musondabemba

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Modular AGI Motivation System with Goal Dynamics

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

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Overview

This proposal delivers a Python/MeTTa framework integrating ECAN weight tuning, PLN-based goal formation, and temporal tradeoff logic (e.g., CurrentNovelty vs. LongTermNovelty). Designed for Hyperon/OpenPsi, it supports concurrent execution of non-conflicting schemata (e.g., physical + social tasks) and resolves human-like dilemmas (e.g., exploration vs. exploitation). Milestones: spec finalization ($6k), prototype development ($12k), and generalization ($12k). It meets SingularityNET’s AGI RFP by modularizing motivational systems for Sophiaverse scalability.

RFP Guidelines

Develop a framework for AGI motivation systems

Proposal Submission (24 days left)

Type SingularityNET RFP
Total RFP Funding $40,000 USD
Proposals 1
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
$30,000_USD
Last Updated
19 Apr 2025

Milestone 1 - Design & Specification Finalization

Description

Draft technical specifications for AGI motivation framework. Define GoalFulfillment predicates (e.g. EvaluationLink → RFS currency) and Ubergoal APIs (e.g. ExploitationVsExploration). Collaborate with SingularityNET’s AGI team for sign-off.

Deliverables

PDF/docs detailing: PLN-ECAN weight adjustment algorithms. ExecutionManager interference checks (e.g. parallel schema compatibility). GitHub branch with stub APIs (no functional logic).

Budget

$6,000 USD

Success Criterion

100% completion of docs reviewed/approved by AGI board. APIs meet OpenPsi modularity standards (peer-validated).

Milestone 2 - Prototype Development

Description

Implement core motivation algorithms (e.g. PLN→ECAN weight adjustments for GoalFulfillment). Build ActiveSchemaPool demo showing non-conflicting parallel execution (e.g. social + physical tasks). Integrate with Hyperon testnets for validation.

Deliverables

GitHub repository with: Functional ExecutionManager prototype. Jupyter notebooks benchmarking interference checks (e.g. schema collision rates). Video demo of MVP inferring subgoals from Ubergoals (e.g. give(Bob me picture)).

Budget

$12,000 USD

Success Criterion

≥85% accuracy in ≥3 OpenPsi scenarios (e.g., Sophiaverse, Unity sims). APIs pass modularity audits by SingularityNET’s AGI review board.

Milestone 3 - Generalization & Community Integration

Description

Refactor APIs for temporal/spatial goal tradeoffs (e.g. CurrentHealth vs. LongTermHealth thresholds). Produce tutorials for ExploitationVsExploration tuning (e.g. parameter sweeps in Jupyter). Assist OpenPsi teams (e.g. Sophiaverse) in deploying the framework.

Deliverables

Docs: API usage examples (e.g. Ubergoal prioritization in ActiveSchemaPool). Video walkthroughs for interference debugging. Code: Docker containers for one-click Hyperon testnet deployment. Pull requests merged into OpenPsi mainline.

Budget

$12,000 USD

Success Criterion

APIs adopted in ≥2 OpenPsi projects (e.g., Sophiaverse, Unity sims). 100% reproducibility per submitted tutorials (peer-verified via SingularityNET forums).

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The weighted average of the 4 perspectives Overall

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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

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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

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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

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