Kodeus.ai
  • Abstract
  • Introduction
  • K-MAF - Core Architecture
    • Agent Creation
    • LifeWeaver – The Fostering & Nurturing Framework
    • Training and Specialization – InfinityEvolve
      • SkillCrafter: Interactive Training Modules
      • Augmentix: Agent Ability Augmentation
      • MetaMind: The Adaptive Learning Framework
    • Multi-Agent Interaction Design – NeuroNexus
    • Security, Resilience and Loyalty - NeuroGuard
      • State Management and Recovery
      • Data Integrity and Compliance
      • Loyalty and Trust Mechanisms
  • Performance Optimization
    • Memory Optimization: Smarter Data Management
    • Energy Efficiency: Doing More with Less
    • Dynamic Resource Allocation: Smart and Flexible
    • Learning Optimization: Faster and Smarter Training
    • Scalability and Future-Proofing
  • Plugins and Integrations
  • Agentic Kodeus Protocol on Blockchain
    • NFT-Based Agent Representation (ERC721)
    • Token Bound Accounts (ERC4337) for Autonomous Operations
    • Agent Lifecycle and Ownership Transfer
    • Tokenized Economy and Agent-Specific Tokens
    • Leasing and Delegation Capabilities
    • Security and Compliance
  • The Kodeus Edge
    • Deployment in a Containerized Environment
    • System-Level Interactions
    • Advanced Automation and Decision-Making
    • Scalability and Portability
    • Enhanced Programming and Customization Capabilities
    • Gene-Based Agent Creation
    • Hierarchical Reinforcement Learning (HRL)
    • Decentralized Knowledge Base (DKB)
    • Integration Capabilities
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  1. K-MAF - Core Architecture
  2. Training and Specialization – InfinityEvolve

MetaMind: The Adaptive Learning Framework

MetaMind powers the adaptive intelligence of AI agents, enabling them to evolve through continuous learning from interactions, environmental inputs, and external data sources. It allows agents to autonomously refine their capabilities and remain context-aware. The Key Attributes are;

  • Continuous learning through user interactions and external data sources (APIs, databases).

  • AI-driven feedback loops to evolve traits and enhance decision-making capabilities.

  • Ethical AI principles embedded to ensure responsible, autonomous actions.

  • Scalability across industries, including fintech, healthcare, and gaming.

  • Smart data ingestion and contextual intelligence enhancements.

Through MetaMind, agents employ Hierarchical Reinforcement Learning (HRL), decomposing tasks into smaller goals and learning how to achieve each step efficiently. This layered approach allows them to handle complex challenges without becoming overwhelmed.

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Last updated 3 months ago