ROADMAP.md

🚀 AEI Framework – ROADMAP

Autonomous & Evolutive Intelligence Framework (AEIF)

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Phase 0 — Foundations & Project Specs

• Initialize the repository:
  • Rust workspace
  • Modular crates structure
  • CI/CD for build & test
• Documentation:
  • README.md (vision, goals, quickstart)
  • GLOSSARY.md or section

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Phase 1 — Core Architecture & Specifications

• Define core traits and interfaces:
  • NeuralNetwork: minimal interface for any network
  • KnowledgeModule: standard interface for attachable skills
  • MemoryStore: abstract storage backend
• Module skeletons: minimal code for each crate with clear responsibilities
• Doc comments: each interface documented (Rust style)

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Phase 2 — Minimal Neural Network Engine

• crates/core/nn module:
  • Neuron, Layer, Network structs
  • Forward propagation (sigmoid/ReLU)
  • Pluggable layer structure for extension
• API design:
  • Instantiate networks with arbitrary topology
  • Forward function: input → output
• Examples:
  • Simple usage in examples/ (e.g., XOR, linear regression)

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Phase 3 — Persistence & Memory Abstraction

• memory module:
  • MemoryStore trait: put/get/save/load pattern
  • In-memory backend (HashMap)
  • File-based backend (JSON/YAML/TOML)
• Serialization:
  • Save and restore neural network weights/config
• Demo:
  • Store/load agent state and support checkpointing

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Phase 4 — Knowledge Modules & Semantic Memory

• crates/modules:
  • Define KnowledgeModule trait (versioned, attach/detach, identify)
  • Basic modules: e.g., "math", "echo", "counter"
• Semantic memory:
  • Structure knowledge as graphs or embeddings
  • Persistence hooks for long-term storage
• API & documentation:
  • How to build/extend modules and feed semantic memory

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Phase 5 — NLP Engine

• Text processing pipeline:
  • Tokenization, stemming, basic parsing
  • Embedding generation for integration with semantic memory
• Language understanding modules:
  • Intent recognition
  • Entity extraction linked to knowledge base
• Examples:
  • Simple chatbot demo showcasing NLP capabilities

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Phase 6 — Runtime & Agent Orchestration

• crates/runtime:
  • Agent scheduler (single & multi-agent)
  • Event loop or tick-based system
• Interactions:
  • Agent-to-agent communication (message/event bus)
  • Dynamic module orchestration
• Examples:
  • Multi-agent scenario in examples/ (agents greeting, exchanging info)

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Phase 7 — Learning, Adaptation & Reasoning

• Learning loop:
  • Backpropagation for simple networks
  • Incremental training API
• Reasoning abilities:
  • Rule-based or heuristic reasoning over semantic memory
  • Simple planning/decision modules
• Metrics & evaluation:
  • Track performance and learning progress

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Phase 8 — Tests, Documentation, and Demos

• Unit & integration tests:
  • Coverage for core crates and modules
• Comprehensive documentation:
  • Guides, API docs, tutorials
• Showcase demos:
  • In examples/: create, train, persist, and interact with agents

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Phase 9 — Community, Extensibility & Release

• Open source onboarding:
  • Issues & milestones on GitHub
  • Clear contributor guidelines
• Extensibility:
  • Plugin system and extension templates
  • FFI hooks for other languages
• First release (v0.1):
  • API stabilization
  • Publish on crates.io and announce to the community

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Stretch Goals

• Web or CLI interface for agent management
• Support for alternative neural architectures (RNN, CNN)
• Persistence in cloud backends
• Interfacing with real-world data (APIs, web scraping)
• Integration with other languages (Python bindings, etc.)

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How to contribute

• Discuss new features via GitHub issues
• Propose your own modules or skills
• Submit PRs with tests and documentation

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This roadmap is iterative and may evolve based on community feedback and project progress.