Multi-agent intention recognition using logical hidden semi-Markov models

Intention recognition (IR) is significant for creating humanlike and intellectual agents in simulation systems. Previous widely used probabilistic graphical methods such as hidden Markov models (HMMs) cannot handle unstructural data, so logical hidden Markov models (LHMMs) are proposed by combining HMMs and first order logic. Logical hidden semi-Markov models (LHSMMs) further extend LHMMs by modeling duration of hidden states explicitly and relax the Markov assumption. In this paper, LHSMMs are used in multi-agent intention recognition (MAIR), which identifies not only intentions of every agent but also working modes of the team considering cooperation. Logical predicates and connectives are used to present the working mode; conditional transition probabilities and changeable instances alphabet depending on available observations are introduced; and inference process based on the logical forward algorithm with duration is given. A simple game “Killing monsters” is also designed to evaluate the performance of LHSMMs with its graphical representation depicted to describe activities in the game. The simulation results show that, LHSMMs can get reliable results of recognizing working modes and smoother probability curves than LHMMs. Our models can even recognize destinations of the agent in advance by making use of the cooperation information.