Action generation model based on changes in state patterns

Gouko Manabu, Tomi Naoki, Nagano Tomoaki, Ito Koji

Research output: Contribution to journalArticlepeer-review


In this paper, we propose a self-organized learning model that can generate behaviors for successfully performing various tasks. The model memorizes various relationships between changes in a state pattern and a motor command through learning. After the learning, the model can perform various tasks by generating the various behaviors automatically. We confirmed the performance of the model by applying it to a mobile robot simulation. The results indicate that suitable behaviors for all the tasks generated spontaneously. Additionally, we propose a sequential learning method which modifies the memorized various relationships while the model executes the task. And we confirmed the effectiveness of the sequential learning by the simulation.

Original languageEnglish
Pages (from-to)1690-1698+9
JournalIEEJ Transactions on Electronics, Information and Systems
Issue number9
Publication statusPublished - 2009
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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