Develop a rigorous research plan that defines the project’s theoretical foundations, formal mathematical framework, and practical implementation path. This milestone will include the formalization of the Motivational Neuron Architecture, where motivational drives send voting signals to nonlinear neurons aggregating influence via generalized means, with Quadratic Voting as a special case. The research will formalize the use of reward prediction error (RPE) learning inspired by dopaminergic coding and its integration into a predictive coding loop over motivational states. The plan will detail how the architecture dynamically computes urgency via risk-sensitive information fusion and how action selection emerges from the aggregation of motivational signals. It also includes precise specifications for integration with Hyperon components such as ECAN, DAS, and MeTTa.

A comprehensive research report containing: - Literature review of motivational theories, risk profiles for information fusion, Quadratic voting, and psychological and biological theories of motivation. - Mathematical formulation of the generalized mean neuron framework for motivational aggregation. - Description of the predictive coding loop, reward prediction error mechanism, and credit update dynamics. - Integration design with Hyperon components (ECAN for dynamic activation, DAS for distributed motivational state storage, MeTTa for logic encoding). - Urgency computation methods using risk profiles derived from information fusion theory. - Ethical alignment framework and welfare metric design.

$5,100 USD

Completion of a publishable-level research plan approved by internal reviewers or advisors. The document must provide a clear architecture that could be implemented by a technical team, including a detailed mathematical model and algorithm structure.

Develop a fully functional prototype of the Risk-Aware Motivational Architecture, showcasing how motivational drives, urgency computation, and decision neurons interact dynamically. This prototype will implement the full predictive loop, including urgency calculation via risk profiles, action valuation via learned expectations, and aggregation of motivational influence via generalized mean neurons. The system will demonstrate RPE-based learning, enabling drives to update their action expectations over time. It will also include mechanisms for human-in-the-loop feedback, allowing real-time adjustments of drive parameters and welfare weights.

A functional prototype MeTTa-based implementing: - Motivational modules with urgency and risk profiles. - Generalized mean-based action selection, with configurable exponent parameters (including Quadratic Voting as a special case). - Welfare metric feedback and adaptative credit reallocation. - Ethical constraint enforcement within the decision loop. - Unit tests in limited scenarios and a clear plan regarding how to generalize to deal with broader domains. - Full documentation of prototype design, installation, configuration, and usage. - Logs and screenshots from test scenarios.

$6,060 USD

The prototype can demonstrate the complete motivation-action-learning loop. This includes properly implementing motivational drives that dynamically adapt their expectations based on reward prediction error dynamics. The system must exhibit how action selection is governed by nonlinear neuron aggregation using generalized mean functions, and this behavior should be validated by varying the exponent to observe distinct decision-making patterns ranging from conservative to risk-seeking strategies. A key indicator of success is the prototype’s capacity to enforce ethical constraints while dynamically adjusting motivational priorities based on internal learning and external human feedback. Furthermore, the prototype must function in real time, operating without noticeable delays during small-scale simulations. The source code is expected to be clean, modular, well-documented, and reproducible, enabling future developers to extend and integrate the system easily.

Complete all framework components and conduct comprehensive evaluation, documentation, and packaging for integration with Hyperon. Extend the prototype to broader contexts, refine the performance, and formalize the framework for future scalability and research. The final milestone ensures the delivery of a robust, scalable, and ethically aligned motivational architecture ready for use in Hyperon-based AGI systems.

A Final Delivery Package containing: - Final version of motivational framework code. -- Fully functioning motivational neuron network with generalized mean aggregation. -- Reward prediction error learning with historical tracking of action expectations. Full welfare metric evaluation pipeline, including motivational satisfaction, ethical alignment, and efficiency. - Integration guides for Hyperon components: -- ECAN dynamic activation control. -- DAS storage of motivational histories and welfare evaluations. -- MeTTa logic for motivational rules, urgency computations, credit updates, and ethical checks. - Complete developer and user documentation package containing deployment instructions, and extensibility guidelines. - Performance analysis (adaptability, efficiency, alignment scores) - Case study results demonstrating: -- Behavioral adaptation over time. -- Influence of varying risk profiles and generalized mean exponents. -- Examples of both human-like and alien motivational configurations. - Public Town hall presentation. - Final report summarizing the project.

$3,840 USD

The success of Milestone 3 will be determined by delivering a complete and modular motivational framework that meets the RFP’s objectives for real-time, adaptive, and ethically aligned decision-making systems. The framework must demonstrate its ability to dynamically adapt behavior based on internal learning through reward prediction error mechanisms and external input via human feedback. Additionally, it should be validated that the generalized mean neuron aggregation produces distinguishable behavioral outcomes, reflecting variations in risk sensitivity and decision-making styles, ranging from risk-averse to decisive configurations. The framework should consistently adhere to ethical constraints and demonstrate measurable alignment with human values and welfare metrics under dynamic and changing conditions. Comprehensive documentation must accompany the system, guiding technical developers and non-technical stakeholders to deploy, extend, and integrate the system within Hyperon or other AGI infrastructures. Finally, the public town hall presentation must effectively communicate the project’s results, demonstrate the framework's functionality, and receive positive engagement and constructive feedback from the community, indicating the broader relevance and readiness of the framework for further adoption and development.