Identifying Emergent Dynamical Structures in Network Models

AI*IA 2013: Advances in Artificial Intelligence

Serra

et al. 2013

Self Cite

Abstract. Automatic techniques for the design of artificial computational systems, such as control programs for robots, are currently achieving increasing attention within the AI community. A prominent case is the design of artificial neural network systems by means of search techniques, such as genetic algorithms. Frequently, the search calibrates not only the system parameters, but also its structure. This procedure has the advantage of reducing the bias introduced by the designer and makes it possible to explore new, innovative solutions. The drawback, though, is that the analysis of the resulting system might be extremely difficult and limited to few coarse-grained characteristics. In this paper, we consider the case of robots controlled by Boolean networks that are automatically designed by means of a training process based on local search. We propose to analyse these systems by a method that detects mesolevel dynamical structures. These structures are emerging patterns composed of elements that behave in a coherent way and loosely interact with the rest of the system. In general, this method can be used to detect functional clusters and emerging structures in nonlinear discrete dynamical systems. It is based on an extension of the notion of cluster index, which has been previously proposed by Edelman and Tononi to analyse biological neural systems. Our results show that our approach makes it possible to identify the computational core of a Boolean network which controls a robot.

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Section: C(s)mentioning

confidence: 99%

Identification of Dynamical Structures in Artificial Brains: An Analysis of Boolean Network Controlled Robots

AI*IA 2013: Advances in Artificial Intelligence

Serra

et al. 2013

Self Cite

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On Some Properties of Information Theoretical Measures for the Study of Complex Systems

Filisetti

Communications in Computer and Information Science

et al. 2014

The identification of emergent structures in dynamical sys-\ud tems is a major challenge in complex systems science. In particular, the\ud formation of intermediate-level dynamical structures is of particular in-\ud terest for what concerns biological as well as artificial systems. In this\ud work, we present a set of measures aimed at identifying groups of ele-\ud ments that behave in a coherent and coordinated way and that loosely\ud interact with the rest of the system (the so-called \relevant sets"). These\ud measures are based on Shannon entropy, and they are an extension of a\ud measure introduced for detecting clusters in biological neural networks.\ud Even if our results are still preliminary, we have evidence for showing\ud that our approach is able to identify and partially characterise the rele-\ud vant sets in some artificial systems, and that this way is more powerful\ud than usual measures based on statistical correlation. In this work, the\ud two measures that contribute to the cluster index, previously adopted in\ud the analysis of neural networks, i.e. integration and mutual information,\ud are analysed separately in order to enhance the overall performance of\ud the so-called dynamical cluster index. Although this latter variable al-\ud ready provides useful information about highly integrated subsystems,\ud the analysis of the different parts of the index are extremely useful to\ud better characterise the nature of the sub-systems

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On the Robustness of the Detection of Relevant Sets in Complex Dynamical Systems

Carra

Communications in Computer and Information Science

et al. 2016