Multi-agent based knowledge graph learning path planning method

Abstract

The application discloses a knowledge graph learning path planning method based on multiple agents, and belongs to the technical field of intelligent optimization. In order to solve the problem that planning constraint conflicts are prone to occur when multiple agents plan learning paths on a knowledge graph, and it is difficult to reach an agreed path in real time, the application encodes the knowledge graph through a graph transformer, generates a candidate learning path by using centralized training and decentralized execution of multiple agent reinforcement learning, constructs an incremental satisfiability judgment problem for conflict detection and outputs an unsatisfiable core and a conflict type, performs consistency arbitration update based on a segmented consensus variable driven by a conflict kernel by using an alternating direction multiplier method, and adjusts a penalty parameter according to an original residual error, a dual residual error and a conflict type. Meanwhile, the application learns and compresses transmission of the consensus variable update amount, and controls the number of rounds, so that the technical effects of outputting a consistent learning path satisfying constraints, reducing the number of iteration rounds and communication overhead, and improving real-time performance are achieved.

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