Human-Machine Function Allocation Method Based on a Non-Cooperative Game for the Manned Submersible

Abstract

The human-machine function allocation (HFA) strategy of manned submersibles is an important factor that affects the reliability of oceanauts. However, the uncertainty of the HFA strategy makes its optimization very complicated. To this end, a non-cooperative game-based HFA method is proposed, which transforms the multi-objective optimization model of mental workload and situation awareness (SA) under the allocation strategy into a non-cooperative game model and forms a mapping relationship. Mental workload and situation awareness are used as non-cooperative game players. Assignable functions are attributed to the players by fuzzy clustering analysis and combined with non-assignable functions to construct the utility matrix by utility functions. The optimal allocation strategy combination is obtained through the Nash equilibrium analysis of the utility matrix. The proposed method was applied to the optimization of the HFA strategy of the manned submersible during the navigation.

Keywords

• Human-machine function allocation (HFA)
• mental workload
• situation awareness (SA)
• SAGAT
• VACP