Risk Proneness Estimation Method Developed in Relation to the Decision Taker that Controls the Robotic System

TL;DR

This paper introduces a method to estimate a robotic system operator's risk proneness by analyzing their decision-making behavior using decision trees and Hurwitz criteria, especially in uncertain or emergency scenarios.

Contribution

It presents a novel approach to infer an operator's risk attitude from observed decisions, aiding in adaptive control of robotic systems under uncertainty.

Findings

Effective estimation of risk proneness from decision data

Applicable in emergency and military robotic operations

Utilizes decision trees and Hurwitz criteria for analysis

Abstract

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.