Space Syntax & multi-agent simulation An Exploration of Architectural Theory In Multi-Agent Simulation Glenn Elliott – Robotics 790, Fall 2008

Overview My project will investigate the applications Space Syntax, a theory from the field of Architecture, in multi-agent simulation. The project will strive to replicate human movement patterns derived from the relation between humans and their environment’s spatial configuration.

Space Syntax Space Syntax is an Architectural theory that claims space, the voids between objects (including walls, floors, etc.), can be described as a traditional graph of nodes and edges. The major ramification of this is that computation may be performed on space-graph to show various spatial characteristics. Some of these have a strong influence on how humans move through the described space.

Space Syntax Graphs Images from Space is the Machine by Bill Hillier. ^ Graphs describing the relations of blocks A and B to surface C in different configurations. Floor plans of the same area may have > radically different graphs.

Prior Art The bulk of Space Syntax applications in multi- agent simulation has been done by Alan Penn and Alasdair Turner (see “Space Syntax Based Agent Simulation” in Proceedings of the 1st International Conference on Pedestrian and Evacuation Dynamics). I have not yet found any other published contributors to Space Syntax based multi-agent simulations.

Prior Art – Penn & Turner’s Method Step 1: Preprocess a 2D floor plan, deriving visibility of every point to every other point in the floor plan. This is known as a visibility map. (O(n2)) Points are generated from a uniform sample across the floor plan. Step 2: Store visibility information at each point in “angle buckets” such that queries on point visibility within a cone of vision can be quickly performed. Example: “Give me all points visible at point X between -60 and 60 degrees of vision.” Step 3: Place an agent in the environment with a cone of vision. Move the agent towards a random visible goal point within their cone of vision. Repeat every few steps.

Prior Art – Penn & Turner’s Method < An agent’s local view. Regions with high visibility have a high probability of being selected as a goal location. This effectively “draws” an agent into regions of higher visibility.

Prior Art – Penn & Turner’s Method < Frequency of many agents’ paths through an environment (Ikea store).

Novel Aspects There appear to be promising areas of growth for Space Syntax in multi-agent simulation. The “Visibility Map” is only one of many Space Syntax measurements. There are several other higher-level metrics that may be useful an agent simulation. Penn and Turner approach multi-agent simulation from an Architect’s perspective. Their methods analyze an entire space/floor plan/building. Requires a costly O(n2) pre-computation.

Goals Add Space Syntax ideas into existing UNC RVO multi-agent simulator. Evacuation scenarios. May enhance existing algorithms or develop on-the-fly methods. Agents may “explore” between waypoints towards building exit. Hide-and-Seek. Requires higher level Space Syntax metrics (Axial Map, Depth Map). Will not interfere with exiting RVO features such as Proxy Agents.