SophiaVerse Language Action Model

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Expert Rating 3.6
AIAgentBrown
Project Owner

SophiaVerse Language Action Model

Expert Rating

3.6

Overview

A groundbreaking application that integrates LLMs with MeTTa's symbolic reasoning via metta-motto, enabling dynamic, context-aware, and reasoning-driven language interactions for virtual agents in SophiaVerse.

RFP Guidelines

Develop interesting demos in MeTTa

Complete & Awarded
  • Type SingularityNET RFP
  • Total RFP Funding $100,000 USD
  • Proposals 21
  • Awarded Projects 4
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SingularityNET
Aug. 12, 2024

Create educational and/or useful demos using SingularityNET's own MeTTa programming language. This RFP aims at bringing more community adoption of MeTTa and engagement within our ecosystem, and to demonstrate and expand the utility of MeTTa. Researchers must maintain demos for a minimum of one year.

Proposal Description

Project details

The NeotericOS Language Action Model is a groundbreaking approach to virtual agent interaction that bridges symbolic reasoning and generative AI through the MeTTa-Motto integration. Unlike traditional conversational AI, this model provides a multi-layered system for virtual agents to:

  1. Understand and Interpret Complex Commands
    • Transform natural language commands into executable symbolic representations
    • Use MeTTa's knowledge graph to contextualize and expand user intentions
    • Enable multi-step goal decomposition with transparent reasoning
  2. Dynamic Action Planning
    • Generate executable action plans based on symbolic constraints
    • Maintain a dynamic knowledge base of agent capabilities, context, and learned skills
    • Support hierarchical goal achievement with justifiable reasoning chains
  3. Contextual Learning and Adaptation
    • Incrementally update agent knowledge through interactions
    • Learn and store new skills, social protocols, and contextual understanding
    • Provide transparent mechanisms for skill acquisition and modification

Open Source Licensing

GNU GPL - GNU General Public 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

    3

  • Total Budget

    $25,000 USD

  • Last Updated

    8 Dec 2024

Milestone 1 - Symbolic Command Parsing Framework

Description

Develop MeTTa-based natural language to symbolic action translator Implement initial goal decomposition algorithms Create base knowledge representation for agent capabilities Demonstrate basic command understanding and transformation

Deliverables

First prototype of a query-to-action translator

Budget

$10,000 USD

Success Criterion

Working first prototype of a query-to-action translator

Milestone 2 - Dynamic Action Planning System

Description

Implement hierarchical goal achievement mechanism Develop reasoning trace generation for action plans Build initial integration with MeTTa-Motto for LLM interaction

Deliverables

More complex scenario prototype with MeTTa-Motto

Budget

$7,500 USD

Success Criterion

Complex scenario pass

Milestone 3 - Social Interaction Protocol

Description

Design agent-to-agent and agent-to-human interaction models Develop multi-agent communication and negotiation mechanisms Create demonstration scenarios showcasing nuanced interactions

Deliverables

Multi-agent communication mechanisms

Budget

$7,500 USD

Success Criterion

Successful multi-agent task completion

Join the Discussion (0)

Expert Ratings

Reviews & Ratings

Group Expert Rating (Final)

Overall

3.6

  • Feasibility 4.7
  • Desirabilty 3.3
  • Usefulness 3.3

While experts rated this submission highly, ultimately we selected another proposal for strategic reasons.

  • Expert Review 1

    Overall

    2.0

    • Compliance with RFP requirements 4.0
    • Solution details and team expertise 2.0
    • Value for money 2.0
    Need more information

    While proposal aligns conceptually with the RFP, the approach is vague. Needs significantly more specificity to evaluate its viability.

  • Expert Review 2

    Overall

    4.0

    • Compliance with RFP requirements 5.0
    • Solution details and team expertise 3.0
    • Value for money 4.0

  • Expert Review 3

    Overall

    5.0

    • Compliance with RFP requirements 5.0
    • Solution details and team expertise 5.0
    • Value for money 4.0
    It seems reasonable to use semantic parsing to turn commands into plans which are then represented in MeTTa and then executed to drive actions in SV or other virtual worlds

    Doing this with full awesome functionality is a big project but doing it in a simple way seems a good integration demo involving LLM hyperon and virtual world

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