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Title: Anticipating Oblivious Opponents in Stochastic Games
We present an approach for systematically anticipating the actions and policies employed by oblivious environments in concurrent stochastic games, while maximizing a reward function. Our main contribution lies in the synthesis of a finite information state machine (ISM) whose alphabet ranges over the actions of the environment. Each state of the ISM is mapped to a belief state about the policy used by the environment. We introduce a notion of consistency that guarantees that the belief states tracked by the ISM stays within a fixed distance of the precise belief state obtained by knowledge of the full history. We provide methods for checking consistency of an automaton and a synthesis approach which, upon successful termination, yields an ISM. We construct a Markov Decision Process (MDP) that serves as the starting point for computing optimal policies for maximizing a reward function defined over plays. We present an experimental evaluation over benchmark examples including human activity data for tasks such as cataract surgery and furniture assembly, wherein our approach successfully anticipates the policies and actions of the environment in order to maximize the reward.  more » « less
Award ID(s):
2146563
PAR ID:
10575682
Author(s) / Creator(s):
; ;
Editor(s):
Amato, Nancy; Driggs-Campbell, Katie; Ekenna, Chinwe; Morales, Marco; O'Kane, Jason
Publisher / Repository:
Springer Proceedings in Advanced Robotics (SPAR)
Date Published:
Format(s):
Medium: X
Location:
Chicago, USA
Sponsoring Org:
National Science Foundation
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