MoveMeMeta (MMM) Motivation Framework

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Expert Rating 3.7
simuliinc
Project Owner

MoveMeMeta (MMM) Motivation Framework

Expert Rating

3.7

Overview

This 4-month, $30,000 project aims to design a biologically-inspired recommendation architecture theory-based motivational framework for AGI systems. Led by Simuli, will deliver a submission of peer-reviewed article reviewing current motivation in AI and details of our approach MovemeMeta (MMM) -- comprehensive documentation, resources, tools, for minimal design specifications for a systems architecture view of any learning system -- a report of analysis and design integration with Hyperon DAS, ECAN, and MeTTa systems. The solution addresses limitations in current AI/AGI motivation efficiency, process control organization, autonomy, and human-ai alignment in a framework to guide motivation.

RFP Guidelines

Develop a framework for AGI motivation systems

Complete & Awarded
  • Type SingularityNET RFP
  • Total RFP Funding $40,000 USD
  • Proposals 12
  • Awarded Projects 2
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SingularityNET
Aug. 13, 2024

Develop a modular and extensible framework for integrating various motivational systems into AGI architectures, supporting both human-like and alien digital intelligences. This could be done as a highly detailed and precise specification, or as a relatively simple software prototype with suggestions for generalization and extension.

Proposal Description

Company Name (if applicable)

Simuli Inc

Project details

Performance Metrics

  • Development Progress

    • Documentation completeness

    • Specification clarity

    • Architecture design coverage

    • Component interaction definitions

  • Framework Design

    • Recommendation system design completeness

    • Selection mechanism specification

    • Learning system definition

    • Integration interface design

  • Usefulness

    • Blueprint for AGI motivation systems

    • Guide for future implementations with open-source maintenence of protocol

    • Reference for biological-inspired architectures

    • Framework for ethical alignment

  • Research Value

    • Review and approach to framework for motivation in AGI submission to peer-reviewed journal

    • Biological-to-artificial mapping specifications

    • Motivation design evaluation and plans for Hyperon

Problem Description

Current AGI systems face a fundamental architectural limitation: they rely on protocols that struggle to exhibit organizational control of behaviors and constructively autonomous human-like behavior selection. Recent theoretical work has established that there is a method to understanding motivation in human higher cognition by comparing and constrasting two possible architectures for complex control systems:

  1. Instruction Architecture: Used in traditional computing, where conditions trigger unambiguous commands learned through consequence feedback from human defined protocols

  2. Recommendation Architecture: Used in biological brains, where conditions provide weighted recommendations for multiple behaviors which are then learned to be interpreted from both consequence feedback and information:resource management protocols

For AGI motivation systems that must learn and adapt spontaneously through emergent motivation, the instruction architecture is fundamentally inadequate because:

  • Direct condition-to-behavior mappings require extensive testing after subtle changes (brittle)

  • Learning becomes impractical over time due to the risk of undesirable side effects (resource inefficiency)

  • Novel situations cannot be handled effectively without re-learning correct behavior responses involving human defined feedback

  • Real-time adaptation in complex environments is severely limited

Furthermore, while biological systems demonstrate the effectiveness of recommendation architecture theory based approaches, there exists only one example application of principles to AGI design and few in-depth design considerations with current AI/AGI systems. This gap creates significant challenges for developers attempting to implement efficient, adaptive motivation systems in AGI.

Solution Description

We propose implementing a viable framework to guide design of motivation systems -- MoveMeMeta (MMM): a recommendation architecture (RA) based motivational framework directly inspired by the brain's evolutionary demonstration. Our framework maps the same principles that allow biological brains to efficiently select useful behaviors from ermergent motivation while maintaining stability. Rule based engines and DNN, GNN, and neurosymbolic approaches struggle to implement a behavior or task selection protocol that is efficient, scalable, autonomous, and beneficial to the system and it's surrounding environmental system. This results in a lack of dynamic controll between internal processes and priority alignment that can adapt efficiently. Based on L. Andrew Cowardsur framework RA, MMM circummvents this problem with key modularity separation of learning features from behavior selction, a robust learning system for turning information representations into behaviors, and the useful/beneficial alignment of feedback loops between the system and it's surrounding environment to form emergent effective motivation.

Core Framework Components:

  1. Condition Detection System

  • Implements a layered hierarchy similar to cortical organization

  • Each detection unit outputs weighted recommendations for multiple behaviors

  • Natural progression from strategic through tactical to detailed behaviors

  • Supports both human-like and digital motivation patterns through flexible receptive fields

  1. Behavior Selection System

  • Dual-pathway competition mechanism (D1/D2-like) for behavior selection

  • Matrix component for managing behavior release

  • Patch component for handling reward processing

  • Dynamic selection pressure regulation through feedback loops

  1. Learning System

  • Hierarchical reward cascade from strategic to detailed behaviors

  • Separation of behavior selection from reward processing

  • Meta-learning through reward-behavior reinforcement

  • Support for both immediate and long-term strategic learning

Integration with Hyperon

The framework maps naturally to Hyperon components while maintaining biological principles:

  1. DAS (Distributed Atomspace):

  • Stores condition detections and recommendation weights

  • Manages knowledge representation across layers

  • Supports flexible receptive field definitions

  1. ECAN (Economic Attention Allocation):

  • Implements behavior selection competition

  • Manages resource allocation based on recommendation strengths

  • Controls selection pressure through feedback

  1. MeTTa Language:

  • Expresses relationships between conditions and recommendations

  • Defines both human-like and AI motivation patterns

  • Supports reward signal propagation

Technical Implementation Details

Our framework implements based on biological principles of behavior selection in the recommendation architecture. Just as the cortex and basal ganglia work together to select behaviors in biological systems, our framework describes the system architecture requirements for a digital version of motivation to emerge. We aim to submit our approach and review of current systems as a peer-reviewed article. We also plan to provide a package of documentation that can be open-source peer reviewed online for updates and maintence with a focus on clarity and organization intended for AI developers -- including documentation, tutorials, examples, and other tools/resources. We also provide a separate report for Hyperon analysis of current motivation, opportunities and challenges of implementing MMM, and a fully detailed integration plan, verified by Hyperon team.

