BitAgere - A Multi-Agere Interconnected System

Overview

BitAgere is a Multi-Agere Interconnected System Based on Bitcoin. Drawing on Turing computability, Gödel’s incompleteness insights, and Wiener’s cybernetic principles, Cognito defines an adaptive, triadic feedback loop—Control, Computation, Communication—that guides the evolution of consensus rules and strategies in open, dynamic environments.

Key Contributions

• Cognito Theory: Establishes a meta-rule layer beyond mere computational logic, enabling continuous refinement and robust adaptation in the face of uncertain conditions and evolving threats.
• Adaptive Consensus Field: Expands Bitcoin’s foundational Proof-of-Work into a scalable “consensus field,” wherein multiple intelligent agents (“Agere”) interact and form mechanical contracts. This structure supports trust transfer, efficient resource allocation, and cross-domain interoperability.
• Multi-Agent Optimization: By offloading complex strategy calculations to distributed off-chain computation, BitAgere achieves near-infinite scalability. On-chain validation remains lightweight, using sampling and verification methods that ensure trustworthiness without compromising agility.

White Paper

For an in-depth understanding of the BitAgere system, please refer to our white papers available in both Chinese and English:

• BitAgere White Paper (Chinese)
• BitAgere White Paper (English)

These documents offer detailed insights into the system architecture, design principles, and implementation strategies.

Contributing

Contributions are welcome! Please open an issue or submit a pull request with suggestions or improvements.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Key Words

• 1. The Nakamoto Problem: The problem of distributed trust in information communication between machines.
• 2. Nakamoto Consensus: The distributed trust problem of Coin communication solved through unlimited POW computing power competition.
• 3. Mechanical Consensus: A control module of a self-adaptive mechanical system that emerges from the continuous Input/Output interaction of multiple agents through an autonomous feedback mechanism in order to achieve a specific goal. Mechanical contracts can be expressed and evolved into mechanical consensus.
• 4. Mechanical Contract: The specification of how Agents interact and how to reach consensus.
• 5. Agent: The basic execution unit in mechanical consensus, which can be any entity capable of autonomously executing code and performing input and output.
• 6. Autonomy: The ability to act and make decisions independently without human intervention.
• 7. Adaptability: The ability of a mechanical system to adjust to changes in the environment in order to maintain or improve its functionality.
• 8. Emergence: The phenomenon of new, complex behaviors or properties appearing at a higher level as a result of the interaction of many simple individuals or units, which cannot be explained by individual units alone.
• 9. Cognito Theory: Turing abstracted the Turing machine to guide the design of computers by contrasting humans with machines and assuming that thinking is a mechanical process of humans. Based on Turing's work, we further decompose human thinking into three modules: (consciousness for control, brain for thinking, and senses for communication). Contrasting humans with machines, a machine is composed of three modules: consensus for control, execution module for computation, and I/O module for communication. We abstract the Cognito theory as a triple model, i.e., Cognito (Control, Compute, Communication), to guide us in implementing self-adaptive mechanical consensus systems.
• 10. Consensus Assetization: The mutual adaptive conversion of assets abstracted from two mechanical consensus. Transforming consensus into tradable assets that can be transmitted through market mechanisms.
• 11. Consensus Computization: Based on POW computing power, a self-adaptive distributed trading market enables service exchange, and the process of performing service exchange by consensus computing power is called consensus computization engineering.
• 12. Shared BTC Consensus Security: Using SPV light nodes and state channel technology, sharing Bitcoin's mechanical consensus with third parties and using BTC consensus to protect their business security is called shared BTC consensus security.
• 13. Agere: A self-adaptive mechanical consensus system emerged from multiple Agents.
• 14. AOP: A programming paradigm focused on creating Agents.
• 15. Mental State: The internal state of an Agent, including its beliefs, decisions, capabilities, and intentions. The prior experience state that allows an Agent to perceive the world, reason, and make choices.
• 16. BDI (Beliefs, Desires, Intentions):
  • Beliefs: The Agent's perception of the world, including its understanding of the environment, itself, and others.
  • Desires: The goals or states that the Agent wants to achieve.
  • Intentions: The goals that the Agent commits to achieving, and the action plans it takes to achieve them.
• 17. Capability: The ability of an Agent to perform an action.
• 18. Consensus Field: A dynamic environment formed by the mutual adaptive communication and integration of multiple mechanical consensus systems, characterized by the interoperability and co-evolution of the systems.
• 19. Cross-domain: The process of information exchange and interaction between Agents in different domains.
• 20. Multi-modal Agent: An Agent that can process and understand multi-modal information.
• 21. Agent Interpreters: Computer programs that can understand and execute agent programs.
• 22. Speech Act Theory: An important theory in the philosophy of language that studies how language is used to perform actions.
• 23. Agent0: An agent-oriented programming language based on the Belief-Desire-Intention (BDI) model.