Exploitable results Developing artificial societies The visionary goal of the project was to realize an evolving artificial society of virtual agents capable of exploring its virtual world and developing its own view of that world. The long-term target is to learn how to design agents that are able to adapt autonomously to, and then operate effectively in, environments whose features are not known. This ambitious goal was converted into the following concrete project objectives: 1. Develop an artificial society with an emergent culture. 2. Realise a powerful ''emergence engine'' consisting of a well-balanced combination of individual learning, evolutionary learning, and social learning. 3. Develop, evaluate, and use a range of social learning mechanism that allows sharing knowledge with other members of the population. 4. Solve two sociological challenges: Herders in a Semi-arid Area and Central Place Theory. 5. Build a distributed platform to perform simulations. 6. Provide an open arena for others to participate in meeting the challenges and to specify their own The research is carried out in computer simulations, the environments are abstractions of reality. Our main premise is that given a demanding environment (that only allows survival of agents with specific knowledge and skills) and sufficiently powerful adaptation mechanisms, a population of agents will develop the required skills to survive. As concrete drivers behind the development of society of agents we included so-called challenges. A challenge in this context is a demanding situation or environment, inspired by sociology, where staying alive requires the development certain agent behaviour (the solution), for instance trading or herding. The main pillars of the envisioned research are world models and the learning mechanisms generating these. We did not plan to implement specific training facilities or feedback systems rewarding the learning of world models or some desirable behaviour. Instead we were interested in emergent phenomena powered by a basic survival game. The envisioned ''emergence engine'' driving population development is based on three algorithmic building blocks: evolution, individual learning, and social learning. To give rise to interesting emergent features, we were to work on a very large scale with respect to the size of the agent population and agent complexity and planned to enable this by creating a distributed (person- to-person) software infrastructure. Searching for OpenAIRE data... There was an error trying to search data from OpenAIRE No results available
