Space Syntax as a Distributed Artificial Intelligence System: A Framework for a Multi-Agent System Development

This study aims to advance the application of Space Syntax by integrating it with Agent- Based System (ABS) features within the NetLogo environment. Building upon the foundational work done using UCL DepthMap the research replicates Alasdair Turner's early 2000s experiments at the Tate Britain to validate the results within this new framework, establishing a baseline using the original DepthMap outputs from Turner ad Penn. The methodology progresses by constructing a parallel simulation framework in NetLogo, initially confirming functional equivalence through integration diagrams before introducing key ABS elements—agent communication and dynamic attractors in the form of paints located along the museum spaces. These features are hypothesized to enrich the simulation of spatial dynamics by facilitating more nuanced interactions and emergent behaviors, potentially providing deeper insights into human spatial behavior. Findings will show the proposed NETLogo framework is validated by the same correlation, 0.89, Turner obtained in 2007 along his research on Through Vision mathematical definition. Implemented Agent-based-system features, message passing and goals/attractors, will show a clear influence on agents behavior as a priori guessed by Turner in 2011 along his evolved automaton proposal