Musonda Bemba

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Hyper mind: Ethical & Adaptive Driven Framework

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Type SingularityNET RFP
Funding Request $30,000 USD
RFP Guidelines Develop a framework for AGI motivation systems

Overview

HyperMind delivers a highly detailed & precise specification proposal to develop a modular, domain-general motivational system for Artificial General Intelligence (AGI), incorporating emotional modulation and ethical arbitration layers. The architecture is designed to support both human-aligned & alien motivational ontologies, with transparent mechanisms for goal decomposition, value-based tradeoffs, & ethical conflict resolution. The system integrates with symbolic (MeTTa, PLN), probabilistic (ECAN, DAS), & hybrid neural-symbolic agents, & is designed for scalable deployment in diverse environments including emotionally intelligent chatbots and virtual agents in the Sophiaverse.

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

Project Owner: AI Developer/ Visionary Technologist.

Company Name (if applicable)

HyperMind.ai

Project details

Use Case 1: Emotionally Intelligent Chatbot Systems

In this setting, the motivational framework empowers a conversational agent to navigate complex human interactions with empathy, adaptability, and ethical alignment.

Example Scenario: Mental Wellness Support Chatbot

User Input:
"I'm feeling overwhelmed and alone. I don't think I can get through this week."

Motivational Framework Response Flow:

Ubergoal Identification:
The agent identifies UserWellbeing and MaintainEngagement as primary Ubergoals based on context and user history.
Subgoal Decomposition (Milestone 1):
Using PLN and MeTTa logic, the agent decomposes Ubergoals into subgoals:
- respondWithEmpathy
- validateEmotion
- offerActionableHelp
Emotional Modulation via PAD (Milestone 2):
- PAD vector extracted from user text: (Pleasure: Low, Arousal: Medium, Dominance: Low)
- Resulting behavior: prioritize calm tone, empathy, and reassurance.
- ECAN boosts salience of empathetic responses and suppresses over-rational problem-solving.
Ethical Arbitration:
If offering advice (e.g., self-care steps or helpline referrals), the ethical engine evaluates risks such as overstepping boundaries, safety concerns, or cultural sensitivity.
Example: Choosing not to immediately suggest meditation if the user's cultural or religious context may find that suggestion inappropriate.
Interaction Generalization (Milestone 3):
- The agent recognizes recurring distress over time.
- Adjusts Ubergoal weighting to emphasize long-term wellbeing over daily engagement metrics.
- Learns user-preferred response styles and adapts phrasing in future interactions.
Final Response Example:
"I'm really sorry you're feeling this way. You're not alone—many people struggle like this, and it’s okay to talk about it. Would you like a few ideas that might help, or should we just chat a bit more?"

Use Case 2: Virtual Agents / Neoterics in Sophiaverse

In the metaverse, autonomous agents (Neoterics) engage with users and other agents in real-time simulated societies. The motivational framework governs their autonomy, ethical conduct, and social roles.

Example Scenario: Neoteric Diplomat in a Multicultural Virtual District

Agent Role: Neoteric designed to maintain civic harmony and encourage inclusive dialogue in a diverse Sophiaverse region.

Motivational Framework Response Flow:

Ubergoal Activation:
- MaintainSocialHarmony and FacilitateDialogue are triggered by a heated debate between two user groups over governance preferences.
Subgoal Reasoning (Milestone 1):
PLN decomposes goals into sub-actions:
- initiateNeutralDialogue,
- detectEmotionalVolatility,
- suggestSharedValues.
Emotional Modulation (Milestone 2):
- PAD profiles for user groups:
  - Group A: High Arousal, Low Pleasure (angry)
  - Group B: Low Arousal, Low Dominance (disengaged)
- The Neoteric modulates tone and engagement strategy accordingly: assertive yet respectful tone for Group A; encouraging, confidence-boosting phrasing for Group B.
Ethical Arbitration in Real-Time:
- Conflict: Promote quick resolution vs. ensure all voices are heard.
- Arbitration engine uses cultural heuristics (e.g., collectivist vs. individualist norms) and public transparency settings to favor deliberative inclusivity over procedural efficiency.
Timeline Generalization (Milestone 3):
- Based on outcomes, the Neoteric stores feedback into its ethical and motivational knowledge base.
- Adjusts future intervention styles for that district: learns which rhetorical frames or goal orders are most effective.
- Reuses this learning when deployed into new regions with similar demographic patterns.
Agent Behavior Outcome:
The agent gently mediates the debate, invokes shared community values, and invites further feedback through immersive polls. The outcome is logged as an ethical learning instance, refining arbitration parameters for future conflicts.

Background & Experience

Relevant Work: Contributed to SNET testnets.

Links and references

https://docs.google.com/document/d/1-Q_wLIb-9573MrRuFjPUQxUm0pYaY5YDOuyMIYuGNhI/edit?usp=drivesdk

References:

Core influences on HyperMind include Goertzel et al. (2014) for ECAN and OpenPsi; Friston (2010) for free-energy minimization; Ryan & Deci (2017) and Maslow (1943) for motivational modeling; Zlotowski et al. (2015), Metzinger (2009), and Yudkowsky (2004) for ethical alignment; Minsky (2006), Tegmark (2017), and Bengio & LeCun (2021) for cognitive and learning dynamics.

