Develop a specialized natural language generation framework that transforms complex temporal graph structures into human-readable explanations. This framework will include configurable templates for various temporal relationships (intervals, sequences, causality) and adapt explanations based on user expertise level. Implementation will include TMS dependency tracking to explain not just what the system believes but why it believes it.

- Simple library for converting temporal graph structures to natural language explanations - Suite of specialized templates covering at least 5 common temporal relationship types (point-in-time, intervals, sequences, overlaps, causality) - Integration with TMS dependency tracking enabling explanation of belief justifications

$25,000 USD

The framework successfully generates natural language explanations for temporal graphs that non-technical users can understand. Explanations include proper justification structures and can adapt to different technical levels.

Create a bidirectional translation system that converts natural language temporal queries into precise graph queries and translates results back into explanatory language. This system will recognize time references in natural language and classify query intents (point-in-time, interval, sequence, causal). The system will maintain logical consistency through TMS principles.

- Natural language interface for temporal queries with support for at least several types of temporal expressions - Query intent classification system with >90% accuracy for common temporal query types - Bidirectional translation mechanism with comprehensive test suite demonstrating accuracy

$25,000 USD

Users without technical knowledge can successfully retrieve information from temporal knowledge graphs using natural language queries, with the system achieving >85% translation accuracy on our benchmark test set. The system maintains logical consistency when handling contradictory information.

Develop a comprehensive benchmark suite for evaluating temporal graph explanations across multiple dimensions. The benchmark will assess explanation quality, accuracy, and utility across different temporal relationship types and domains. Special attention will be given to evaluating how well explanations convey the system's reasoning process.

- Benchmark datasets covering at least 4 domains (medical, financial, historical, scientific) - Evaluation metrics for explanation fidelity, comprehension, and efficiency - Documentation and tools for running benchmarks and analyzing results

$25,000 USD

The benchmark successfully differentiates between explanation techniques based on meaningful metrics and provides actionable insights for improvement. The benchmark is adopted by at least two other research groups working on temporal reasoning systems.

Develop integration components that connect our temporal explanation system with the MeTTa language and MORK framework. This will enable real-time generation of natural language explanations for temporal inference paths in MeTTa programs. The integration will support truth maintenance capabilities, enhancing interpreter correctness and enabling interactive AI development.

- MeTTa and MORK extension package for accessing temporal explanation capabilities - Example programs demonstrating temporal reasoning explanation in MeTTa

$12,500 USD

Developers using MeTTa can incorporate natural language explanations of temporal reasoning into their applications with minimal code changes.