GenCOA generates a diverse pool of multi-agent mission plans by splitting task allocation (genetic algorithm) from task sequencing (graph reinforcement learning), reaching about 96% of the optimal completion rate.
A novel nonlinear spin wave theory for the spin 1/2 antiferromagnetic Heisenberg model on a triangular lattice
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abstract
We extend the nonlinear spin wave theory (NLSWT) for the spin 1/2 antiferromagnetic Heisenberg model on a triangular lattice (TAFHM). This novel NLSWT considers the corrections one order higher in 1/S than the linear spin wave theory (LSWT). It also distinguishes in which circumstance the negative energy excitation, the sign of the breakdown of LSWT, shall be renormalized to be positive both by a boson normal ordering and a self-consistent iteration. We draw a phase diagram by testing the stability of various magnetic orders for different parameters. In particular, the incommensurate configuration is found unstable by our study. The new phase transition point (PTP) of the collinear configuration agrees well with various previous studies.
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Automated Generation of Diverse Courses of Actions for Multi-Agent Operations using Binary Optimization and Graph Learning
GenCOA generates a diverse pool of multi-agent mission plans by splitting task allocation (genetic algorithm) from task sequencing (graph reinforcement learning), reaching about 96% of the optimal completion rate.