Toward a novel design of swarm robots based on the dynamic Bayesian network

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)


In this chapter, we describe a novel design method of swarm robots based on the dynamic Bayesian network. Recently, an increasing attention has been paid to swarm robots due to their scalability, flexibility, cost-performance, and robustness. Designing swarm robots so that they exhibit intended collective behaviors is considered as the most challenging issue and so far ad-hoc methods which heavily rely on extensive experiments are common. Such a method typically faces a huge amount of data and handles them possibly using machine learning methods such as clustering.We argue, however, that a more principled use of data with a probabilistic model is expected to lead to a reduced number of experiments in the design and propose the fundamental part of the approach. A simple but a real example using two swarm robots is described as an application.

Original languageEnglish
Title of host publicationAdvances in Data Management
EditorsZbigniew Ras, Agnieszka Dardzinska
Number of pages12
Publication statusPublished - 2009

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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