olivier Georgeon

Project Owner

Interactional Motivation (IM)

Expert Rating

n/a

Type SingularityNET RFP
Funding Request n/a
RFP Guidelines Develop a framework for AGI motivation systems

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Overview

Interactional Motivation (IM) implements self-motivation in artificial agents by assigning value to agent-environment interactions themselves, rather than valuing the outcome or states of the environment. Unlike traditional methods that rely on predefined goals or external rewards, IM drives agents to autonomously organize behavior and construct knowledge from their experience of interaction. This aligns with genetic epistemology and theory of enaction. Applying IM within AGI frameworks offers a pathway to study autonomous cognitive development, constitutive autonomy, and autonomous goal construction, potentially moving beyond traditional reinforcement learning and problem-solving AI.

RFP Guidelines

Develop a framework for AGI motivation systems

Internal Proposal Review

Type SingularityNET RFP
Total RFP Funding $40,000 USD
Proposals 21
Awarded Projects n/a

SingularityNET

Apr. 14, 2025

Develop a modular and extensible framework for integrating various motivational systems into AGI architectures, supporting both human-like and "alien digital" intelligences. This could be done as a highly detailed and precise specification, or as a relatively simple software prototype with suggestions for generalization and extension. Bids are expected to range from $15,000 - $30,000.

Proposal Description

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Total Milestones
3
Total Budget
$20,000_USD
Last Updated
27 May 2025

Milestone 1 - Technical Foundations and Platform Selection

Description

This milestone aims to ensure that a well-informed and collaborative decision has been made regarding the choice of development platform and tools for the project. This process will be conducted jointly by the IM team and the SyngularityNET team. One of the candidate platforms under consideration is AIRIS (Autonomous Intelligent Reinforcement Interpreted Symbolism). Another option is to interface the SingularityNET tools with the Petitcat platform.

Deliverables

Detailed research plan specifying the chosen platform breakdown of tasks with timeline and framework design. A functional prototype defining a benchmark to evaluate the IM agent.

Budget

$4,000 USD

Success Criterion

The detailed research plan is approved by both parties. The development platform is operational. The initial configuration has been successfully completed. A basic example is running to demonstrate the benchmark that will be used in subsequent milestones.

Milestone 2 - Intermediary demonstration

Description

This milestone consists in demonstrating the integration and operation of an Interactional Motivation agent within the selected development platform.

Deliverables

A functional prototype showcasing the behavior of an Interactional Motivation agent implemented in the chosen platform. Draft implementation of prototype showcasing the behavior of an Interactional Motivation agent initial testing results and analysis against standard benchmark chosen in Milestone 1.

Budget

$8,000 USD

Success Criterion

The agent, driven by interactional motivation, successfully completes the designated benchmark within the selected platform.

Milestone 3 - Final delivery

Description

Completion and dissemination of the project’s final results through pedagogical materials demonstrations and scientific publication.

Deliverables

Jupyter Notebooks: A series of educational tutorials will be published in the form of Jupyter notebooks to facilitate understanding and dissemination of the project outcomes. Videos: Demonstration videos will be made publicly available on video platforms to showcase the results in action. Scientific Publication: A peer-reviewed scientific article presenting the project’s findings and contributions will be submitted for publication.

Budget

$8,000 USD

Success Criterion

The interactionally motivated agent successfully completes all required levels within the selected platform. The tutorials are published online The scientific publication is submitted to a reputable academic outlet.

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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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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.

olivier Georgeon

Project Owner

Primary investigator Lead developer

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Howard Schneider

Researcher

Collaborate on the underlying issues of motivation. Help translate the principles of the motivational system into a useful algorithmic approach as well as embodiment in software.

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jeff Thompson

Developer

Coding support and testing

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