3-D Interactive Editing of Programmable Graphs

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3-D Interactive Editing of Programmable Graphs

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Overview

It is sort of a Sci-fi trope (e.g. Iron Man) to feature a 3D interface where one manipulates, and in particular connects between, objects floating in a 3D space, as opposed to 2-D one in real world visual programming. The artistic intuition might have some scientific merits. Arbitrary graphs (or just complete graphs) can be embedded in the 3D Euclidean space but not in 2D. So if we visually edit a graph in 3D, we may avoid the nontrivial problem of wire placement, which may be the reason why 2D visual programming don’t scale well with complexity. We will use MORK/MeTTa as the underlying representation, so future growth in 3D graph editing will gravitate towards the MORK/MeTTa ecosystem.

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
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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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Proposal Video

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  • Total Milestones

    4

  • Total Budget

    $80,000 USD

  • Last Updated

    28 May 2025

Milestone 1 - Initial setup

Description

Plumbing through the basic application environment.

Deliverables

A SDL2+Vulkan application that runs on a Linux PC and displays some minimal graphics (e.g. a box) on a Meta Quest 3 preferably wirelessly over ALVR.

Budget

$10,000 USD

Success Criterion

A SDL2+Vulkan application that runs on a Linux PC and displays some minimal graphics on a Meta Quest 3.

Milestone 2 - Generic graph drawing

Description

Implement generic graph drawing and editing (creating/deleting a node adding and displaying textual metadata).

Deliverables

Generic graph drawing and editing capability on top of the previous deliverable backed by PyTorch-geometric.

Budget

$30,000 USD

Success Criterion

Generic graph drawing and editing capability, backed by PyTorch-geometric. Responsiveness and smoothness is crucial. So GPU instancing, level-of-detail, and culling are also to be resolved at this stage. At this stage the underlying representation of graph will just be PyTorch arrays (as in GNN). An issue to be solved here whether we can directly use the PyTorch array in Vulkan, which is a very specific problem to be resolved. It should be possible but at worst we would need to copy and slightly transform the array a few times, which wouldn't be a huge problem. Some provisions to playback steps graph rewriting are also to be made at this stage.

Milestone 3 - Integration with MORK

Description

Integrate the last deliverable with MORK

Deliverables

Graph drawing and editing capability now backed on MORK. As well as handling hypergraphs/metagraphs. Playback of graph rewriting steps (especially incremental adjustments to layouts)

Budget

$20,000 USD

Success Criterion

Graph drawing and editing capability, backed by MORK, or in other words synchronized to MORK. Hypergraphs/metagraphs are transformed to/from regular graphs between MORK and PyTorch-geometric. Some minor changes/extensions may be required on PyTorch-geometric. Performance aspects associated with local updates (when updating a small part of the graph, it should not require copying over the whole graph) will need to be addressed. It may also be possible that the PyTorch representation no longer needs to be the whole graph. This will be investigated further and decided on during actual implementation.

Milestone 4 - Finetuning/optionals

Description

Some final finetuning/optional features

Deliverables

Some changes to the latest deliverable to improve performance if not previously addressed. Integration with finger tracking gloves to improve input responsiveness (note that accuracy largely correlates with responsiveness.) and hence usability - if test results indicate that they are compatible with keyboards. Integration with Apple Vision Pro (or newer hardware available at that time capable of as high/higher resolution) still through ALVR. ~ 5000 is allowed for the cost of hardware. Some videos demonstrating the use.

Budget

$20,000 USD

Success Criterion

As per deliverable description

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