Photo credits : paintings done by Gilbert Williams ( Illumination Window (1989), Healing Earth (1994)







The MUSCAMAGS Project Website

Project funded by: GEOIDE, The Canadian Network of

Centers of Excellence in Geomatics




MAGS Project

Context of the MAGS Project

In an increasingly interconnected and fast-changing world, decision makers from various sectors (governmental, military, industrial, medical, social) need to deal with multi-actor dynamic spatial situations (MADSS) that involve a large number of actors of different types (human, animal, static, mobile, computer systems, etc.) acting in geographic spaces of various extents. There are numerous MADSSs that need to be monitored in order to insure human security and equipment preservation (flood, earthquake, wildfire, oil slicks), the respect of public order (evacuation of people, crowd monitoring and control, peace-keeping activities) or the adequate use of infrastructures (monitoring of people and households transportation and shopping habits to better plan transportation infrastructures, location of services’ and retailers, etc.). For any particular MADSS, decision makers need to obtain an overall understanding of the situation, to monitor its evolution, to develop strategies and tactics to adequately intervene, to develop and compare alternative intervention scenarios. Yet there exists no integrated tool that helps decision makers when dealing with such complex situations.

Geosimulation aims at modeling systems at the scale of individuals and entity-level units of the built environment and provides a new way to simulate how geographic spaces can be used by their future users, particularly in urban environments. In the MAGS Project we developed a generic software platform and a method for the creation of Multi-Agent Geo-Simulations involving several thousand agents interacting in virtual geographic environments (in 2D and 3D) and endowed with spatial cognitive capabilities (perception, navigation, reasoning).


Description of the MAGS System

The MAGS System (MultiAgent Geo-Simulation) (Moulin et al., 2003) is a generic platform that can be used to simulate, in real-time, thousands of knowledge-based agents navigating in a 2D or 3D virtual environment. It runs on Windows Platforms.

MAGS agents have several knowledge-based capabilities such as perception, navigation, memorization, communication and objective-based behavior which allow them to display an autonomous behavior within a 2D-3D geographic virtual environment. The agents in MAGS are able to perceive the elements contained in the environment, to navigate autonomously inside it and to react to changes occurring in the environment.

MAGS has been used to develop applications in several domains: crowd behavior in urban environments, customers’ behavior in shopping Mall (Ali, Moulin 2005), the use of geosimulation to support planning activities in dynamic spatial situations and its application to the attack of forest fires, the use of MAGS to simulate the propagation of West Nile Virus in a large territory (Southern part of Quebec).

Click here if you want to see a Powerpoint presentation about the MAGS Project

Demonstration and Tear Gaz in front of Quebec parliament House

A fence blocks the crowd


A method to develop multi-agent geo-simulation applications

We developped a generic approach (Ali Moulin 2005) that we refined when developing several multi-agent geo-simulations which simulate various kinds of spatial behaviors in virtual geographic environments. Several of these applications are related to the customers’ shopping behavior shopping malls : One in Toronto (Square One) and three in Quebec City (Place Ste-Foy, Place De La Cité and Place Laurier). We also developed an application that simulates the shopping behavior of customers in a big department store (Zellers). The main aim of the development of this kind of applications is to help shopping mall or retail managers to understand the shoppers’ behavior in the shopping mall or in a store, in order to evaluate how the changes of the layout of the mall or store can influence shoppers’ behaviors.

The Virtual Geographic Environment

The spatial characteristics of the simulation environment and static objects are generated from data stored in a Geographic Information System and in related databases. The spatial characteristics of the environment are recorded in raster mode which enables agents to access the information contained in various bitmaps that encode different kinds of information about the virtual environment and the objects contained in it. The AgentsMap contains the information about the locations of agents and the static objects contained in the environment. The ObstaclesMap contains the locations of obstacles, the AriadneMap contains the paths that can be followed by mobile agents, the HeightMap represents the elevations of the environment, etc. The information contained in the different bitmaps influences the agent’s perception and navigation. In MAGS the simulation environment is not static and can change during the simulation. For example, we can add new obstacles, or gaseous phenomena such as smoke, dense gases and odors which are represented using particle systems, etc. (Moulin et al., 2003). A module in MAGS simulates the propagation of dense gas or smoke. Another module enables the user to manipulate or add objects in the virtual environment while the simulation runs: hence, agents perceive these new obstacles and react accordingly.

More details can be found in the MAGS Presentation and in (Moulin et al. 2003)

Some details about MAGS agents’ capabilities

MAGS agents have several knowledge-based capabilities.

- The agent perception process: In MAGS agents can perceive (1) terrain characteristics such as elevation and slopes; (2) the elements contained in the landscape surrounding the agent including buildings and static objects; (3) other mobile agents navigating in the agent's range of perception; (4) dynamic areas or volumes whose shape changes during the simulation (ex.: smoky areas or zones having pleasant odors); (5) spatial events such as explosions, etc. occurring in the agent's vicinity; (6) messages communicated by other agents. (Moulin and al., 2003).

- The agent navigation process: In MAGS agents can have two navigation modes: Following-a-path-mode in which agents follow specific paths which are stored in a bitmap called ARIANE_MAP or Obstacle-avoidance-mode in which the agents move through open spaces avoiding obstacles. In MAGS the obstacles to be avoided are recoded in specific bitmap called OBSTACLE_MAP.

- The memorization process: In MAGS the agents have three kinds of memory: Perception memory in which the agents store what they perceive during the last few simulation steps; Working memory in which the agents memorize what they perceive in one simulation and Long-term memory in which the agents store what they perceived in several simulations (Perron et Moulin., 2004).

- The agent's characteristics: In MAGS an agent is characterized by a number of variables whose values describe the agent's state at any given time. We distinguish static states and dynamic states. A static state does not change during the simulation and is represented by a variable and its current value (ex.: gender, age group, occupation, marital status). A dynamic state is a state which can possibly change during the simulation (ex.: hunger, tiredness, stress). A dynamic state is represented by a variable associated with a function which computes how this variable changes values during the simulation. The variable is characterized by an initial value, a maximum value, an increase rate, a decrease rate, an upper threshold and a lower threshold which are used by the function. Using these parameters, the system can simulate the evolution of the agents' dynamic states and trigger the relevant behaviors (Moulin et al., 2003).

- The objective-based behavior: In MAGS an agent is associated with a set of objectives that it tries to reach. The objectives are organized in hierarchies which are is composed of nodes that represent composite objectives and leaves that represent elementary objectives which are associated with actions that the agent can perform. Each agent owns a set of objectives corresponding to its needs. An objective is associated with rules containing constraints on the activation and the completion of the objective. Constraints are dependent on time, on the agent's states, and the environment's state. The selection of the current agent's behavior relies on the priority of its objectives. Each need is associated with a priority which varies according to the agent's profile. An objective's priority is primarily a function of the corresponding need's priority. It is also subject to modifications brought about by the opportunities that the agent perceives or by temporal constraints (Moulin and al., 2003).

- The agent communication process: In MAGS agents can communicate with other agents by exchanging messages using mailbox-based communication.

More details can be found in the MAGS Presentation and in (Moulin et al. 2003)