A logical hierarchical hidden semi-Markov model for team intention recognition
type: Item
instance of: scholarly article
title: A logical hierarchical hidden semi-Markov model for team intention recognition
zbMATH Open document ID: 1422.68225
DOI: 10.1155/2015/975951
author: Shi-guang Yue
author: Peng Jiao
author: Yabing Zha
author: Quanjun Yin
published in: Discrete Dynamics in Nature and Society
publication date: 2019-02-19T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 68T42
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68T10
Mathematics Subject Classification ID: 68T37
zbMATH DE Number: 7025571
Wikidata QID: Q59105606
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/975951
OpenAlex ID: W2139956023
cites work: Hidden semi-Markov models
cites work: The hierarchical hidden Markov model: Analysis and applications
cites work: Q4411146
cites work: Q3624004
cites work: Markov logic networks
cites work: Location-based reasoning about complex multi-agent behavior
description: scientific article; zbMATH DE number 7025571