A cluster game collaborative cognition method and system based on human-machine hybrid intelligence

By constructing a unified human-machine confidence representation system and knowledge graph reasoning path backtracking, the problem of heterogeneous incompatibility of human-machine confidence is solved, the consistency of cluster cognition and the reliability of decision-making are improved, and efficient collaboration is adapted to complex human-machine hybrid cluster game scenarios.

CN122242768APending Publication Date: 2026-06-19DONGHUA UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
DONGHUA UNIV
Filing Date
2026-04-28
Publication Date
2026-06-19

AI Technical Summary

⚠Technical Problem

Existing technologies have failed to effectively address the heterogeneity of confidence representation paradigms between humans and machine intelligent agents in human-machine hybrid scenarios. This leads to continuous distortion of cognitive information during cross-agent transmission in the distributed reasoning process of the cluster, making it impossible to form a stable and consistent global collaborative cognition. Consequently, it is difficult to meet the high-reliability collaborative operation requirements of human-machine hybrid clusters in complex game scenarios.

⚗Method used

By collecting quantitative and qualitative confidence data of human-machine game knowledge, a unified confidence representation result is established, and confidence decay detection and inference compensation are performed on each collaborative cognitive node in the cluster. Correction is made by backtracking the reasoning path of knowledge graph and matching prior knowledge. Combined with the improved Raft consensus protocol and Nash equilibrium solution to generate collaborative strategies, the fusion and closed-loop optimization of human-machine instructions are realized.

🎯Benefits of technology

It improves the consistency of cluster cognition and the reliability of decision-making, blocks the cascading decay of confidence, ensures the real-time nature of collaboration and scenario robustness, and is suitable for various complex human-machine hybrid cluster game scenarios.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention discloses a collaborative cognitive method and system for cluster game based on human-machine hybrid intelligence, relating to the field of knowledge reasoning technology. The method includes: step 100, collecting confidence data corresponding to human-machine game knowledge to obtain quantitative confidence data and qualitative confidence data; step 200, constructing a unified confidence representation result based on the quantitative and qualitative confidence data; step 300, performing confidence decay detection and inference compensation at cluster collaborative cognitive nodes based on the unified confidence representation result to obtain calibrated confidence data; and step 400, performing cluster game cognitive consensus fusion based on the calibrated confidence data to obtain a global game cognitive result. This invention, by constructing a unified human-machine confidence representation system, can achieve a closed-loop human-machine collaborative cognition, balancing real-time collaboration with scenario robustness, and adapting to various complex cluster game scenarios.
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