Multi-Agent Geo-Simulation for Crowd ControlE. Iskandar, K. Seifu, W. Dhaouadi, M. Mekni

Computer Science and Information Technology Department, St. Cloud State University, St. Cloud, Minnesota

OBJECTIVES

SCENARIO CREATION

EMERGING GROUP MANAGER DYNAMIC SOCIAL IDENTITY

DATA ANALYSIS & VALIDATION

CONCLUSION & FUTURE WORKS

BIBLIOGRAPHY

CONTROLSA. Time SliderB. Information PanelC. Police Behavior Control

Models and simulations that describe crowd behaviors in conflict situations involving control forces do not exist in a form usable to assess the impact of non-lethal weapons (NLW) on crowd dynamics and the resolution of conflict. The aim of this project is to develop a method, models, and a platform than can be used to assess the effectiveness of various types of NLW in situations of crowd control in urban environments. The proposed solution enhances agent models with a social dimension used for the management of groups of agents and their collective actions. A simulation tool was developed to calibrate and use the agent models in realistic scenarios and geographic environments, as well as to generate data for quantitative or qualitative analysis of the effectiveness of different intervention techniques.

PROTOTYPE FEATURES• Simulates in situations of conflict: the behavior of the crowd, the

behavior of control forces and the interactions between them• Simulates emerging groups and their social influence on agents• Simulates the use of non-lethal weapons and their physical and

psychological influence• Simulates a realistic 3D environment

(terrain, buildings, interest points, etc.)• Evaluates the effectiveness of various intervention strategies

using NLW• Integrates with a system dynamics model used for the macro-

simulation of crowd events

We developed a complete application that puts in place our agent, group, and information models. It allows the simulation of agents (crowd and control forces) in environments generated from GIS files [3]. Scenarios can be created and customized to help control forces prepare for specific events or to improve their intervention techniques. A scenario can be run as often as desired, possibly altering the intervention strategy at every trial, in order to compare the output simulation data [3].

The application allows also the editing o f scenarios. Fig. 1 shows a palette used to drag new components to be simulated in the environment. Other parameters also can be edited, such as the equipment worn by officers, the average walking speed, and the strength of tear gas. All information is stored in a XML scenario file (Fig. 2).

Fig 1 (left): Component Palette &

Fig 2 (top): XML Scenario File

In purposive crowds, people gather for a specific collective purpose [2]. We suggest that when modeling a purposive crowd, the most important element is not the individual, but the group. Individuals reason and make decisions on an individual basis, but their references are groups [1]. A bystander may observe a crowd event individually, but she may try to join a nearby group of demonstrators which attracts her.

Our group model (Fig. 3) is generic and does not represent any specific type of group, such as bystanders or instigators. Emerging groups can be scripted but they are usually created dynamically by agents who do not necessarily know each other. The group is perceptible from all agents because it is geometrically defined by its formation. Thus, we use the term spatio-temporal group (STG). No role exists in a STG, except for a controller agent who sets the formation and navigates in the environment. Other agents take their positions with respect to him. Crowd agents decide when to join and leave a STG, based on their appreciation of the STG [1].

Agents assess STGs based on the STGs’ projected images, which include the formation, the history of collective actions and events, and the aggressiveness level, which is based on the actions. Members of a STG are free to perform any individual action, but they typically participate in collective actions with the other members [1].

Fig 3 (top): Spatio-Temporal Group (STG)

Model

A social identity (SI) is a very high-level behaviour that defines the way in which an agent behaves. Agents start with a fundamental social identity and a profile [2]. A profile is a simple data structure that contains parameters and that influences the agent’s behaviour, but does not change it radically [2]. Agents may adopt a different SI based on their perception of the surrounding groups. This change fundamentally alters their behaviours [2].

For each agent, low-level human behaviours, the SI controller and the adopted SI are implemented by parallel and hierarchical finite automata, run by the PLAMAGS behaviour engine (Fig. 4). The PLAMAGS engine allows agents to hold several automata at once, as well as to add and remove any of them at any time. Each agent objective (automaton node) may require a resource in order to execute, such as the availability of the right arm or simply a projectile such as a rock.

