Sanskrit Lakāras in AI: A Temporal Logic Framework

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Future of Technology Desk

Abstract

This paper presents an advanced study into the computational potential of Sanskrit verb tenses (Lakāras) as temporal operators in artificial intelligence. Building on Rick Briggs’ foundational 1985 NASA-backed research, we critically reassess the claim that Sanskrit’s linguistic structure is particularly well-suited to AI representation. By analyzing Lakāras as temporal-logical primitives, we propose a novel mapping of Sanskrit tenses to discrete computational states in AI, particularly in planning, process control, and procedural programming. This paper introduces a theoretical construct: Tense-State Computational Grammar (TSCG), which integrates Paninian linguistics with modern state machine logic and offers a pathway toward constructing interpretable AI with embedded tense-awareness.

1. Introduction

Linguistic precision is a cornerstone of symbolic artificial intelligence. Formal logic languages like LISP and Prolog succeed because of their rule-based structure and minimal ambiguity. However, modern AI now demands languages that can capture nuances of intention, temporality, and conditionality. Sanskrit—an ancient but meticulously structured language—offers just such a framework.

In 1985, Rick Briggs published “Knowledge Representation in Sanskrit and Artificial Intelligence” in the AI Magazine. He argued that Sanskrit’s syntax could represent knowledge without distortion or ambiguity. This paper aims to expand on his thesis, introducing a modern computational model grounded in Sanskrit's verb tense system.

2. Theoretical Framework: Temporal Logic Meets Linguistics

2.1. Temporal Logic in AI

Temporal logic underpins reasoning over sequences of events or actions. In AI, it enables:

• Situation Calculus

• Linear Temporal Logic (LTL)

• Computation Tree Logic (CTL)

• Event Calculus

However, few natural languages encode such temporal distinctions as precisely as Sanskrit.

2.2. Sanskrit’s Lakāras: A Tense-Modality-Aspect System

Sanskrit divides verbal action into nine distinct Lakāras, far more nuanced than the tripartite past-present-future system in most languages.

This paper is AI edited

3. Review of Rick Briggs’ Work (1985)

Rick Briggs’ paper asserted that Sanskrit’s formal grammar could support unambiguous knowledge representation. He highlighted:

• Precision of Syntax: Based on Panini’s Ashtadhyayi.

• Elimination of Ambiguity: Due to fixed word-function relationships.

• Semantic Embedding: Through inflectional morphology.

Limitations of Briggs’ Work:

• He focused on propositional representation, not process modeling.

• Temporal structure was underdeveloped.

• No computational implementation was proposed.

This paper aims to address these gaps by formalizing Sanskrit's tense system as a computational temporal model.

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4. Lakāras as Computational State Operators

We now introduce the concept of Tense-State Computational Grammar (TSCG)—a system in which each Lakāra maps to a computational state or temporal operator.

4.1. Finite State Diagram

Each verb form transitions between states:

5. Implementation Blueprint: TLPL – Tense Logic Programming Language

Key Features:

• Tense-modality mappings

• Agent-based control

• Natural language inspired syntax

6. Use Cases in AI

6.1. Temporal Planning Agents

Agents with intentions (Leṭ), plans (Luṭ), and actions (Laṭ), align with BDI architectures.

6.2. Explainable AI

The transparency of tensed representations supports human-interpretable decision trails.

6.3. Narrative Intelligence

Lakāras can order events in generated stories with precision (Liṭ → Laṭ → Lṛṭ).

6.4. Robotics

Commands (Loṭ) and permissions (Vidhi-Liṅ) enable linguistically-grounded robotic control systems.

7. Challenges and Research Directions

• Corpus Development: Lack of fully annotated Lakāra-tagged Sanskrit corpora.

• Parser Design: Need for tense-aware syntactic parsers.

• Integration: Bridging between TLPL and conventional logic programming.

• Computational Metrics: Formalizing complexity and efficiency.

8. Conclusion

Sanskrit’s Lakāra system presents a deeply structured, computationally viable approach to modeling time, intention, and modality. While Rick Briggs hinted at Sanskrit's potential for knowledge representation, this paper extends his insights into actionable temporal logic systems. We propose that Sanskrit is not just a language of the past—it is a linguistic prototype for the future of AI.

References

• Briggs, Rick. “Knowledge Representation in Sanskrit and Artificial Intelligence.” AI Magazine, Spring 1985.

• Panini. Ashtadhyayi (4th Century BCE).

• Allen, James. “Maintaining Knowledge About Temporal Intervals.” Communications of the ACM, 1983.

• Kamp, Hans & Reyle, Uwe. From Discourse to Logic. Kluwer, 1993.

• Shoham, Yoav. “Temporal Logics in AI: Semantics and Ontology.” AI Journal, 1987.

• Goyal, Pawan, et al. “A Treebank of Sanskrit based on Paninian Grammar.” COLING, 2012.