2011/01/31 by Andrea Roli, Roli, Andrea, Mattia Manfroni +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial Intelligence (cs.AI) #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Neural and Evolutionary Computing (cs.NE) #Robotics (cs.RO) #Single-cell and spatial transcriptomics #cs.AI #cs.NE #cs.RO
paper · pdf · doi:10.48550/arxiv.1101.6001
11 pages, 6 figures
arxiv created 2011/01/31 · openalex publication_date 2011/01/31 · arxiv updated 2015/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Dynamical systems theory and complexity science provide powerful tools for analysing artificial agents and robots. Furthermore, they have been recently proposed also as a source of design principles and guidelines. Boolean networks are a prominent example of complex dynamical systems and they have been shown to effectively capture important phenomena in gene regulation. From an engineering perspective, these models are very compelling, because they can exhibit rich and complex behaviours, in spite of the compactness of their description. In this paper, we propose the use of Boolean networks for controlling robots' behaviour. The network is designed by means of an automatic procedure based on stochastic local search techniques. We show that this approach makes it possible to design a network which enables the robot to accomplish a task that requires the capability of navigating the space using a light stimulus, as well as the formation and use of an internal memory.