Anna Mikeda

Project Owner

HERMES

Expert Rating

n/a

Type SingularityNET RFP
Funding Request n/a
RFP Guidelines Advanced knowledge graph tooling for AGI systems

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Overview

HERMES (Hypergraph Experiential Reasoning & Motivational Engagement System) is a symbolic graph construction tool that transforms AGI agent experience into causally structured knowledge graphs. By integrating experiential learning from AIRIS and goal-driven modulation from MAGUS, HERMES constructs hypergraph substructures linking actions, environmental changes, and goal satisfaction. These symbolic MeTTa-native graphs support reasoning, planning, and learning within the OpenCog Hyperon framework. HERMES constructs reusable, annotated causal representations that evolve alongside agent behavior, enabling AGI systems to refine their decision-making through grounded symbolic reflection.

Project Tags:
Community and Collaboration

RFP Guidelines

Advanced knowledge graph tooling for AGI systems

Internal Proposal Review

Type SingularityNET RFP
Total RFP Funding $350,000 USD
Proposals 40
Awarded Projects n/a

SingularityNET

Apr. 16, 2025

This RFP seeks the development of advanced tools and techniques for interfacing with, refining, and evaluating knowledge graphs that support reasoning in AGI systems. Projects may target any part of the graph lifecycle — from extraction to refinement to benchmarking — and should optionally support symbolic reasoning within the OpenCog Hyperon framework, including compatibility with the MeTTa language and MORK knowledge graph. Bids are expected to range from $10,000 - $200,000.

Proposal Description

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Total Milestones
3
Total Budget
$180,000_USD
Last Updated
27 May 2025

Milestone 1 - Foundation and Prototyping

Description

Establish core architecture and demonstrate basic causal graph extraction.

Deliverables

Comprehensive HERMES architecture design document Definition of integration points with AIRIS MAGUS and Atomspace Initial causal extractor prototype demonstrating action → effect → goal links Preliminary MeTTa output format specification Sample trace and graph outputs for a simple agent interaction scenario Detailed evaluation plan and benchmarking criteria

Budget

$36,000 USD

Success Criterion

Architecture document reviewed and approved by the team Prototype successfully extracts causal links from simple agent behaviors MeTTa expressions correctly represent basic action-effect relationships Benchmarks established for all four evaluation dimensions

Milestone 2 - Graph Toolchain and Integration

Description

Complete construction and export pipeline integrate with target systems.

Deliverables

Full implementation of causal graph constructor module Goal satisfaction annotation engine with multi-level confidence metrics Export system generating annotated Atomspace graphs and MeTTa expressions MORK-compatible serialization for persistence and retrieval Bi-directional connectors: AIRIS logs → HERMES processor → Atomspace Atomspace → HERMES translator → MAGUS goal evaluators Midpoint integration test demonstrating symbolic graph influence on MAGUS decisions Technical documentation covering APIs CLI and graph schemas

Budget

$72,000 USD

Success Criterion

Constructor generates complete causal graphs from complex agent behavior Confidence metrics accurately reflect certainty of causal relationships Bidirectional data flow demonstrated between AIRIS, HERMES, and MAGUS Persistence system retrieves graphs with >80% fidelity At least one test case shows MAGUS using HERMES data for improved decisions

Milestone 3 - Evaluation Optimization and Release

Description

Validate reasoning utility optimize performance and release to community.

Deliverables

Comprehensive evaluation suite measuring all success metrics Optimization of core algorithms based on performance testing Public release of: Open-source HERMES module with documentation Sample data and MeTTa scripts for common use cases Jupyter notebooks demonstrating integration approaches Use-case demonstration in a simulated environment (Neoterics virtual world) Submission to AGI-aligned open-source repositories and community forums

Budget

$72,000 USD

Success Criterion

Algorithm optimizations yield >25% improvement in processing speed Documentation passes external review for clarity and completeness Neoterics demo shows fully functional integration in realistic scenarios Code passes quality checks and is accepted by community repositories

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The weighted average of the 4 perspectives Overall

0.0
Each RFP defines a maximum allowed budget, but teams can differentiate their proposal by offering a solution with a lower budget or a wider scope.Value for money

0.0
This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.Compliance with RFP requirements

0.0
RFPs will offer varying degrees of freedom. This rating indicates the quality of the team's specific solution ideas, the provided details, and the reviewer's confidence in the team's ability to execute.Solution details and team expertise

0.0

Review Headline

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Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses. Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively). Feasibility (secondary) This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding. Viability (secondary) This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?” Examples:

A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
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