Develop a robust and modular ETL pipeline leveraging MORK to automate the transformation of structured datasets into symbolic MeTTa expressions. The pipeline will be designed to handle multiple input formats, including CSV, JSON, and RDF, and convert them into logic-compliant MeTTa assertions suitable for symbolic reasoning. It will support dynamic data schema mapping through configurable templates and enable seamless integration with downstream MeTTa-based inference workflows. The system will also include tooling for batch processing, error tracking, and output validation to ensure the reliability and consistency of symbolic outputs. By providing a streamlined path from real-world data to symbolic logic, this pipeline will serve as the foundation for knowledge graph generation, reasoning, and continuous symbolic updates within AGI systems. This milestone will also prepare all artifacts and reusable templates necessary to adapt the pipeline to new domains or datasets with minimal manual intervention, thus accelerating adoption and scalability.

1. MORK Configuration Templates A library of flexible MORK configuration templates will be completed, supporting symbolic mapping for CSV, JSON, and RDF inputs. These templates will enable quick transformation of structured data into MeTTa expressions for various reasoning domains. 2. Python Execution Script A dynamic Python script will be developed to trigger the MORK pipeline with customizable inputs, outputs, and mapping files. This script will support batch processing and parameter-based execution, streamlining the symbolic data ingestion process. 3. Output Validation Mechanism A robust output validation system will be implemented using unit tests to verify the structure and correctness of generated MeTTa expressions. These tests will ensure reliability and consistency across symbolic transformations.

$15,000 USD

Success will be measured by the ability of the pipeline to accurately transform multiple structured data formats including CSV, JSON, and RDF into valid MeTTa expressions that adhere to defined symbolic logic patterns. The transformation process must consistently produce syntactically correct and logically sound MeTTa assertions, capable of being loaded and processed by the MeTTa reasoning engine without errors. Validation will be performed through a series of automated unit tests designed to confirm structural integrity, correct variable substitution, and proper logic mapping. Additionally, sample test sessions will be conducted using representative datasets from at least two different domains to ensure generalizability. The generated symbolic outputs must match expected results and support further inference in downstream tasks. Success will also include the ability to adapt the configuration templates and script parameters to new data schemas with minimal manual intervention, demonstrating the pipeline’s flexibility and readiness for wider adoption in AGI workflows.

Implement a rule-based symbolic reasoning core using the MeTTa language, designed to perform logic-based inference over structured symbolic data. This reasoning engine will include a comprehensive library of reusable logic rules that cover various use cases such as classification, concept linking, and conditional inference. The system will support multi-hop reasoning, allowing it to derive conclusions through multiple layers of logical transformation. It will also be capable of executing symbolic pattern rewrites and rule chaining to simulate intelligent thought processes. The reasoning engine will generate traceable and human-readable logic chains that make the inference process transparent and explainable. These outputs will be tested and logged to ensure reproducibility and consistency. The MeTTa engine will be built to handle inputs dynamically, enabling real-time updates to logic rules and symbolic knowledge graphs. This milestone lays the foundation for creating modular and interpretable AGI systems that rely on logic-driven decision-making instead of opaque neural computations.

1. MeTTa rule library for classification, chaining, and conditionals A curated set of MeTTa rules will be developed to support symbolic classification, multi-step inference chaining, and conditional logic patterns. These rules will be reusable and modular, covering core reasoning functions for diverse domains. 2. Inference chaining examples and demos A series of demonstration scripts will showcase how symbolic facts are processed and expanded through multiple rule applications. These examples will illustrate inference chaining in action, validating the logic engine across practical reasoning scenarios. 3. Symbolic output logs with human-readable chains Symbolic output logs will be generated to capture the full reasoning path taken during inference. Each inference step will be recorded in a clear, human-readable format to support interpretability, auditing, and debugging of logic chains in real-time.

$20,000 USD

Success will be defined by the system’s ability to accurately infer new symbolic relationships from a given set of base triples using predefined MeTTa logic rules. The reasoning engine must consistently apply classification, conditional, and chaining rules to generate valid inferences that extend the original knowledge graph. Each inference must be traceable through a clearly documented logic path, allowing users to review and audit every transformation step. The correctness of inferences will be verified using test-driven validations that compare actual output against expected results across multiple test cases. These tests will include edge cases and multi-hop logic chains to confirm the robustness of the reasoning engine. Additionally, the outputs must remain readable and interpretable to humans, maintaining transparency and explainability. The success of this milestone will also depend on the reusability and modularity of the rule library, ensuring it can be applied across different domains and adapted to new data structures with minimal effort.