Each agent continuously monitors its surroundings and may be influenced by the actions of other agents to adopt a SI different from his fundamental one. The SI controller handles the task of adding and removing automata for SIs.In this project, the Sis are bystanders, demonstrators, demonstrator leaders, instigators, instigator leaders, police squad members, and police leaders.

Fig 4 (top): Agent Model

Thanks to the 3D visualisation, it is possible to qualitatively assess the effectiveness of an intervention technique. In addition, an information model was developed to allow for quantitative analyses. The model tracks agents and groups, individual and collective actions detailed with time intervals, descriptions and categories, and aggressiveness levels.

The model (Fig. 5) is generic and its applicability was validated by coupling the application with a system dynamics (SD) model that simulates crowd control situations at the macro-level. The information model is able to generate all data necessary as input to the SD model. This one was used to crosscheck our models for calibration purposes. It also allows to quickly try many intervention techniques and eliminate the undesirable ones. The multi-agent system allows analyzing in detail the remaining techniques.

The micro-level (agent) and meso-level (group) behaviours were validated using video footage from real crowd events. Macro-level (crowd) behaviours were validated by testing and comparison with others models.

Fig. 6 & 7 compares for three types of crowds in their overall aggressive- ness, which is one of the key factors considered in intervention strategies, along with the number of injured people, the crowd dispersion, the cost, and others.

We created an agent model and an emerging group model that work hand in hand to simulate plausible crowd behaviours. We created also an information model that allows the visualization and analysis of virtually any aspect of the agents’ behaviours at the micro, meso and macro level. We delivered an application that can be used for editing and running scenarios, as well as allowing user interactions while the scenarios are running. We calibrated the system and experimented by coupling it with a SD model.

FUTURE WORKS:• Improve the software to be used as a full decision-support

system• Improve the software to be used as a training system• Enhance the crowd behaviours to increase plausibility and

simulate more actions and better communication• Enhance the control forces strategies and NLW arsenal• Perform more in-depth validation of crowd behaviours• Improve the simulation engine for better path planning and to

handle more agents (current limit is 900 agents)

Fig 5 (top left): Main elements of the information model. Fig 6 & 7 (top and left): output data

analysis of 3 crowds

The application allows pausing, stopping, slowing down and speeding up the simulation (A). It is possible to add, remove, and drag agents, but not to control their behaviours, because each agent is autonomous. An information panel (B) allows getting detailed information about each individual component, including its history of actions, state (social identity, health, appreciation of other groups, etc), and needs. The user can set-up multiple cameras (D) and take snapshots of the scene (E). The user can also control the police officers while the application is running (C). However, only global strategies can be given. Micro-management is performed by the agent (squad leader).

D. Camera ControlE. Screen Capture Tool

1

Agents may join the large demonstrators STG (blue) or the small overlapping instigator STG

(red) if they like these group’s actions.

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3

Information about agents and other components can be viewed in real time and is always saved using the information model.

A bystander (green shirt) appreciated the actions from surrounding groups and adopt the social identity of “Demonstrator”. He is now

joining the group.

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[1] Mekni, Mehdi, Crowd Simulation Using Informed Virtual Geospatial Environments, 2nd WSEAS International Conference on Information Technology and Computer Networks (ITCN’13), 2013.[2] Kapalka, Michal, Simulation of Human Behavior in Different Densities as Part of Crowd Control Systems, Intelligent Information and Database Systems, pp. 202-211. Springer International [2] Publishing, 2015[3] Mekni, Mehdi, Abstraction of Informed Virtual Geographic Environments, Geo-Spatial Information Science 15, No. 1, pp. 27-36, 2012

ised0301@stcloudstate.edu, seki0801@stcloudstate.edu, wdhaouadi@stcloudstate.edu, mmekni@stcloudstate.edu St. Cloud State University

CSITComputer Science and

Information Technology