API Reference object v#0.1.2

Meta-schema system enabling objects to reason about and modify their own schemas. Implements hierarchical schema inheritance with runtime evolution capabilities.

Object-Oriented Reinforcement Learning Framework

Collective learning implementation for OORL framework enabling emergent group intelligence through distributed coordination and knowledge sharing.

Distributed learning across object coalitions

Monte Carlo Graph Search implementation with Graph Attention and Contrastive Learning.

Graph node structure for MCGS

Monte Carlo Tree Search implementation for OORL framework with Q* optimal policy enhancement.

MCTS tree node structure

Learning to learn: adaptation of learning strategies themselves

Policy learning implementation for OORL framework with social and collective learning.

Individual policy learning with social awareness based on AAOS interaction dyads

Provides protocol verification capabilities including LTS (Labeled Transition System) verification, reachability analysis, and protocol property checking.

OORL Reward Learning module implementing mathematical reward combination algorithms as specified in AAOS Section 6.

Object-Oriented Reinforcement Learning (OORL) Object implementation based on Autonomous Agent Object Specification (AAOS).

Advanced AI reasoning capabilities for AAOS objects using DSPy framework. Provides pre-built signatures for common object behaviors and interactions.

Advanced cryptographic capabilities including post-quantum cryptography, zero-knowledge proofs, and homomorphic encryption for AAOS.

Advanced agent monitoring system that tracks agent behavior, performance, and coordination patterns across the distributed object system.

Main OTP Application for the AAOS Object system.

Byzantine fault tolerance mechanisms for the Object P2P network.

Distributed coordination service for multi-object operations. Uses consensus algorithms and conflict resolution for large-scale coordination.

Bridge module integrating DSPy framework with AAOS objects for advanced reasoning capabilities. Enables objects to use DSPy signatures and LM Studio inference for intelligent behavior.

Mock LM Studio client for testing and development.

High-performance demonstration runner using OTP principles. Shows the full AAOS system in action with proper BEAM/OTP patterns.

Distributed registry for Objects using Kademlia DHT algorithm.

Distributed Low-Communication (DiLoCo) Training implementation for AAOS.

End-to-end encryption for Object network communication.

Object-Oriented Exploration strategies for OORL framework.

LLM-powered function calling system for Object self-organization.

Hierarchical Object Composition and Decomposition for OORL framework.

Defines and manages interaction patterns between Objects for self-organization.

Enhanced LLM integration for AAOS objects using DSPy.

Object-Oriented Reinforcement Learning (OORL) Mailbox implementation based on AAOS Section 4 - Object Interactions.

GenStage consumer for processing messages from the MessageRouter. Handles actual message delivery to target objects.

High-performance message routing service using GenStage for backpressure. Handles message delivery between objects with priority queuing and load balancing.

Supervisor for the message routing system with multiple consumers.

Self-Reflective Meta-DSL implementation based on AAOS specification.

NAT traversal mechanisms for P2P Object communication.

Coordinates network operations across all network components.

Binary protocol for efficient Object communication over network.

Network proxy for transparent remote Object access.

Supervisor for all network-related components of the Object system.

Network transport layer abstraction for Object communication.

Advanced neuro-symbolic reasoning engine for AAOS objects.

OpenAI API client implementation for AAOS. Implements the same interface as MockLMStudio for drop-in replacement.

Self-scaffolding object that consumes OpenAI OpenAPI specification and generates an autonomous agent with full API capabilities.

P2P bootstrap and discovery service for the Object network.

Real-time performance monitoring and metrics collection for the AAOS system. Uses telemetry and ETS for high-performance metrics aggregation.

Quantum-inspired algorithms for AAOS optimization and computation.

Real-time entanglement correlation engine using GenServer.

Real-time quantum entanglement simulation with WebRTC-style hooks for Elixir/OTP.

Complex number representation for quantum amplitudes

Two-qubit entangled quantum state

Single qubit quantum state representation

Quantum measurement simulation with probabilistic outcomes and real-time correlation.

Dynamic self-evaluation system with visual reinforcement for quantum simulations.

Manages system resources including memory, CPU, network, and storage limits. Provides graceful degradation and emergency shutdown capabilities.

A more robust network transport layer that handles startup failures gracefully.

Manages schema evolution and self-modification across the object system. Implements distributed consensus for schema changes and evolution tracking.

ETS-based registry for tracking object schemas and evolution. Provides fast lookup and atomic updates for the object schema space.

Demonstration of the complete self-organizing Object system.

Serialization and deserialization for Object instances.

GenServer implementation for individual AAOS objects.

Advanced Byzantine fault-tolerant consensus with sharding for AAOS.

Stream emitter that generates ideas with configurable rate and quality. Respects backpressure from connected processors.

Stream processor with backpressure control for ideation and data flow.

Object subtypes implementation based on AAOS specification. Defines specialized object types: AI Agents, Human Clients, Sensor Objects, Actuator Objects, and Coordinator Objects.

Autonomous AI Agent with advanced learning and reasoning capabilities. Implements full OORL capabilities including meta-learning and self-modification.

Object for environmental manipulation and action execution.

Coordination and orchestration object for multi-agent systems.

Human client interface with natural language processing and preference learning. Handles human-AI interaction patterns and learns user preferences.

Specialized object for environmental sensing and data collection.

Enhanced supervision tree for the AAOS Object system with comprehensive fault tolerance.

Demonstration of the comprehensive Object system with mailboxes and subtypes based on the AAOS specification.

Self-organizing system orchestrator that manages the entire Object ecosystem.

Object-Oriented Transfer Learning mechanisms for OORL framework.

Manages trust relationships between objects, including reputation tracking, Byzantine fault detection, and trust-based decision making.

DSPy-powered comprehensive test failure analysis and diagnosis system. Uses GPT-4.1-mini through DSPy signatures to analyze and provide solutions for test failures.