LakeML is a research and prototype project that aims to capture the dynamic interplay of ideas among multiple AI agents. Inspired by the metaphor of a "Lake of Possibility," LakeML envisions a computational environment where simple, specialized agents interact over a shared context—much like ripples on a lake’s surface. Through their interactions, these agents collaboratively generate, verify, and refine hypotheses until emergent, collectively endorsed solutions arise.
Traditional AI systems are often built as isolated monoliths with little cross-communication. In contrast, LakeML seeks to create a system where:
- Emergent Behavior: Local interactions among agents (e.g., generating, verifying, and synthesizing proposals) lead to global, emergent solutions.
- Collaborative Intelligence: By sharing context and feedback, agents “converse” and collectively build knowledge—mirroring human collaborative decision-making.
- Dynamic Contextualization: Like droplets creating ripples on a lake, every agent’s input modifies the shared state, leading to continuous refinement and self-correction.
LakeML draws from theories in swarm intelligence, multi-agent reinforcement learning, and collaborative intelligence. Our goal is to build a platform that not only solves problems but does so by simulating a dynamic dialogue among diverse, purpose-driven agents.
The primary objectives of LakeML include:
- Emergent Decision Making: Enable agents to collaboratively converge on refined solutions through iterative interactions.
- Adaptive Learning: Incorporate reinforcement learning and rule-based logic so agents can adjust their behavior based on feedback.
- Scalable Collaboration: Develop a robust and extensible shared context and communication protocol that can eventually support many agents and complex tasks.
- Bridging Theory and Practice: Provide a research platform to explore and demonstrate how emergent behaviors can be harnessed for collaborative AI.
LakeML is built around several core components:
- Agent Modules:
- Generator Agents produce initial hypotheses (proposals).
- Verifier Agents assess these proposals and provide feedback (verification or rejection).
- Synthesizer Agents combine existing proposals into meta-hypotheses.
- RL-Enhanced Generator Agents utilize Q-learning to refine proposal strategies over time.
- Shared Context:
A central "blackboard" (initially a Python dictionary) that aggregates all messages exchanged between agents. - Communication Protocol:
Structured JSON-like messages carry information such as message type, content, priority, and references to related messages. - Iteration Controller:
An asynchronous loop (using Python’sasyncio) that drives the system in discrete time steps, enabling iterative feedback and emergent dynamics. - Metrics Collector:
Tools to monitor key performance indicators (e.g., proposal counts, verification rates) and help visualize the system’s behavior over time.
The repository is organized as follows:
- README.md: Project overview and setup instructions.
- requirements.txt: (Currently empty, as we use only Python standard libraries; add packages as needed.)
- main.py: Entry point for running the asynchronous simulation loop.
- agents.py: Contains definitions for all agent types (basic Generator, Verifier, Synthesizer, and RL-enhanced Generator).
- metrics.py: Implements a metrics collector for tracking system performance.
- Python 3.7 or higher
- (Optional) A virtual environment for dependency management
- Clone the Repository:
git clone <repository-url> cd LakeML