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  The project is concerned with emergence and complexity in socially-inspired artificial systems. We will study large systems consisting of an environment and an inhabitant population. The main goal of the project is to realize an evolving artificial society capable of exploring the environment and developing its own image of this environment and the society through cooperation and interaction. We will work with virtual grid worlds and will set up environments that are sufficiently complex and demanding that communication and cooperation are necessary to adapt to the given tasks. The population's weaponry to develop advanced skills bottom-up consists of individual learning, evolutionary learning, and social learning. One of the main innovations of this project is social learning interpreted as passing knowledge explicitly via a language to others in the same generation. This has a synergetic effect on the learning processes and enables the society to rapidly develop an "understanding" of the world collectively. If the learning process stabilises, the collective must have formed an appropriate world map. Then we will probe the collective mind to learn how the agents perceive the environment, including themselves, and what skills and procedures they have developed to adapt successfully. This could yield new knowledge and surprising perspectives about the environment and the survival task. The project represents a significant scale-up beyond the state-of-the-art in two dimensions: the inner complexity of inhabitants and the size of the population. To achieve and explore highly complex organisms and behaviours, very large populations will be studied. This will make the system at the macro level complex enough to allow significant behaviours (cultures etc) to emerge in separate parts of the system and to interact. To enable this we will set up a large distributed computing infrastructure, and a shared platform to allow very large scale experiments in a p2p fashion.