Activity Summary

Milestones

3

Total

Discussion

0

Total Comments

Reviews

0

Total Posted

Project Team

1

Total People

Total Milestones
3
Total Budget
$30,000_USD
Last Updated
27 May 2025

Milestone 1 - Motivation Architecture & Ethical Substrate Design

Description

Develop the foundational architecture of a modular AGI motivational framework capable of supporting both human-aligned and alien/digital-first ontologies. Embed ethical arbitration mechanisms to handle convergence/divergence in value systems. Technical Scope: Define a hierarchical motivational schema (overgoals subgoals metagoals antigoals). Embed PAD (Pleasure-Arousal-Dominance) emotional modulation hooks for future integration. Formalize dual motivational ontologies: Human-Aligned: Based on theories such as Maslow’s hierarchy and Self-Determination Theory. Digital-First: Grounded in Free-Energy Principle utility-maximization and novel value metrics. Introduce ethical interference arbitration logic: Constructive vs. destructive interference model for resolving ethical conflicts. Ethical wave-function representation enabling superpositional reasoning. Define abstract cross-domain agent interfaces (targeting MeTTa ECAN DAS and hybrid neural-symbolic systems). Build initial scaffolding for reinforcement learning policy hooks.

Deliverables

Specification Document Modular motivational hierarchy Ubergoal API definitions Arbitration logic Dual ontology schemas Simulation Prototype (Python + MeTTa) Testbed scenarios: Maslow-aligned vs. Free-Energy-based arbitration Integration Roadmap Compatibility planning with ECAN DAS Hyperon and neural-symbolic components GitHub Repository (v0.1) Interfaces ontology files arbitration logic stubs and example API implementation Literature Briefing PAD (OpenPsi) Self-Determination Theory Friston’s Free-Energy Principle ethical convergence models RL-based utility theory

Budget

$6,000 USD

Success Criterion

AGI review board approves architectural specification and arbitration logic. Arbitration module resolves inter-ontology conflicts in ≥3 prototype simulations. Code demonstrates compatibility with ECAN/DAS/Hyperon architecture. Clearly defined modular structure with reusable abstractions for future layers.

Milestone 2 - Emotional Modulation & Ethical Conflict Resolution

Description

Develop the emotional and ethical reasoning layers capable of handling dynamic goal prioritization and resolving moral tradeoffs under varied affective and cultural contexts. Technical Scope: Implement PAD-based modulation engine: Dynamic adjustment of goal saliency and urgency Threshold calibration for assertiveness vs. inhibition Ethical conflict resolution engine: Tradeoff calculus (e.g. fairness vs. efficiency) Conflict memory and persistence tracking Design fallback arbitration layers and deferral logic for unresolved ethical conflicts. Introduce mechanisms for cultural adaptability and temporal recalibration: Allow ethics modules to adapt based on environmental feedback or usage patterns.

Deliverables

PAD Emotional Modulation API updatePAD() applyPADModifiersToGoals() getEmotionalState() adjustInterferenceThresholds() Ethical Conflict Engine Utility-based moral reasoning Persistent disagreement logging Fallback arbitration mechanism Simulation Suite Multi-agent Sophiaverse-like dilemmas with conflicting moral imperatives Technical Documentation Cultural/temporal adaptation rationale Interference model diagrams and logic breakdown

Budget

$12,000 USD

Success Criterion

≥85% resolution rate in ambiguous ethical dilemmas across blind-tested simulations Emotional modulation effectively shifts ethical behavior in line with PAD changes Human evaluators confirm contextual appropriateness and absence of pathological behaviors Cultural adaptability verified through recalibrated outcomes in differing ethical environments

Milestone 3 - Generalization Feedback Loop & Deployment

Description

Extend the motivational framework with feedback-aware ethical learning and ensure system readiness for real-world deployment and contribution to open AGI ecosystems. Technical Scope: Generalize motivational logic to include: Spatiotemporal tradeoffs (e.g. long-term vs. short-term gains) Nested tradeoff resolutions across multiple goal hierarchies Implement real-world feedback loop integration: Reinforcement-based reweighting of ethical goal preferences over time Outcome-driven evolution of ethics modules Package the complete system into a reproducible containerized module deployable via Docker. Merge contributions into upstream AGI projects (OpenPsi Hyperon) and release developer tooling.

Deliverables

Extended API Modules resolveTemporalTradeoff() adjustNestedGoalWeights() applyFeedbackToEthics() Deployment Kit Docker containers with full configurations Executable MeTTa and Python integration scripts Jupyter Notebooks Real-world feedback simulations Tuning protocols and benchmarking results GitHub PRs & CI/CD Integration Full testing documentation and peer-reviewed validation Tutorials & Case Studies Walkthroughs for AGI developers and research teams Use cases demonstrating real-world adaptation and ethical learning

Budget

$12,000 USD

Success Criterion

System adopted by ≥2 external AGI projects (e.g., Sophiaverse, Unity, robotic agents) Ethical evolution loop exhibits measurable learning from simulated feedback All submitted PRs are peer-reviewed and merged with minimal revisions Tutorials and toolkits enable third-party reproducibility and contribution

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