Accounting For Observer'S Partial Observability In Stochastic Goal Recognition Design

Motivated by security applications, where agent intentions are unknown, actions may have stochastic outcomes, and an observer may have an obfuscated view due to low sensor resolution, we introduce partially-observable states and unobservable actions into a stochastic goal recognition design framework. The proposed model is accompanied by a method for calculating the expected maximal number of steps before the goal of an agent is revealed and a new sensor refinement modification that can be applied to enhance goal recognition. A preliminary empirical evaluation on a range of benchmark applications shows the effectiveness of our approach.