Design the core motivational graph engine based on a flexible node-edge architecture where each node represents a drive or subdrive and edges define the influence or modulation between them. This engine will serve as the foundational substrate for modeling motivation in AGI agents enabling structural representation of drive hierarchies dependencies and dynamics. Alongside the graph system a set of basic APIs will be developed to allow external components or agents to create edit and query the motivational structure programmatically. These APIs will support functions such as drive weight adjustment satisfaction threshold updates and influence rewiring. To ensure usability and modularity the system will include initial test schemas for common motivation types (e.g. novelty cooperation self-preservation). Foundational logic for integrating this graph engine with simulated agents will also be implemented allowing drive states to update based on environment feedback. This milestone will establish the engine’s structure interfaces and extensibility for later stages.

1. Graph Engine with Dynamic Drive Linking A motivation graph engine will be developed to support dynamic linking of drives and subdrives. Nodes and edges will represent evolving motivational relationships enabling runtime updates and reflecting contextual shifts in agent behavior. 2. YAML-based DSL for Defining Motivational Schemas A domain-specific language (DSL) using YAML will be introduced to define motivational structures. Users will be able to declare drives thresholds and influence links in a readable format allowing easy customization and reuse across agent types. 3. REST API for Drive State Manipulation A RESTful API will be implemented to interact with the motivation engine. Endpoints will allow reading and modifying drive states adjusting weights and querying influence relationships enabling external systems to monitor and influence agent motivation.

$4,000 USD

*Ability to create, modify, and query motivation graphs dynamically in real time. *Support for: - Defining drives, weights, thresholds, and edges via YAML schema and REST API. - Real-time structure instantiation from config files. -Runtime graph updates without restarts. *Simulated agents must show correct motivational state changes in test environments. *Validation through: - Automated tests (graph integrity, API correctness, internal state accuracy). - Scenarios demonstrating modularity, adaptability, and readiness for agent integration.

Implement a reinforcement learning framework that enables AGI agents to adapt their motivational structures by updating drive weights and inter-drive influences based on behavioral outcomes. The system will track the agent’s interactions measure the effectiveness of drive-driven behaviors and use those results to modify the underlying motivational graph. A meta-reward signal will be introduced to reinforce successful adjustments to the motivational system itself not just external task completions. This meta-reward will evaluate how well the modified motivation contributes to sustained goal-aligned behavior over time. The RL agent will learn to balance internal satisfaction with external success gradually optimizing the motivational landscape. This mechanism allows the agent to evolve new drive configurations resolve internal conflicts and adapt to changing environments. The framework will be tested using simulated agents and recorded scenarios to ensure that meta-reward dynamics produce stable interpretable and meaningful motivational evolution across episodes.

1. RL Module with Feedback Loop A reinforcement learning module will be developed to monitor motivation-behavior-outcome chains and adjust drive parameters accordingly. It will support real-time feedback and enable agents to optimize their drive structures based on experience over time. 2. Meta-Reward Function for Drive Tuning A custom meta-reward function will be implemented to reward the agent not only for task success but for effective motivational adjustments. The function will evaluate the impact of drive changes on long-term behavior alignment promoting adaptive drive tuning. 3. Training Logs and Analysis Reports Detailed logs will be generated for all training sessions capturing drive adjustments reward signals and behavioral changes. Reports will summarize learning progress stability metrics and the evolving structure of the agent’s motivational graph.

$5,000 USD

*Agent must learn and improve performance via motivational graph updates. *Drive changes must result in: - Better task completion, reward gain, or behavioral consistency. *Reinforcement Learning must: - Link drive updates to meta-reward signals. - Promote adaptive motivation tuning over static configurations. *System should show: - Conflict resolution and adaptability in dynamic environments. - Statistical performance gains in experiments comparing adaptive vs. static agents.

Integrate the motivational graph engine into lightweight AGI agents to enable live motivation-driven decision-making within controlled simulation environments. These agents will operate in toy worlds such as GridWorld and TextWorld where they will encounter varying tasks environmental stimuli and constraints. The motivational graph will govern each agent’s decision logic by dynamically influencing priorities internal goals and action selection. As agents navigate these environments they will rely on their evolving motivational structures to adapt to success or failure shifting behavior based on real-time feedback. This milestone will also include encoding basic survival exploration and social drives within the graph to simulate nuanced goal prioritization. The integration will test whether agents are capable of revising their drives meaningfully as a function of environmental changes or internal outcomes. Successful execution will show that the system produces interpretable drive shifts measurable behavior adaptation and agent-specific motivational divergence across multiple simulation runs.

