This initial phase focuses on comprehensive research and detailed planning. We will conduct a thorough survey and evaluation of various neuro-symbolic DNN architectures including but not limited to PyNeuraLogic and KANs based on their potential for embedding logic rules and enhancing reasoning capabilities within interactive AI agents. Leveraging our team's experience building video-based avatar chat systems with tools like Vertex AI and DialogFlow CX we will define the specific problem domain or use case within this context where these architectures will be applied focusing on improving aspects like multi-step reasoning or learning from limited data observed in our previous work. We will design the overall architectural framework for integrating the chosen neuro-symbolic DNNs with the Hyperon Atom Space and Meta language for symbolic manipulation and reasoning considering how to bridge data-driven and symbolic paradigms effectively. The plan will detail the methodology for development evaluation and ensuring reproducibility.

Detailed Research Plan: Outlining the chosen neuro-symbolic architectures for investigation the specific use case/problem domain within interactive agents the integration strategy with Hyperon/Meta/LLMs a detailed methodology for development and evaluation (including criteria for interpretability small data learning structured learning and reasoning performance) and an agile breakdown of tasks with timeline. Framework Design Document: Specifying the proposed high-level architecture for neuro-symbolic DNN integration within the Hyperon ecosystem including how logic rules derived from our application domain or user-defined rules will be embedded and how interactions with key Hyperon components (Atom Space Meta) and potentially external DNNs (like LLMs) will be managed. Initial Literature Survey Summary: A brief report summarizing the findings from the initial research into relevant neuro-symbolic DNN architectures and their potential applications.

$20,000 USD

The research plan is comprehensive, technically sound, and clearly addresses the RFP objectives and evaluation criteria for the chosen use case. The framework design is well-defined and technically feasible, illustrating a clear path for integration with key Hyperon components and selected neuro-symbolic DNNs. The task breakdown and timeline are realistic and provide a solid foundation for the subsequent implementation phase. All initial deliverables (Detailed Research Plan, Framework Design Document, Literature Survey Summary) are completed and reviewed to satisfaction.

This milestone involves the initial development and implementation of the core integration framework designed in Milestone 1. We will focus on building a functional prototype demonstrating how logic rules can be embedded within a selected neuro-symbolic DNN architecture (e.g. using libraries like PyNeuraLogic) and integrated with the Hyperon Atom Space and Meta language for synergistic symbolic manipulation and reasoning. The development will target a concrete albeit simplified instance of the chosen interactive agent use case (e.g. implementing a specific logical reasoning step within a dialogue flow or a simple planning task for a virtual avatar). We will conduct preliminary testing of the integrated components to verify basic functionality demonstrate the process of rule embedding and gather initial results against the defined evaluation metrics. This phase will also involve starting the documentation process focusing on the implemented core components.

Draft Implementation Codebase: A functional prototype codebase demonstrating the core integration of at least one neuro-symbolic DNN architecture with Hyperon/Meta and the capability to embed and potentially utilize defined logic rules within the DNN structure. This code should align with the framework design from Milestone 1. Initial Testing Results and Analysis: A report summarizing the results from preliminary tests on a basic test case related to the chosen use case providing early insights into the framework's operation and potential benefits regarding performance interpretability learning from small data or structured learning. Draft Technical Documentation: Initial documentation covering the setup of the implemented framework its key software components the process for embedding logic rules and instructions on how to run the preliminary tests.

$40,000 USD

The implemented prototype successfully demonstrates the technical feasibility of integrating neuro-symbolic DNNs with Hyperon/Meta and embedding logic rules as designed in Milestone 1, providing a concrete example of the neural-symbolic bridge. The core components are functional and exhibit basic interaction as expected. Preliminary testing is completed, and the results provide meaningful initial insights into the system's capabilities, even if not fully optimized or comprehensive. The codebase is structured logically, and the draft documentation is started and accurately reflects the current state of the implementation, demonstrating a commitment to reproducibility. The project is on track to proceed to full implementation and comprehensive evaluation.

The final milestone focuses on completing the implementation conducting comprehensive evaluation and finalizing all deliverables. We will refine the integrated framework based on insights gained from Milestone 2 potentially extending it to handle more complex rules or larger datasets relevant to the interactive agent use case. Extensive experiments will be performed using the chosen dataset(s) and scenarios rigorously evaluating the system against all defined success criteria. The primary focus will be on demonstrating and analyzing results related to improved reasoning accuracy interpretability learning from small data and/or structured learning within the context of the chosen AI task for interactive agents. We will ensure the work is fully reproducible and provide thorough documentation. Recommendations for future research directions will also be provided.

Final Implementation Codebase: The complete refined and thoroughly tested code for the neuro-symbolic integration framework within Hyperon/Meta structured for clarity maintainability and reproducibility. Comprehensive Evaluation Report: A detailed report presenting the experimental setup methodology dataset(s) used complete results and in-depth analysis of the system's performance across all evaluation criteria (reasoning interpretability small data learning structured learning scalability potential). This report will clearly articulate the findings and their implications. Complete Documentation Suite: Thorough and clear documentation covering the system architecture installation instructions usage examples the process for embedding logic rules detailed experimental procedures (including system configuration) and insights for future research or extension. Framework Demonstration: A presentation or video showcasing the implemented system and highlighting its key capabilities and demonstrated improvements based on the evaluation results.

$40,000 USD

All technical implementation goals are achieved, resulting in a functional and integrated neuro-symbolic system within Hyperon. The comprehensive evaluation is completed successfully, and the results are clearly presented and analyzed in the final report, providing strong evidence of how the chosen neuro-symbolic approach addresses the RFP objectives for the specific use case. The final codebase is complete, well-documented, and provided in a usable format, fully supporting reproducibility. The documentation is complete, clear, and sufficient to allow other researchers to understand, replicate, and potentially extend the work. All final deliverables are completed to a high standard, fulfilling the project commitments outlined in the proposal.