---
title: "Imitation Learning of Team-play in Multiagent System based on Hidden Markov Modeling"
authors:
  - "Itsuki Noda"
doi: ""
url: "https://ijcai.org/proceedings/2003/240"
pdf: "https://ijcai.org/proceedings/2003/0240.pdf"
venue: "Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence"
year: 2003
pages: "1470-1471"
track: ""
keywords:
---

# Imitation Learning of Team-play in Multiagent System based on Hidden Markov Modeling

Itsuki Noda

*Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence*, pages 1470-1471.

## Abstract

This paper addresses agents' intentions as building blocks of imitation learning that abstract local situations of the agent, and proposes a hierarchical hidden Markov model (HMM) to represent cooperative behaviors of teamworks. The key of the proposed model is introduction of gate probabilities that restrict transition among agents' intentions according to others' intentions. Using these probabilities, the framework can control transitions flexibly among basic behaviors in a cooperative behavior.