Risk Proneness Estimation Method Developed in Relation to the Decision Taker that Controls the Robotic System
This addresses the problem of understanding operator behavior in uncertain situations like emergencies or military actions, but appears incremental as it combines existing models without clear performance gains.
The paper tackles the problem of estimating a robotic system operator's risk proneness by observing their control decisions, using the Hurwitz pessimism/optimism criterion and decision trees as base models. The result is a method that estimates the operator's pessimism/optimism parameter through a reverse setting approach.
This work suggests the estimation method developed in relation to the position of the robotic system (RS) operator, showing his degree of risk proneness. The base models are: Hurwitz pessimism/optimism criterion and decision trees. The problem is solved using the reverse setting: we estimate pessimism/optimism parameter of the operator (decision taker) by observing what decisions he makes when controlling the RS. The solution context of such decision taker position estimation problems can be: using RS in emergency situations, in military actions and other situations connected with the uncertainty of the situation.