支援型マルチアームバンディットにおけるリスク回避的な人間バイアスのモデル化
Risk-Averse Biased Human Policies in Assistive Multi-Armed Bandit Settings
家庭用サービスロボットとの協働を想定した支援型マルチアームバンディット問題で、累積プロスペクト理論に基づく人間のリスク回避バイアスを観測可能報酬で扱い、チームの効用を高めるアルゴリズムを提案した。
著者: Michael Koller, Timothy Patten, Markus Vincze
分類: cs.RO
原文アブストラクト
Assistive multi-armed bandit problems can be used to model team situations between a human and an autonomous system like a domestic service robot. To account for human biases such as the risk-aversion described in the Cumulative Prospect Theory, the setting is expanded to using observable rewards. When robots leverage knowledge about the risk-averse human model they eliminate the bias and make more rational choices. We present an algorithm that increases the utility value of such human-robot teams. A brief evaluation indicates that arbitrary reward functions can be handled.
