A Logical Hierarchical Hidden Semi-Markov Model for Team Intention Recognition
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 Hi...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2015/975951 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832556359872151552 |
|---|---|
| author | Shi-guang Yue Peng Jiao Ya-bing Zha Quan-jun Yin |
| author_facet | Shi-guang Yue Peng Jiao Ya-bing Zha Quan-jun Yin |
| author_sort | Shi-guang Yue |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c819fc63c4b54876a9ea81e38ac2e71f |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-c819fc63c4b54876a9ea81e38ac2e71f2025-02-03T05:45:42ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2015-01-01201510.1155/2015/975951975951A Logical Hierarchical Hidden Semi-Markov Model for Team Intention RecognitionShi-guang Yue0Peng Jiao1Ya-bing Zha2Quan-jun Yin3College of Information System and Management, National University of Defense Technology, Changsha 410073, ChinaCollege of Information System and Management, National University of Defense Technology, Changsha 410073, ChinaCollege of Information System and Management, National University of Defense Technology, Changsha 410073, ChinaCollege of Information System and Management, National University of Defense Technology, Changsha 410073, ChinaIntention 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.http://dx.doi.org/10.1155/2015/975951 |
| spellingShingle | Shi-guang Yue Peng Jiao Ya-bing Zha Quan-jun Yin A Logical Hierarchical Hidden Semi-Markov Model for Team Intention Recognition Discrete Dynamics in Nature and Society |
| title | A Logical Hierarchical Hidden Semi-Markov Model for Team Intention Recognition |
| title_full | A Logical Hierarchical Hidden Semi-Markov Model for Team Intention Recognition |
| title_fullStr | A Logical Hierarchical Hidden Semi-Markov Model for Team Intention Recognition |
| title_full_unstemmed | A Logical Hierarchical Hidden Semi-Markov Model for Team Intention Recognition |
| title_short | A Logical Hierarchical Hidden Semi-Markov Model for Team Intention Recognition |
| title_sort | logical hierarchical hidden semi markov model for team intention recognition |
| url | http://dx.doi.org/10.1155/2015/975951 |
| work_keys_str_mv | AT shiguangyue alogicalhierarchicalhiddensemimarkovmodelforteamintentionrecognition AT pengjiao alogicalhierarchicalhiddensemimarkovmodelforteamintentionrecognition AT yabingzha alogicalhierarchicalhiddensemimarkovmodelforteamintentionrecognition AT quanjunyin alogicalhierarchicalhiddensemimarkovmodelforteamintentionrecognition AT shiguangyue logicalhierarchicalhiddensemimarkovmodelforteamintentionrecognition AT pengjiao logicalhierarchicalhiddensemimarkovmodelforteamintentionrecognition AT yabingzha logicalhierarchicalhiddensemimarkovmodelforteamintentionrecognition AT quanjunyin logicalhierarchicalhiddensemimarkovmodelforteamintentionrecognition |