This first milestone lays the foundation for the proposed research by producing a structured thoughtful plan for developing a motivational driver framework for AGI. It includes a conceptual outline of the framework a timeline with task breakdowns and an early-stage design sketch for how motivational drivers can be represented and reasoned about within Hyperon. The plan will guide future work and ensure that theoretical foundations and implementation considerations are aligned from the start.

Detailed Research Plan: A written document outlining the goals conceptual underpinnings intended outcomes and key challenges of the proposed framework. Agile Task Breakdown and Timeline: A task-oriented roadmap broken into sprints/phases showing how the work will be divided over time including major decision points and checkpoints. This will support iterative development and allow for mid-course adjustments. Preliminary Framework Design: A conceptual model describing the components of a motivational driver (e.g. context rules adaptability) and initial ideas for how these elements may be expressed in MeTTa and integrated into the Hyperon ecosystem

$6,000 USD

Research plan clearly articulates the problem space, objectives, and technical approach. Agile task breakdown presents a realistic, iterative strategy with identifiable milestones. Preliminary design identifies core elements of the framework and demonstrates plausible integration points with MeTTa, ECAN, and DAS—without requiring full design detail at this stage.

This milestone focuses on defining the structure and relationships of motivational drivers—the elements that guide intelligent behavior in AGI. The goal is to develop a clear modular framework that explains: Drivers and Context: Each driver operates within a context that shapes its relevance and intensity. This includes environmental cues internal states and prior experience. Context determines when a driver activates and how strongly it influences behavior. Driver Interactions: Drivers may support compete with or combine with one another. For example curiosity may reinforce learning while efficiency may suppress exploration. These relationships must be explicitly represented. Evolution Over Time: A driver’s meaning and priority can shift with experience. For example early repetition might increase motivation (“practice makes perfect”) but over time overexposure might lead to reduced motivation (“familiarity breeds contempt”). The framework must support this kind of adaptive change. This milestone includes expressing several representative drivers and their interactions in MeTTa demonstrating how motivational dynamics can be encoded and evolve over time. Finally it includes preliminary investigation into how such drivers might be applied meaningfully within AGI systems to influence learning and adaptation—laying the groundwork for later integration and prototyping.

Framework Design Document including: Definitions of core motivational drivers and their relationship to context (e.g. environmental conditions internal states). Characterization of driver interactions (supportive competitive compound). Description of how drivers evolve over time based on experience. Initial Representations in MeTTa demonstrating: A sample set of base and interacting drivers. Context-aware rules that modulate driver activation and intensity. Mechanisms for rule adaptation over time (e.g. changing priorities or meanings). Preliminary Application Analysis: Exploration of how motivational drivers may be used to influence AGI learning processes. Identification of possible “hook points” in AGI training or decision-making where driver influence could be applied in future work.

$12,000 USD

A clearly defined and modular motivational framework that distinguishes drivers, contexts, and interactions. At least 3–5 motivational drivers represented in MeTTa, each with: Associated contextual conditions, Rules for interaction (e.g., reinforcement, competition), Demonstrated potential for change over time. Conceptual demonstration (not implementation) of how driver-based motivation could influence AGI learning behavior. Initial mapping of use scenarios or “hook points” in AGI development where the framework could later be applied.

This final milestone consolidates the research framework development and early representational work into a complete package suitable for guiding future integration into AGI systems. The focus is on delivering a well-defined and documented motivational driver framework supported by early MeTTa prototypes that demonstrates how motivational structures can evolve over time interact with each other and eventually influence learning behavior. While full integration with Hyperon is beyond scope this milestone includes a clear analysis of potential integration pathways identifying conceptual “hook points” within AGI learning pipelines and how motivational drivers could influence internal goal prioritization attention or adaptation mechanisms. In addition this milestone will document different classes of drivers (e.g. base compound context-dependent) their structural representation and how changes in experience or feedback can trigger rule rewrites—capturing long-term shifts in motivational patterns (e.g. from reward-seeking to avoidance). This directly supports AGI learning systems that evolve their own internal goals in response to success failure and changing priorities. A key part of this milestone is a clear roadmap that articulates how this work can be scaled and extended in future stages to influence AGI behavior more directly as well as how it might eventually be connected to core Hyperon components like ECAN and DAS.

Final Research Report Complete description of the motivational framework: drivers contexts interactions and temporal evolution. Definitions and examples of core driver classes (base compound context-sensitive). Explanation of how motivational rules may be rewritten over time in response to outcomes and experience. MeTTa Prototypes Code samples showing representation of several driver types. Example interaction dynamics (e.g. competing or supporting drivers). Simple rule rewrite scenarios illustrating motivational evolution (e.g. from “practice makes perfect” to “familiarity breeds contempt”). Preliminary AGI Application Concepts Identification of points within AGI learning workflows where motivational drivers could meaningfully influence behavior. Conceptual examples of how motivational structure could shift learning focus task selection or behavioral adaptation. Documentation & Roadmap Technical explanation of the representational structures used. Annotated MeTTa examples. Recommended next steps for scaling integrating with ECAN/DAS and extending to larger AGI systems.

$12,000 USD

A clearly articulated, modular framework for motivational drivers, with defined classes and relationships. At least several working MeTTa-based driver examples demonstrating structure, context interaction, and evolution. Demonstrated ability to represent both human-aligned and non-human (e.g., optimization-driven) motivational patterns. A coherent roadmap with actionable insights into how the framework can be extended and integrated with AGI training pipelines or Hyperon components.