A Logical Hierarchical Hidden Semi-Markov Model for team intention recognition

Abstract

Intention recognition is significant in many applications. In this paper, we focus on team intention recognition, which identifies the intention of each team member and the team working mode. To model the team intention as well as the world state and observation, we propose a Logical Hierarchical Hidden Semi-Markov Model (LHHSMM), which has advantages of conducting statistical relational learning and can present a complex mission hierarchically. Additionally, the LHHSMM explicitly models the duration of team working mode, the intention termination, and relations between the world state and observation. A Logical Particle Filter (LPF) algorithm is also designed to infer team intentions modeled by the LHHSMM. In experiments, we simulate agents' movements in a combat field and employ agents' traces to evaluate performances of the LHHSMM and LPF. The results indicate that the team working mode and the target of each agent can be effectively recognized by our methods. When intentions are interrupted within a high probability, the LHHSMM outperforms a modified logical hierarchical hidden Markov model in terms of precision, recall, and F -measure. By comparing performances of LHHSMMs with different duration distributions, we prove that the explicit duration modeling of the working mode is effective in team intention recognition.