A knowledge-driven layered inverse reinforcement learning approach for recognizing human intents
Rupam Bhattacharyya, Shyamanta Moni Hazarika · Journal of Experimental & Theoretical Artificial Intelligence · 2020
There is a rising trend in exploring the capability of inverse reinforcement learning (IRL) in high dimensional demonstrations. Our aim is to recognise human intents from video data within an IRL framework. For this, we present a two-layered maximum likelihood IRL model. The usefulness of knowledge representation (KR) schemes and availability of advisors at different layers is exploited through this model. Two main aspects are addressed: a. the importance of having abstract high-level information to the IRL framework in terms of semantic object affordance and b. deductively exploring the utility of a state at different temporal abstractions. The effectiveness of the proposed model has been evaluated with the help of standard Cornell Activity Dataset (CAD-120).