Artificial Intelligence: Adaptive Certainty Propagation in Distributed Cognitive Systems

• Shiva Kumar Bommakanti

  Author

Distributed artificial intelligence (AI) systems require protocols with scalability and fault tolerance for making decisions asynchronously over networks. Through the adaptive propagation of a certain strength and the recursive structure of consensus summits, a new consensus protocol is proposed in this paper, which is robust and flexible. The proposed framework allows agents to reach agreement with various confidence levels rather than using binary commitments like in traditional approaches. Moreover, these confidence levels are based on validator reliability and consistency. By utilizing a Directed Acyclic Graph (DAG), the flow of information can be organized effectively. This allows for increased scaling and a reduction in the costs of communication. Furthermore, aggregate summit proof validation can be done efficiently as the networks get enhanced. With iteration of the recursive summit mechanism, more trust gets established in the system's decision, and it becomes robust to noisy observations, network latencies, and malicious behavior among parties. The protocol performs its task asynchronously and does not require the existence of a global clock. Moreover, the protocol achieves Byzantine fault tolerance in the presence of up to one-third malicious validators. The adaptive confidence model enhances collective decision-making across heterogeneous AI ecosystems, making the framework suitable for decentralized machine learning platforms and large language model deployments. The method achieves the scalable verification of finality for strong consensus. These attributes make the protocol a potential solution for next-generation distributed artificial intelligence systems.

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