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Tech for Autonomous Systems – Autonomous Decision Taking Capabilities II

The increasing autonomy of systems and their growing interconnection is creating complex systems in many areas. In the figure a representation of relationships among systems in health care domain. Credit: Columbia University

Complex Systems Technologies

Symbiotic Autonomous Systems are complex systems since they are the result of several interplaying factors and change their behaviour to adapt to changes in their environment. A single bacteria is a (very) complex system, current artificial autonomous systems are way less “complex” than a bacteria but still many falls under the category of complex systems.

The sets of relations an autonomous system has with its environment can often be described through the theory of Small World with sets of weak and strong relations (links). This is because the number of relations, particularly for systems that move around, like a self driving car or a drone, is quite large and the quality of relations varies a lot.

Some of these relations are passive, like a car becoming aware of a dog, a few may involve direct communications (like car to car communications). Modelling of these relations is an important part of a successful autonomy.

The degree of complexity in an autonomous system include both the system itself as well as the relations the system has to face. There are ways of measuring this complexity, like statistical complexity and self-dissimilarity. More work is needed in this area with specific reference of complexity in symbiotic autonomous systems.

Also, notice that telecommunications systems in general and internet specifically may be seen as complex systems for their high number of component elements and the variety of their interactions. With the shift from hierarchical architectures of the past, where complexity was managed in terms of hierarchy, hence highly reduced (one may claim that telecommunications electromechanical systems and even the first generation of electronic switches were “complicated”, not “complex”) to the flatter hierarchy of today the complexity of telecommunications systems has grown and the advent of IoT with millions of them having an autonomous behaviour that affect the overall network is further increasing this complexity.

The drive of Telecom Operator to manage in a rigid way the 5G network, also understandable in terms of limiting its complexity, may fail given the rise of the edges and their evolution in a “chaotic” way. It is most likely that 5G networks will have an increased level of complexity greater than current LTE networks.

Applying complexity metrics to today telecommunications networks and simulating first, then measure, the complexity of future 5G networks may be a good topic of research with several practical effects.

5G, for its characteristics of being (also) a communications fabric self-created at the edges by “autonomous systems” may prove to be a key component in their evolution. The variety of protocols that will be embedded in 5G provides the latitude required for communications between and within symbiotic autonomous systems.

Several domains, like smart cities, health care, production processes are becoming complex systems. Notice that a “complex” system is, in a way, “complicated” but the difference is that complication is an essential characteristics of a complex systems and it cannot be reduced because the system is … complex. On the other hand many systems are complicated but it is possible to reduce them into individual components each of with is “easy” and also the relations among them can be seen in subset making them “easy” (both to understand and manage). A complex system complexity cannot be reduced since complexity is an integral part of it. A bacteria can be “decomposed” in terms of its cellular organs and the metabolic relations can be identified and separated. However what you get from this decomposition is no longer understandable as a bacteria. You need to put it back all together and observe the overall clockwork to understand what is going on.

About Roberto Saracco

Roberto Saracco fell in love with technology and its implications long time ago. His background is in math and computer science. He's currently Head of the Industrial Doctoral School of EIT Digital, co-chair of the Symbiotic Autonomous Systems Initiative of IEEE-FDC. Until Aprile 2017 he led the EIT Digital Italian Node. Previously, up to December 2011 he was the Director of the Telecom Italia Future Centre in Venice, looking at the interplay of technology evolution, economics and society. At the turn of the century he led a World Bank-Infodev project to stimulate entrepreneurship in Latin America. He is a senior member of IEEE where he leads the Industry Advisory Board within the Future Directions Committee. He teaches a Master course on Technology Forecasting and Market impact at the University of Trento. He has published over 100 papers in journals and magazines and 14 books. He writes a daily blog,  http://sites.ieee.org/futuredirections/category/blog/, with commentary on innovation in various technology and market areas.

One comment

  1. Good point Roberto. I am also exploring this topics (in a limited scope) with my research group and will eventually contact you later.

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