Urban designers use sketches and symbols to express spatial intentions, while many generative urban design tools rely on numeric parameters that are difficult to relate to this visual process. We propose a cross-scale workflow and a web-based prototype that translates symbolic sketches into controllable procedural urban generation. A vocabulary of shapes, informed by examples of urban design diagrams and sketching exercises with graduate urban design students, encodes attributes such as network structure, density, and program. The prototype uses the \$1 Unistroke Recognizer to map gestures to symbols and translates their locations and sizes into tensor-field attractors and weighted parameter grids. Grid and radial tensor fields guide street-network generation, while the parameter grids control building heights and programs. Implemented in JavaScript and TypeScript with React and Three.js, the prototype supports interactive sketching, visualization, and urban massing generation. We describe the methodology and illustrate prototype interactions, then discuss downstream integration with architectural-scale generative tools for further refinement and analysis. The work connects designers' symbolic input with an explicit procedural generation process.
Conflicting stakeholder objectives make it difficult to coordinate investments in urban resilience. We model conflict-driven urban design as a non-zero-sum digital board game in which stakeholder agents allocate scarce resources and balance individual payoffs against shared urban development and resilience objectives. We use multi-agent reinforcement learning (MARL) to train policies and examine their behavior across three settings. On a 4 x 4 board, learned policies reach about 84% of the total return found by a simulated annealing benchmark. On an 8 x 8 board with spatial and resource constraints, trained agents favor projects that improve resilience in self-play. In a 12 x 12 simulation initialized from historical maps of Tatsumi, Tokyo, trained agents achieve a mean total score of 452.1, compared with 357.3 for a random-policy baseline across 10,000 simulation runs. Gains in adaptability, sustainability, flood resistance, and greenery come with reductions in vitality and density. A human-in-the-loop interface supports replay and lets participants take over stakeholder decisions, providing a testbed for exploring these trade-offs in participatory urban planning.