This report documents the program and the outcomes of the Dagstuhl Seminar 11172 "Artificial Immune Systems". The purpose of the seminar was to bring together researchers from the areas of immune-inspired computing, theoretical computer science, randomised search heuristics, engineering , swarm intelligence and computational immunology in a highly interdisciplinary seminar to discuss two main issues: first, how to best develop a more rigorous theoretical framework for algorithms inspired by the

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... immune system and second, to discuss suitable application areas for immune-inspired systems and how best to exploit the properties of those algorithms. Seminar 26.-29. April, 2011-www.dagstuhl.de/11172 1998 ACM Subject Classification I.2.8 Problem Solving, Control Methods, and Search; Heur-istic methods. Artificial immune systems (AISs) are inspired by biological immune systems and mimic these by means of computer simulations. They are seen with interest from immunologists as well as engineers. Immunologists hope to gain a deeper understanding of the mechanisms at work in biological immune systems. Engineers hope that these nature-inspired systems prove useful in very difficult computational tasks, ranging from applications in intrusion-detection systems to general optimization. Moreover, computer scientists identified artificial immune systems as another example of a nature-inspired randomized search heuristic (like evolutionary algorithms, ant colony optimization, particle swarm optimization, simulated annealing, and others) and aim at understanding their potential and limitations. While the relatively new field has its successful applications and much potential its theoretical foundation is still in its infancy. Currently there are several not well connected strands within AIS theory, not even a general agreement on what the central open problems are, and only a weak connection between AIS theory and AIS applications. The main goals of the proposed seminar include bringing together computer scientists and engineers to strengthen the connections within AIS theory, connections to other researchers working on the theory of randomized search heuristics, and to improve connectivity between AIS theory and applications.