Dwane van der Sluis

Project Owner

NACE inspired attention evaluation frame work

Type SingularityNET RFP
Funding Awarded $11,250 USD
RFP Guidelines Framework for evaluating approaches to attention allocation

Status

Overall Status
⏳ Contract Pending
Funding Transfered
$0_USD
Max Funding Amount
$11,250_USD

Funding Schedule

View Milestones

Milestone Release 1	$1,250 USD	Pending	TBD
Milestone Release 2	$2,500 USD	Pending	TBD
Milestone Release 3	$2,500 USD	Pending	TBD
Milestone Release 4	$2,500 USD	Pending	TBD
Milestone Release 5	$2,500 USD	Pending	TBD

Overview

To build a software package that is pip installable, that exposes the planning step of gymnasium grid world problems, in a way that allows experimentation and bench marking.

RFP Guidelines

Framework for evaluating approaches to attention allocation

Complete & Awarded

Type SingularityNET RFP
Total RFP Funding $60,000 USD
Proposals 5
Awarded Projects 2

SingularityNET

Oct. 9, 2024

SingularityNET

rfp owner

Framework for evaluating approaches to attention allocation

Complete & Awarded

The goal of this project is to develop a framework to evaluate various approaches to Attention Allocation (AA) within the OpenCog Hyperon and PRIMUS architectures. The AA system dynamically allocates cognitive resources to Atoms in the Distributed Atomspace (DAS), and this framework will help assess AA methods based on desired cognitive dynamics. The framework will improve both Probabilistic Logic Networks (PLN) and evolutionary methods like Meta-Optimizing Semantic Evolutionary Search (MOSES), which are critical components of the PRIMUS architecture.

View Full RFP

Proposal Description

Open Source Licensing

GNU GPL - GNU General Public License

Links and references

https://github.com/patham9/NACE

Activity Summary

Milestones

5

Total

Discussion

0

Total Comments

Reviews

7

Total Posted

Project Team

1

Total People

Group Expert Rating (Final)

Overall

5.0

Compliance with RFP requirements 3.3
Solution details and team expertise 3.8
Value for money 3.7

While not addressing the core research posed by the RFP this proposal was deamed to be relevant and of good value.

New reviews and ratings are disabled for Awarded Projects

Overall Community

3.3

from 7 reviews

5

2
4

1
3

1
2

1
1

1

Feasibility

3.3

from 7 reviews

Viability

3.8

from 7 reviews

Desirabilty

3.7

from 7 reviews

Usefulness

0

from 7 reviews

Sort by

7 ratings

Expert Review 1
Overall

1.0
- Compliance with RFP requirements 1.0
- Solution details and team expertise 1.0
- Value for money 0.0
Very narrow proposal focused on a single algorithm and its single domain!

Strong reject! Dwane wants to analyze our own NACE algorithm and improve planning by trying different types of AA applied. While the RFP asks for a general framework for AA evaluation for whole PRIMUS, such narrow solution is not applicable for it. Also I want to note the lack of team’s prior experience in the AA field.

Expert Review 2
Overall

2.0
- Compliance with RFP requirements 1.0
- Solution details and team expertise 3.0
- Value for money 0.0
Attention in planning in NACE

Adds attention allocation in NACE planning process for better compute usage. This is useful but misses the wider picture of Attention Allocation in Hyperon and PRIMUS. It could potentially extend to PRIMUS and since it is technologically tractable while details are lacking in the proposal, 2 stars are given as it at least includes a way to measure performance.

Expert Review 3
Overall

4.0
- Compliance with RFP requirements 4.0
- Solution details and team expertise 5.0
- Value for money 0.0
This is a modest proposal but well fleshed out and worthwhile

Basically he suggests to incorporate attention into NACE and evaluate attention mechanisms that way. Why not. Patrick etc. would probably do this anyway but they could use help and the cost here is low.

Expert Review 4
Overall

3.0
- Compliance with RFP requirements 5.0
- Solution details and team expertise 3.0
- Value for money 0.0
The main idea seems to modify NACE to take advantage of attention allocation.

Interesting proposal, I think NACE is a great test case to evaluate attention allocation, but it should not be limited to that. It seems that proposal is not ambitious enough compared to its corresponding RFP. Good value for money though.

Expert Review 5
Overall

5.0
- Compliance with RFP requirements 4.0
- Solution details and team expertise 5.0
- Value for money 0.0
Good value for useful research related to RFP

While this proposal lacks detail, the milestones are well-defined with a realistic scope. The proposal does not address AA in relation to PLN and MOSES however I’m inclined to fund it if other proposals for this RFP aren’t overwhelmingly more compelling. Low cost / good high value.

