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This work presents a concept of interactive machine learning in a human design process. An urban design problem is viewed as a multiple-criteria optimization problem. The outlined feature of an urban design problem is the dependence of a design goal on a context of the problem. We model the design goal as a randomized fitness measure that depends on the context. In terms of multiple-criteria decision analysis (MCDA), the defined measure corresponds to a subjective expected utility of a user. In the first stage of the proposed approach we let the algorithm explore a design space using clustering techniques. The second stage is an interactive design loop; the user makes a proposal, then the program optimizes it, gets the user’s feedback and returns back the control over the application interface.
The idea about a simulation program to support urban planning is explained: Four different, clearly defined developing paths can be calculated for the rebuilding of a shrinking town. Aided by self-organization principles, a complex system can be created. The dynamics based on the action patterns of single actors, whose behaviour is cyclically depends on the generated structure. Global influences, which control the development, can be divided at a spatial, socioeconomic, and organizational-juridical level. The simulation model should offer conclusions on new planning strategies, especially in the context of the creation process of rebuilding measures. An example of a transportation system is shown by means of prototypes for the visualisation of the dynamic development process.