Accelflare

Project Owner

Cognitive Quotient-Based AGI Motivation Framework

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Type SingularityNET RFP
Funding Request $20,000 USD
RFP Guidelines Develop a framework for AGI motivation systems

Overview

We propose a Cognitive Quotient (CQ)-based motivation framework designed for AGI systems within the Hyperon architecture. By quantifying Descriptive, Knowledge, and Reasoning capabilities, this modular system enables adaptive motivation shifts in AGI agents. The framework supports human-like and alien motivational logic, integrates with ECAN, DAS, and MeTTa, and is optimized for real-time reasoning. Initial deliverables include a functional prototype, symbolic logic library, and use-case validations in chatbot and virtual agent environments.

Project Tags:
Community and Collaboration

RFP Guidelines

Develop a framework for AGI motivation systems

Complete & Awarded

Type SingularityNET RFP
Total RFP Funding $40,000 USD
Proposals 19
Awarded Projects 2

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

Our Team

👥 Our Team

The Cq Tester (Cognitive Quotient (CQ) AGI Motivation Framework) is being developed by Accelflare, a research-driven Data Science and AI company specializing in high performance computing, symbolic reasoning, geospatial intelligence, and scalable systems.

We operate at the intersection of cognitive science and engineering. With a strong track record in HPC & AI research and product deployment, we blend innovation with execution to advance next-generation intelligent systems.

Company Name (if applicable)

Accelflare.com

Project details

We propose a Cognitive Quotient (CQ)-based motivation framework designed for integration into AGI systems within the Hyperon architecture. By quantifying Descriptive, Knowledge, and Reasoning (D-K-R) capabilities, the framework enables adaptive, real-time motivational shifts in AGI agents. It is engineered to support both human-like and non-human ("alien digital") motivational structures, aligning with the ethical, symbolic, and computational goals of decentralized AGI platforms such as PRIMUS and Hyperon.

At its core, the system leverages the CQ Tester—a symbolic reasoning evaluation tool that interprets agent behavior through FSMs, Regex, and MeTTa representations. This enables the transformation of internal AGI cognition into structured symbolic performance metrics. These metrics, in turn, guide motivational redirection in response to goal success/failure, cognitive consistency, and ethical alignment.

🔧 Core Features
- Fuzzy Probabilistic Bidirectional Graphs (FPBGs) encoding D-K-R transitions with interval-based and ternary weighting
- Symbolic transition modeling using Finite State Machines and Regular Expressions to represent motivational paths
- Integration with ECAN for resource-aware attention reallocation driven by CQ-derived motivational demands
- Compatibility with DAS, enabling symbolic storage of motivation history and agent decision rationale
- Native MeTTa representations for cognitive rules, allowing dynamic update and ethical policy injection
💡 Use Cases
- Chatbot Systems: Goal prioritization evolves as the agent assesses dialogue structure and engagement through CQ scores
- Virtual Metaverse Agents: Symbolic context awareness governs interaction, priority shifting, and exploration cycles
- Humanoid Robots: Sensor-driven internal states are transformed into MeTTa expressions, adjusting motivator weights based on symbolic cognition
🧪 Project Milestones
- M1: Research & Design Blueprint
  Deliverables: Motivation logic formalism, D-K-R modeling via FPBGs, Regex, and FSM templates in MeTTa
- M2: Prototype Implementation
  Deliverables: Symbolic motivator engine (CQ scores → MeTTa triggers), chatbot and agent demos, ECAN/DAS integration tests
- M3: Final Report & Open Toolkit
  Deliverables: Symbolic motivator API, MeTTa expression sets, ethical test cases, full documentation and evaluation

Open Source Licensing

Custom

This software is released under a limited-access, dual-license model.

🔒 Most components of the Cognitive Quotient (CQ) AGI Motivation Framework—including the multi-agent interaction engine—are proprietary and licensed for **commercial use only**.

📘 Select symbolic logic modules and demo scripts may be made available for academic research under a **Restricted Research License** upon request.

No part of this software may be used, copied, modified, or redistributed for commercial purposes without a formal licensing agreement.

To request licensing for commercial or research use, contact: info@accelflare.com

Background & Experience

The team brings over multiple decades of combined expertise in computer science, cognitive systems, embedded architectures, and high performance computing. Members have led advanced R&D in defense, aerospace, cyber security and AI acceleration, developing systems for real-time cognition, scene understanding, and embedded vision.

Highlights include:

Development of FSM and Regex-based systems.
Patents in AI hardware acceleration, data parsing, and neural network orchestration.
International collaborations with Intel, Microchip, and others on FPGA/SoC-based AI systems.
Research contributions, including training-free generic AI algorithms.
Experience designing cognitive decision architectures using graph-theoretic models.
High Performance Computing for AI Mainframes, Cyber Security and Data Processing

Links and references

Direct References:

Accelflare.com

WO2022005409A1 - A method and apparatus for hardware accelerated data parsing, processing and enrichment - Patent

Team Portfolio Refs:

(PDF) Comparative Analysis and Modern Applications of PoW, PoS, PPoS Blockchain Consensus Mechanisms and New DLTs

WO2023121624A3 - Ensemble learning with parallel artificial neural networks in embedded and integrated systems - Patent

Dynamic Recursive Point Pattern Matching Algorithm for CBIR - File Exchange - MATLAB Central

Activity Summary

Milestones

3

Total

Discussion

0

Total Comments

Reviews

0

Total Posted

Project Team

3

Total People

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

Milestone 1 - CQ Motivation Blueprint

Description

Design a formalized CQ-based motivational architecture. Define how Descriptive Knowledge and Reasoning (D-K-R) scores translate into symbolic motivational states. Establish motivational flow rules using FSM Regex and MeTTa representations.

Deliverables

Technical design document Symbolic model: FPBG + FSM + Regex mappings MeTTa templates for initial motivator logic Timeline and testing plan

Budget

$4,000 USD

Success Criterion

Accepted by reviewers as a valid, extensible symbolic framework Demonstrates logical motivation shift mapping from CQ values

Milestone 2 - Prototype Implementation & Agents

Description

Implement a working prototype that uses CQ scores to drive motivational changes in agents. Create a multi-agent interaction simulation (e.g. chatbot or metaverse-style agent) where goal prioritization changes based on D-K-R logic.

Deliverables

CQ core module in Python/MATLAB/Javascript/MeTTa Agent simulation system with CQ feedback loop Preliminary ECAN and DAS plugin mockups Technical validation results

Budget

$8,000 USD

Success Criterion

Agents demonstrate dynamic motivation adaptation Symbolic CQ scoring loop fully operational MeTTa-based motivator successfully drives decisions

Milestone 3 - Final Integration and Delivery

Description

Complete and package the CQ Motivator system. Finalize symbolic models polish test environments and deliver full technical documentation. Showcase limited research modules and outline the commercial application roadmap.

Deliverables

Final MeTTa motivator library Project documentation & integration guide Summary demo video of agent motivation dynamics Licensing structure and roadmap for commercial scale-up

Budget

$8,000 USD

Success Criterion

Reviewer-approved framework System fully tested in multi-agent setting Project published with research-facing documentation

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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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Accelflare

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Accelflare LTD UK www.accelflare.com

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Software and Development Support for Accelflare company.

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