Deceptive Opponent Modeling with Proactive Network Interdiction for Stochastic Goal Recognition Control

Junren Luo, Wei Gao, Zhiyong Liao, Weilin Yuan, Wanpeng Zhang, Shaofei Chen

Abstract:

Goal recognition based on the observations of the behaviors collected online has been used to model some potential applications. Newly formulated problem of goal recognition design aims at facilitating the online goal recognition process by performing offline redesign of the underlying environment with hard action removal.
In this paper, we propose the stochastic goal recognition control (S-GRC) problem with two main stages: (1) deceptive opponent modeling based on maximum entropy regularized Markov decision processes (MDPs) and (2) goal recognition control under proactively static interdiction.
For the purpose of evaluation, we propose to use the worst case distinctiveness (wcd) as a measure of the non-distinctive path without revealing the true goals, the task of S-GRC is to interdict a set of actions that improve or reduce the wcd.
We empirically demonstrate that our proposed approach control the goal recognition process based on opponent's deceptive behavior.