Document the architecture for a cortex-inspired motivation system. This core of the framework establishes critical properties of a system needed to create motivation in a digital system. This includes critical design specs (e.g. of information flow architecture processing efficiency requirements information traversal mechanisms etc.). Also included in this initial documentation is detailed literature motivating the theory of how behavior selection in this system's design effectively creates the phenomena of motivation -- as long as key system conditions and components are met. Specific detail in how Hyperon is meeting the criteria of specifying how different layers process information how receptive fields are defined and detected and how detection units output weighted recommendations rather than current task-specific commands.

Document overview and explanation of high-level required design specification requirements with explanatory graphics organized with clarity and user-friendly reading in mind Documentation and explanation of how each critical component of architecture design effects motivation and overall theory and literature associated with the motivation system of th - Layer organization specification documenting information flow between processing levels - Receptive field definition guidelines showing how conditions combine across layers - Weight recommendation system specification Report on challenges and benefits of the current Hyperon architecture design related to implementing MoveMeMeta (MMM).

$7,500 USD

Complete documentation of MoveMeMeta system overview, block diagram, graphical descriptions, and description of critical components for minimal implementation reviewed and updated incorporating feedback from RA expert and from several AI architecture designers Verified report on hyperon architecture design readiness for MMM by Hyperon team.

Documentation clearly written and organized given a high-level overview of motivation mechanism in a AI system mapped from theory of understanding higher cognition in terms of anatomy and physiology the Recommendation Architecture (RA) developed by L. Andrew Coward. Detail the behavior selection system based on basal ganglia architecture implementing direct (D1) and indirect (D2) pathways for behavior selection and competition for AI systems.

Documentation given a high-level overview of motivation mechanism in a AI system via mapping concepts from RA - Dual pathway specification - Competition system - Information Theory approach to behavior selection - Conflict prevention mechanism documentation - Effective conflict prevention theory discussion Report comparing and contrasting MoveMeMeta with ECAN including possible integration roadmap

$7,500 USD

Documentation clearly written and organized in reader-friendly manner, of complete pathway interaction patterns and competition mechanism described in AI model context verified from RA expert and evaluated/revised based on variety of different AI design experts Verified report from Hyperon team of report comparing and contrasting MoveMeMeta with ECAN including possible integration roadmap Documentation of research based algorithm candidates for implementing the motivation mechanism.

Document the learning system based on the striatum's matrix/patch organization detailing reward processing and cascading for AI systems as it relates to motivation. Define and describe the learning system and information use in behavior selection. Define a framework for measuring motivation in relation to behavior selection of an AI system.

Documentation of learning system specifications and detailed description of how learning protocol design choices relate to motivation. Including details on how the critical components were selected based on rigorous research. Explaining relationships between critical components that create motivation. - Weight adjustment mechanism specification - Reward signal processing documentation - Temporal correlation system specification - Strategic to tactical reward cascade documentation Documentation defining a methodology for quantifying and qualifying motivation from behavior selection protocols. Report on Hyperon's quantity and quality of current motivation -- challenges and perks to integrate MoveMeMeta -- integration design plan with Hyperon

$7,500 USD

Feedback from open-source peer review supports that documentation clearly explains interactions between critical system components in relation to behavior selection and how motivation emerges, revised and updated accordingly. - Complete weight modification mechanisms - Clear reward propagation paths - Validated temporal correlation - Clear multi-modal learning information Feedback from expert colleagues in agreement with methodology for quantifying and qualifying motivation based on MMM are scientifically and logically sound, revised and updated accordingly. Verification from Hyperon team of analysis and integration report, revised and updated accordingly.

Create comprehensive documentation for integrating all components into a cohesive framework. Submit academic article to peer-reviewed journal. Tutorials examples and verification of approach with literature.

Complete system integration specification Performance optimization guidelines Implementation validation framework Submit academic article to peer-reviewed journal focused for technical audience Comprehensive documentation package focused for developer and general audience use Comprehensive report package focused on use of MoveMeMeta to solve challenges in Hyperon.

$7,500 USD

Feedback from community on documentation package supports clear and effective communication and understanding and user-friendly readibility Feedback from journal on submission status Feedback from Hyperon team is verified findings and plausibility of integration with MMM Everything published on platform for open-source style maintenance and updating.