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Authors: Shi-guang Yue ; Ya-bing Zha ; Quan-jun Yin and Long Qin

Affiliation: National University of Defense Technology, China

Keyword(s): Multi-agent intention recognition; probabilistic graphical models; first order logic; duration modeling; logical semi-Markov models;

Related Ontology Subjects/Areas/Topics: Agent Based Modeling and Simulation ; Application Domains ; Complex Systems Modeling and Simulation ; Robotics, Automation and Mechatronics ; Sensor Networks ; Simulation and Modeling ; Simulation Tools and Platforms ; Software and Architectures

Abstract: 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 ev aluate 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. (More)

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Paper citation in several formats:
Yue, S.; Zha, Y.; Yin, Q. and Qin, L. (2014). Multi-Agent Intention Recognition using Logical Hidden Semi-Markov Models. In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH; ISBN 978-989-758-038-3; ISSN 2184-2841, SciTePress, pages 701-708. DOI: 10.5220/0005036707010708

@conference{simultech14,
author={Shi{-}guang Yue. and Ya{-}bing Zha. and Quan{-}jun Yin. and Long Qin.},
title={Multi-Agent Intention Recognition using Logical Hidden Semi-Markov Models},
booktitle={Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH},
year={2014},
pages={701-708},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005036707010708},
isbn={978-989-758-038-3},
issn={2184-2841},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH
TI - Multi-Agent Intention Recognition using Logical Hidden Semi-Markov Models
SN - 978-989-758-038-3
IS - 2184-2841
AU - Yue, S.
AU - Zha, Y.
AU - Yin, Q.
AU - Qin, L.
PY - 2014
SP - 701
EP - 708
DO - 10.5220/0005036707010708
PB - SciTePress

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