Portal:Complex Systems Digital Campus/E-Department on Designing artificial complex systems

Portal:Complex_Systems_Digital_Campus/E-Department_on_Designing artificial complex systems Designing Artificial Complex Systems Modeling and simulation are crucial complementary tools in the exploration of complex systems. The recent and fast-growing development of complex systems research in many scientific fields, along with the strong interdisciplinary interactions that it created, was greatly stimulated by the striking advances in computer networks and high-performance calculation. Information and communication technologies represent today a major tool of investigation in complex systems science, often replacing analytic and phenomenological approaches in the study of emergent behavior. In return, information technologies also benefit from complex system research. Artificial complex systems can be created to analyze, model and regulate natural complex systems. Conversely, new and emergent technologies can find inspiration from natural complex systems, whether physical, biological or social.

Grand Challenges: 1.	Using artificial complex systems for the understanding and regulation of natural complex systems 2.	Finding inspiration in natural complex systems for the design of artificial complex systems 3.	Building hybrid complex systems. 1. Using artificial complex systems for the understanding and regulation of natural complex systems Natural complex systems (NCS) include systems found in nature (pattern formation, biological organisms, ecosphere, etc) but also systems spontaneously originating from human activity (cities, economy, transportation, etc.) A key application of artificial complex systems (ACS) is to assist the description, generation and support of these NCS. One major challenge is to design and develop systems that can carry out a methodical exploration and/or regulation of NCS. In particular, ACS design can complement human collective intelligence by integrating different levels of expertise and harmonize or manage contradictions in collaborative works. Such artificial systems can be based on structures and functioning principles different from the natural systems they observe. An ACS could serve to regulate, schedule, repair or modify the NCS. The execution of ACS can be asynchronous and separate from the NCS, or it can be integrated with it.

Examples: •	Reconstructing the topology of neural connections in the brain by means of neuro-imagery and artificial vision based on a distributed architecture •	Observation of interest groups and interaction networks on the Internet (forums, blogs, instant messaging) through software agents •	Airflight dynamics and network 2. Finding inspiration in natural complex systems for the design of artificial complex systems In order to create technological systems that are autonomous, robust and adaptive, new engineering approaches must draw inspiration from NCS. For example, in computer security, systems able to mimick the biological immune system can provide useful solutions against continuously evolving attacks on computer networks. These ACS are built upon intrinsically distributed, self-organizing and evolutionary entities. They reproduce the original behavior and organizational principles that are found in NCS but have no equivalent in traditional technical design. In some domains, biology could even replace physics at the foundation of new engineering principles.

NCS provide rich sources of ideas in the development of decentralized systems that can display robustness, modularity, and autonomy in dynamically changing environments (i.e., “ubiquitous computing”, “ambient intelligence”). ACS should be able to reproduce the dual principles of cooperation and competition that are observed in NCS.

On the other hand, bio-inspired artificial design is not constrained by any fidelity to the original NCS. Computer and technological innovation can free designers from experimental data or real examples of functioning mechanisms. Examples include neural networks inspired by neuroscience and genetic algorithms by Darwinian evolution. ACS created this way can also play a heuristic exploratory role for NCS. Engineering inventions allow us to better understand, even predict the natural phenomena that inspired them. Examples:

•	Neuro-inspired artificial intelligence and robotics •	Collective optimization and swarm intelligence inspired from social animal behavior •	Evolutionary robotics •	Intelligent materials, auto-assembling materials, and morphogenetic engineering (nanotechnologies) •	Ambient intelligence •	Computer security inspired by immune systems or social interactions 3. Design of Hybrid Complex Systems The rapid dissemination of computing devices and systems in our society (cellphones, PDAs, etc.) and the intricacy and profusion of their interconnections constitute a major case of hybrid or “techno-social” complex systems. Such systems can be studied as complex communities combining natural and artificial agents. Users can instruct machines, themselves capable of autonomous learning and adaptation to their environment.