Expert Review 6
Overall

5.0
- Compliance with RFP requirements 5.0
- Solution details and team expertise 5.0
- Value for money 0.0
A known quantity (NACE and AIRIS), and uses a gymnasium environment for comparing attention allocation schemes.

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Overall

Confidence level that the project is possible at all Feasibility

0
Confidence in a successful outcome considering team, time, and budget Viability

0
Market fit - Balancing needs and benefits against competition Desirabilty

0
To what extent will the project help the AI platform grow? Usefulness

0

Review Headline

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Review Summary

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Expert Review

Overall

Confidence level that the project is possible at all Feasibility

0
Confidence in a successful outcome considering team, time, and budget Viability

0
Market fit - Balancing needs and benefits against competition Desirabilty

0
To what extent will the project help the AI platform grow? Usefulness

0

Review Headline

0 /50 chars

Review Summary

0 /5000 chars

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.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.

Desirability (secondary) Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference? Example:

Creating a translation service from, say Spanish to English might be possible, but it's questionable if such a service would be able to get a significant share of the market

Usefulness (secondary) This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?” For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?). For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things? Examples:

A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

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.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.

Creating a translation service from, say Spanish to English might be possible, but it\'s questionable if such a service would be able to get a significant share of the market

A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

Total Milestones
5
Total Budget
$11,250_USD
Last Updated
3 Feb 2025

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Expert Ratings

Reviews & Ratings

Group Expert Rating (Final)

Overall

5.0

Compliance with RFP requirements 3.3
Solution details and team expertise 3.8
Value for money 3.7

While not addressing the core research posed by the RFP this proposal was deamed to be relevant and of good value.

New reviews and ratings are disabled for Awarded Projects

Expert Review 1
Overall

1.0
- Compliance with RFP requirements 1.0
- Solution details and team expertise 1.0
- Value for money 0.0
Very narrow proposal focused on a single algorithm and its single domain!

Strong reject! Dwane wants to analyze our own NACE algorithm and improve planning by trying different types of AA applied. While the RFP asks for a general framework for AA evaluation for whole PRIMUS, such narrow solution is not applicable for it. Also I want to note the lack of team’s prior experience in the AA field.

Expert Review 2
Overall

2.0
- Compliance with RFP requirements 1.0
- Solution details and team expertise 3.0
- Value for money 0.0
Attention in planning in NACE

Adds attention allocation in NACE planning process for better compute usage. This is useful but misses the wider picture of Attention Allocation in Hyperon and PRIMUS. It could potentially extend to PRIMUS and since it is technologically tractable while details are lacking in the proposal, 2 stars are given as it at least includes a way to measure performance.

Expert Review 3
Overall

4.0
- Compliance with RFP requirements 4.0
- Solution details and team expertise 5.0
- Value for money 0.0
This is a modest proposal but well fleshed out and worthwhile

Basically he suggests to incorporate attention into NACE and evaluate attention mechanisms that way. Why not. Patrick etc. would probably do this anyway but they could use help and the cost here is low.

Expert Review 4
Overall

3.0
- Compliance with RFP requirements 5.0
- Solution details and team expertise 3.0
- Value for money 0.0
The main idea seems to modify NACE to take advantage of attention allocation.

Interesting proposal, I think NACE is a great test case to evaluate attention allocation, but it should not be limited to that. It seems that proposal is not ambitious enough compared to its corresponding RFP. Good value for money though.

Expert Review 5
Overall

5.0
- Compliance with RFP requirements 4.0
- Solution details and team expertise 5.0
- Value for money 0.0
Good value for useful research related to RFP

While this proposal lacks detail, the milestones are well-defined with a realistic scope. The proposal does not address AA in relation to PLN and MOSES however I’m inclined to fund it if other proposals for this RFP aren’t overwhelmingly more compelling. Low cost / good high value.

Expert Review 6
Overall

5.0
- Compliance with RFP requirements 5.0
- Solution details and team expertise 5.0
- Value for money 0.0
A known quantity (NACE and AIRIS), and uses a gymnasium environment for comparing attention allocation schemes.

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

0 /50 chars

Review Summary

0 /5000 chars

The weighted average of the 4 perspectives Overall

5.0
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

3.7
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

3.3
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

3.8

Review Headline

0 /50 chars

0 /5000 chars

Warning: Adding final group rating for this project will prevent expert users from adding new or editing existing reviews

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.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.

Creating a translation service from, say Spanish to English might be possible, but it\'s questionable if such a service would be able to get a significant share of the market

A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

Dwane van der Sluis

Project Owner

Project owner and Sole contributor.

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