Memoryless Adversaries in Imperfect Information Games

Dhananjay Raju (The University of Texas at Austin), Georgios Bakirtzis (The University of Texas at Austin), Ufuk Topcu (The University of Texas at Austin)

Abstract

Given an agent with limited sensing capabilities, we analyze whether it is possible to deploy a new agent in the operational space of the preexisting agent in a safe manner. One approach for modeling the interaction of the introduced agent with its environment, which contains the preexisting agent, is through a two-player game of imperfect information. However, the computational cost of solving this game is prohibitive. Restricting the preexisting agent's strategy to just memoryless strategies and assuming that the introduced agent has perfect information alleviates the computational cost while still modeling realistic environments. The proposed algorithm for solving the game finds a winning strategy for the introduced agent by solving a quantified Boolean formula (QBF) for the game. We justify this approach by establishing a matching PSPACE lower bound. We also show that this result holds even when the preexisting agent uses bounded history to condition its play.