Large-scale Multi-agent Simulation and Crowd Sensing with Humans in the Loop

Overview

Introduction to Augmented Virtuality

Main topic of this talk is Fusion of Real and Virtual worlds creating Augmented Virtuality by using Mobile Agents!

Simulation of Socio-Technical Systems

• Socio-technical systems are characterized by interactions of:

  • Human-Human (initiated by a human)
  • Human-Machine (initiated by a human)
  • Machine-Human (initiated by a machine, e.g., a chat bot)
  • Machine-Machine (initiated by a machine)

• The simulation of social ensemble behaviour requires simplification of interactions and individual behaviour

• Commonly simulations are performed with less than 1000 entities (humans, machines, ..) in a sandbox world

• Agent-based Modelling (ABM) is a suitable behaviour model for simulation

Augmented Reality

• Augmented reality is well known for extending the real world by adding computer-generated perceptual information and overlaid sensory information

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Field Studies

• Experimental field studies are commonly used in social science to test social models or to derive social models
• The ensemble size in field studies is often limited to less than 1000 individuals or entities

Data Mining and Machine Learning are important tools to derive meaningful information from experimental and aggregated data.

Taxonomy of Data Mining

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Crowd Sensing

• Crowd data can be used in field studies to extend the information data base or replace classical (survey) field studies

• Mobile Crowd Sensing combines aggregation of user data and mobile computing, i.e., creating spatially annotated data traces

• Among data supplied by users explicitly, sensor data of mobile devices can be used, too. But: Weakly correlated data!