1. Two Agents with Evolving Motivational Structures Two distinct agents will be implemented each equipped with the motivational graph engine. As they interact with their environments their drives will adapt differently based on experiences demonstrating the system’s flexibility and personalized evolution. 2. Simulation Logs and Video Runs Each simulation will produce detailed logs of motivational states decisions and drive updates. Selected runs will be recorded as videos or animations to visualize how drive shifts influence behavior over time providing insight into adaptive motivation in action. 3. Behavior Comparison Metrics A suite of metrics will be created to compare agent behavior across episodes including task efficiency adaptability and motivational stability. These comparisons will highlight the impact of evolving drives on decision-making and long-term performance outcomes.

$4,000 USD

* Agents must adjust drives dynamically based on feedback from simulation environments. * Drive modifications should: - Improve behavior (e.g., task efficiency, goal handling). - Be distinct across agents, confirming personalized motivational evolution. * Metrics and logs must reflect: - Correlation between drive updates and performance. - Behavioral stability and improvement across simulations.

Create an introspection module capable of capturing and exporting structured internal representations of an agent’s motivational state. This module will periodically generate JSON-based snapshots that reflect the current configuration and dynamics of the motivational graph including active drives drive intensities volatility scores and alignment with behavioral outcomes. These snapshots will enable developers and researchers to analyze how internal motivation evolves in response to environmental changes and agent experiences. The module will also track stability metrics such as drive volatility and motivation-to-behavior consistency to assess how balanced or reactive the agent’s internal state is over time. To support interpretability and user engagement a web-based dashboard will be developed to render this introspective data visually showing drive graph evolution key metric trends and comparative agent views. This visualization tool will help identify patterns in motivational drift highlight decision pivots and support external auditing of agent behavior from a human-aligned perspective.

1. JSON Reporting Schema for Motivation Snapshots A structured JSON format will be created to represent motivational states including drive intensities volatility and behavior alignment. These snapshots will provide interpretable timestamped records of the agent’s internal motivational dynamics. 2. Dashboard UI for Viewing Drive Graphs and Drift Trends A web-based dashboard will be developed to visualize evolving motivational graphs and track changes in drive structures over time. The interface will include interactive graph views metric charts and time-based drift analytics for introspection and monitoring. 3. Documentation for Dashboard Usage User documentation will accompany the dashboard explaining how to launch navigate and interpret the visualization tools. It will include setup instructions schema references and example insights to support community use experimentation and feedback.

$3,500 USD

* System must output accurate, structured JSON snapshots of motivational states. *Snapshots should reflect: - Drive intensities, satisfaction, volatility, and alignment with actions. *Dashboard must: - Visually render graph evolution and motivational metrics clearly. - Include timeline views, trend charts, and annotations. *Validation through: -Internal tests and external user feedback. - Ability for users to trace internal state changes to agent behavior.

Perform comprehensive final testing of the entire MetaMotivator system to ensure all modules function as expected both independently and as a fully integrated pipeline. Each component motivation graph engine reinforcement learning module introspection system and API interface will undergo validation to confirm correct functionality robustness and interoperability. The full MetaMotivator framework will be documented in detail including setup instructions usage examples configuration guides and technical explanations of each module. Two complete use case walkthroughs will be published highlighting how agents evolve motivational structures and adapt over time in different simulation scenarios. These case studies will include visual outputs behavior logs and performance analysis. To engage the broader AGI and research community all code documentation and demo materials will be uploaded to a public GitHub repository. The project will also include a set of video presentations to illustrate key concepts and demonstrate system capabilities in action.

1. Final Benchmarks and Evaluation Suite A complete benchmarking suite will be created to test agent behavior motivational drift and learning efficiency. Evaluation metrics will be standardized and applied across use cases to verify system performance adaptability and internal consistency. 2. Public GitHub Repository with Code and Docs All project components including source code configuration files test environments and documentation will be published in a public GitHub repository. The repo will be structured for ease of use and open for contributions replication and experimentation. 3. Case Study Write-Ups and Video Demos Detailed write-ups of each simulation scenario will be produced to illustrate system behavior in real-world use cases. Accompanying video demonstrations will visualize agent learning motivational changes and introspection outputs across multiple environments.

$3,500 USD

*All system modules (graph, RL, API, introspection) must function correctly independently and together. *System should be: -Robust, interoperable, and well-documented.