Condition Detection System

The system mirrors cortical organization, where different layers process increasingly complex combinations of information:

  • Layer organization follows biological principles of information flow

  • Each detection unit outputs recommendations with different weights for multiple behaviors

  • Creates natural hierarchy from strategic through tactical to specific behaviors

  • Supports flexible definition of both human-like and AI motivation patterns

Behavior Selection System

Following basal ganglia architecture, the system implements:

  • Direct pathway (D1-type) for strengthening preferred behaviors

  • Indirect pathway (D2-type) for suppressing competing behaviors

  • Competition-based selection process for real-time performance

  • Dynamic regulation of selection pressure through feedback loops

Learning System

The dopamine-inspired reward mechanism includes:

  • Separation of behavior selection from reward processing

  • Sophisticated learning system for weight adjustment

  • Reward cascades across behavior levels

  • Meta-learning through reward-behavior reinforcement

Competition and USP

Unique Design Elements

  • Long term theory of recommendation architecture application in AGI

  • Complete biological principle mapping to digital learning systems

  • Clear implementation pathways for Hyperon ecosystem

  • Comprehensive documentation guidelines with tutorials, examples, and open-source like environment for feedback, updates, and maintence

Design Advantages

  • Based on proven biological systems that prioritize efficiency

  • Supports AI motivation as a emergent phenomena to select behaviors

  • Scales from simple to complex behaviors

  • Potential to add value in guiding human-AI alignment within the system
  • Enables natural learning processes

Open Source Licensing

MIT - Massachusetts Institute of Technology License

Proposal Video

Not Avaliable Yet

Check back later during the Feedback & Selection period for the RFP that is proposal is applied to.

  • Total Milestones

    4

  • Total Budget

    $30,000 USD

  • Last Updated

    8 Dec 2024

Milestone 1 - MoveMeMeta Overview and Architecture Design View

Description

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.

Deliverables

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).

Budget

$7,500 USD

Success Criterion

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.

Milestone 2 - Mechanism of Motivation Documentation and Check

Description

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.

Deliverables

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

Budget

$7,500 USD

Success Criterion

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.

Milestone 3 - Utility of Learning in Motivation

Description

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.

Deliverables

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

Budget

$7,500 USD

Success Criterion

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.

Milestone 4 - Final Delivery and Framework Compilation

Description

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.

Deliverables

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.

Budget

$7,500 USD

Success Criterion

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.

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Expert Ratings

Reviews & Ratings

Group Expert Rating (Final)

Overall

3.7

  • Feasibility 4.0
  • Desirabilty 4.0
  • Usefulness 4.0
  • Expert Review 1

    Overall

    1.0

    • Compliance with RFP requirements 1.0
    • Solution details and team expertise 1.0
    • Value for money 1.0
    Fishy neuroscience?

    "Our framework maps the same principles that allow biological brains to efficiently select useful behaviors from ermergent motivation while maintaining stability." I don't buy that and find it a very suspicious claim. I would argue one knows the principles of biological brains at this point. Despite the huge efforts in Computational Neuroscience there are many theories and it is not yet clear which ones are right. Also their approach while somewhat motivated by neuroscience (at least on a high level) seems overly complex and does not pin down the guiding principles that could explain motivation. For instance picking one particular point "Implements a layered hierarchy similar to cortical organization", without further details it is not clear what this even means as there are many interpretations of how the cortex might be organized and there are many aspects of it. For instance existing models such as HTM and Sparsey do not explain motivation at all at current stage. Similar issues I have with other points like "Natural progression from strategic through tactical to detailed behaviors" which seem to hang in the air. As these points are part of their "Core Framework Components", it is highly underspecified and I find it hard to see how it would lead to the relevant RFP-related outcomes.

  • Expert Review 2

    Overall

    4.0

    • Compliance with RFP requirements 5.0
    • Solution details and team expertise 5.0
    • Value for money 5.0
    The proposal is to develop Coward's Recommendation model as a motivational front end for HYperon

    I would give this 4.5 stars if I could. It's a very very strong proposal. I would like to see this work done and I think the Simuli team are the right folks to do it. My reservation is that it's really about human level motivations and for Hyperon we want to go beyond the human level too, and this approach probably falls apart when one gets to strong self-modification... or at least it's not clear to me why it doesn't. But still a nice solid cognitively and biologically inspired and practical approach to motivation for the. merely human level phase of. Hyperon dev is quite interesting.

  • Expert Review 3

    Overall

    5.0

    • Compliance with RFP requirements 5.0
    • Solution details and team expertise 5.0
    • Value for money 5.0

    Very comprehensive and modular biologically inspired framework that focuses on efficiency. Though it could incorporate psychological theories such as those currently being considered in Hyperon, it is not limited to such ideas but in a sense forms an underlying structure for such systems.

  • Expert Review 4

    Overall

    5.0

    • Compliance with RFP requirements 5.0
    • Solution details and team expertise 0.0
    • Value for money 5.0
    Million-dollar value brilliant biologic approach !

    It's so easy to review a brilliant proposal! The biological plausibility is what gives me confidence - as also proven successful by the titanic works of Stephen Grossberg, inventor of the Adaptive Resonance Theory. From all that I have seen in the submissions - if I can only fund one proposal, this would be it! ... This is a million-dollar value - for the super-modest 30 K investment requested.

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