Establish the foundational architecture for the AGI motivational system. Define a modular hierarchy of overgoals, subgoals, metagoals, and antigoals with embedded emotional (PAD) and attentional modulators. Develop initial dynamic evaluation metrics (e.g., fitness, novelty, measurability) and formalize dual motivational ontologies (human-aligned vs. alien/digital-first) using structured ethical wave function representations. Incorporate an interference-aware arbitration layer to simulate agreement/convergence from ethical superposition (see: constructive vs. destructive ethical interference). Create abstract interfaces for cross-domain deployment in symbolic (MeTTa), probabilistic (ECAN, DAS), and hybrid (neural-symbolic) agents. Establish stubs for future reinforcement learning policy hooks.

Spec PDF: Modular motivational hierarchy, Ubergoal APIs, interference arbitration logic, domain-general abstractions. Simulation Script: Arbitration tests for Maslow-like goals vs. Free-Energy-based expectations (Python + MeTTa). Integration Map: Roadmap for ECAN, DAS, MeTTa, and symbolic-neural adapters. GitHub v0.1: Interfaces, arbitration stubs, ontology files, and initial API implementation. Literature Brief: OpenPsi PAD, Self-Determination Theory, Friston free energy, RL-based utility models, and ethical convergence theory.

$6,000 USD

AGI board signs off on architecture spec and ethical interference arbitration logic. Arbitration layer produces coherent cross-ontology goal resolutions in ≥3 test scenarios. Preliminary code shows compatibility with ECAN/DAS/Hyperon. Clearly modular, with reusable abstractions for both human-aligned and non-human motivational frames.

Develop the emotional modulation engine using the Pleasure-Arousal-Dominance (PAD) model and link it to dynamic goal prioritization, urgency modulation, and assertiveness thresholds. Build a modulator-stacked ethical reasoning layer to handle moral tradeoffs through constructive ethical wave interaction (e.g., fairness vs. efficiency, autonomy vs. safety). Encode a mechanism for persistent disagreement handling by flagging divergent ethics that resist convergence and trigger fallback arbitration layers or deferral to external context evaluators. Ensure ethics modules are culturally adaptive by allowing temporal recalibration based on feedback from usage and outcome models.

PAD Emotional API: updatePAD(), applyPADModifiersToGoals(), getEmotionalState(), adjustInterferenceThresholds(). Ethical Conflict Engine: Weighted utility tradeoff resolution with persistent disagreement handling and ethical memory. Simulation Suite: Sophiaverse-like social dilemmas showcasing conflict resolution and PAD impact (e.g., simulated scenarios of moral conflict). Documentation: Rationale for cross-cultural flexibility, ethical recalibration logic, and use of constructive/destructive ethical interference.

$12,000 USD

≥85% successful moral conflict resolution in ambiguous simulations (double-blind tested). Human evaluators confirm context-aware ethical judgment with no pathological bias or runaway optimization. System demonstrates modulation based on shifting PAD states and cultural/temporal ethics deltas.

Expand the motivational framework to support spatiotemporal tradeoffs, nested tradeoff resolution, and feedback-informed ethical evolution. Implement real-world feedback loops to adjust ethical goal weightings using longitudinal reinforcement (e.g., how real-world outcomes reshape goal priorities). Deploy the finalized motivational system as a Dockerized Hyperon module, with reproducible tuning and benchmarking notebooks. Push PRs into OpenPsi repositories and provide materials for external contributors. Promote adoption with ethical validation case studies and tutorials.

Generalized APIs: resolveTemporalTradeoff(), adjustNestedGoalWeights(), applyFeedbackToEthics(). Deployment Kit: Docker containers, config templates, MeTTa & Python execution scripts. Notebooks: Tuning modulators, real-world ethical feedback simulation, and evolution tracking. Pull Requests: Merged into OpenPsi & Hyperon core with full CI/CD testing and review. Walkthroughs: Developer tutorials on deploying in simulations or embodied agents.

$12,000 USD

Adopted in ≥2 external AGI research environments (e.g., Sophiaverse, Unity AGI sim, or robotic agents). Ethical evolution loop demonstrates learning from simulated real-world outcomes (e.g., reduced regret/utility loss). PRs pass peer validation and merge with minimal revision; tutorials lead to reproducible test cases.