution in repetitive tasks. It introduces the syntax and semantics of an action object imperative logic, extending the MIRA formalism (Srinivasan and Parthasarathi, 2021) with explicit deduction rules for sequencing. The correctness of instruction sequencing is established through a validated theorem, which is based on object dependencies across successive instructions. This is further supported by proofs of soundness and completeness. This formal verification enables reliable instruction sequencing, impacting AI applications across areas like task planning and robotics by addressing temporal reasoning and dependency modeling.