Develop a Python-based command-line interface (CLI) and a lightweight FastAPI web interface to provide users with streamlined access to the MORK-to-MeTTa symbolic reasoning pipeline. The CLI will enable local execution by allowing users to specify input datasets, MORK configuration templates, and MeTTa rule files directly from the terminal. It will also offer options for batch processing, output redirection, and automated validation. In parallel, the FastAPI web interface will provide an interactive, browser-accessible environment where users can upload structured data files, define or select reasoning rule sets, and trigger real-time inferences. The API will expose endpoints for data transformation, rule execution, and result retrieval, supporting both JSON responses and human-readable symbolic logs. Both interfaces will be designed with user-friendliness and flexibility in mind, ensuring compatibility with future UI extensions or pipeline integrations. This milestone aims to make symbolic reasoning accessible to technical and non-technical users alike.

1. Python CLI (argument-driven MeTTa runner) A Python command-line interface will be developed to execute the full symbolic reasoning flow. It will accept input arguments for data files, templates, and rule sets, allowing users to run and test MeTTa sessions directly from the terminal with ease. 2. FastAPI endpoints with Swagger/OpenAPI docs A FastAPI-based web interface will expose endpoints for uploading data, triggering transformations, and retrieving results. Integrated Swagger/OpenAPI documentation will guide users in interacting with the API, ensuring usability and clear workflow guidance. 3. JSON outputs for symbolic results The system will produce structured JSON outputs capturing the results of symbolic reasoning sessions. Each response will include the original inputs, inferred relations, and human-readable logic traces, supporting integration, analysis, and transparency.

$15,000 USD

Success will be achieved when users are able to interact with the full symbolic reasoning system end-to-end, either through a command-line interface or a web-based FastAPI interface. Users should be able to upload datasets, select or provide MORK configuration templates, define MeTTa rule files, and trigger the transformation and reasoning pipeline with minimal setup. The system must reliably return rule-based inference results in both human-readable and JSON formats, ensuring output clarity and machine-readability. Each session must be reproducible—meaning that identical inputs and rules consistently yield the same results. Furthermore, the interfaces must support real-time response handling, provide informative feedback in the event of errors, and allow for output logging and retrieval. The success criterion also includes ease of use, with clear documentation for both CLI and API endpoints, and the flexibility to support future UI layers or batch execution. This ensures broad usability, auditability, and smooth integration into AGI workflows.

Deploy two fully functional use cases of the SYMBIOS system to demonstrate its practical value across different domains, one focused on education and the other on scientific reasoning. The educational use case will involve structured curriculum data transformed into symbolic representations to support intelligent tutoring or logic-based feedback. The scientific use case will involve structured facts or taxonomies transformed into symbolic graphs that support hypothesis linking or classification. A complete GitHub repository will be prepared containing all relevant assets, including configuration templates, MeTTa rule sets, test data, reasoning scripts, and automated validation tests. Clear and concise documentation will guide users through setup, configuration, and execution steps. To promote community engagement and accessibility, walkthrough videos or screen captures will be provided to visually demonstrate the pipeline in action from data ingestion to symbolic inference. These materials will ensure reproducibility, community onboarding, and future contributions from developers and researchers.

1. Two use case demonstrations Two working use cases will be implemented,one in education and one in science—to showcase SYMBIOS in real-world scenarios. Each demo will run from data ingestion through to symbolic reasoning, illustrating practical value and system capabilities. 2. Complete GitHub documentation and public repo A public GitHub repository will be created containing all project files, including config templates, MeTTa rules, source code, and test cases. Comprehensive documentation will guide users through setup, usage, customization, and contribution. 3. Onboarding guide and logic writing examples An onboarding guide will be prepared to help new users understand and use the system effectively. It will include step-by-step instructions and annotated examples showing how to write and structure MeTTa rules for various symbolic reasoning tasks.

$15,000 USD

Success will be determined by the successful deployment and execution of two complete SYMBIOS use cases, each demonstrating the system's applicability in distinct domains, one educational and one scientific. These use cases must run end-to-end, starting from raw structured data ingestion via MORK, through rule-based reasoning with MeTTa, and ending in clear, interpretable outputs. The reasoning outcomes must be validated and logically sound. All accompanying assets, configuration files, scripts, rule templates, and test cases will be published in a public GitHub repository. The documentation provided must be peer-reviewed for clarity, completeness, and usability. Positive community engagement, reflected through the project receiving its initial stars, forks, or issues on GitHub, will serve as additional validation. Feedback gathered from public users or early adopters will help refine usability and ensure that the system is accessible to both technical and non-technical users, confirming the milestone's success and readiness for broader adoption.