Search results for "Emergent behaviour"
showing 3 items of 3 documents
Applying complexity science to air traffic management
2015
Versión aceptada obtenida del archivo digital en línea WestminsterResearch de la Universidad de Westminster. Complexity science is the multidisciplinary study of complex systems. Its marked network orientation lends itself well to transport contexts. Key features of complexity science are introduced and defined, with a specific focus on the application to air traffic management. An overview of complex network theory is presented, with examples of its corresponding metrics and multiple scales. Complexity science is starting to make important contributions to performance assessment and system design: selected, applied air traffic management case studies are explored. The important contexts of…
Sports teams as complex adaptive systems: manipulating player numbers shapes behaviours during football small-sided games
2016
Small-sided and conditioned games (SSCGs) in sport have been modelled as complex adaptive systems. Research has shown that the relative space per player (RSP) formulated in SSCGs can impact on emergent tactical behaviours. In this study we adopted a systems orientation to analyse how different RSP values, obtained through manipulations of player numbers, influenced four measures of interpersonal coordination observed during performance in SSCGs. For this purpose we calculated positional data (GPS 15 Hz) from ten U-15 football players performing in three SSCGs varying in player numbers (3v3, 4v4 and 5v5). Key measures of SSCG system behaviours included values of (1) players’ dispersion, (2) …
Emergent behaviors and scalability for multi-agent reinforcement learning-based pedestrian models
2017
This paper analyzes the emergent behaviors of pedestrian groups that learn through the multiagent reinforcement learning model developed in our group. Five scenarios studied in the pedestrian model literature, and with different levels of complexity, were simulated in order to analyze the robustness and the scalability of the model. Firstly, a reduced group of agents must learn by interaction with the environment in each scenario. In this phase, each agent learns its own kinematic controller, that will drive it at a simulation time. Secondly, the number of simulated agents is increased, in each scenario where agents have previously learnt, to test the appearance of emergent macroscopic beha…