Goud, F

Hickmott, S

Padgham, L

Scerri, D

Yehuda, I

Zambetta, F

RMIT Research Repository

Bushfire BLOCKS: A Modular Agent-based Simulation
David Scerri Sarah Hickmott Fabio Zambetta
Ferdinand Gouw Isaac Yehuda
School of CS and IT
RMIT University
Melbourne, Australia
firstname.lastname@rmit.edu.au
Lin Padgham
ABSTRACT
Bushfire BLOCKS is a modular, distributed, agent-based
simulation for exploring and informing bushfire response
strategies. Separate independent modules capture the fire
spread, evacuation of traffic and human behaviour such as
the decision whether to remain at property. These modules
have been developed largely independently, using paradigms
and data models appropriate to their individual purpose.
Modules are integrated into a single global simulation
via central services for managing time advancement and
access to shared variables. Execution is distributed, with
modules running on different machines, whilst a custom
interface enables viewing and control from one screen. The
underlying architecture of this system facilitates extension
of individual modules and addition of new modules, with
limited alteration to other modules.
Categories and Subject Descriptors
I.6.3 [Simulation and Modeling]: Applications
General Terms
Design, Experimentation
Keywords
Agent-based simulation, modular architecture, distributed
simulation, bushfire response
1. SYSTEM OVERVIEW
Faced with a changing climate, and threatened by an
increased risk of major disasters ranging from bushfire to
flooding, the world is looking in all directions for long-term
adaptation solutions and more effective emergency response
strategies. Agent-based simulation is possibly the only
experimental means for exploring complex social systems,
(see e.g. [2]), and can thus play an important role in the
exploration of adaptation and emergency response strategies
which – undoubtedly – must include consideration of human
behaviour. In order to be useful however, these simulations
Cite as: Bushfire BLOCKS: A Modular Agent-based Simulation,
D. Scerri, S. Hickmott, F. Zambetta, F. Gouw, I. Yehuda and L.
Padgham, Proc. of 9th Int. Conf. on Autonomous Agents and
Multiagent Systems (AAMAS 2010), van der Hoek, Kaminka,
Lespérance, Luck and Sen (eds.), May, 10–14, 2010, Toronto, Canada, pp.
XXX-XXX.
Copyright c© 2010, International Foundation for Autonomous Agents and
Multiagent Systems (www.ifaamas.org). All rights reserved.
must capture a range of aspects of the considered scenario,
each possibly requiring distinct expertise. Moreover, dif-
ferent paradigms may be useful in modelling, ranging from
those that use many lightweight reactive agents, to those
that use cognitive agents, to those that focus on agent teams
and organisational structures. Our work here has been
driven by the desire to build up an increasingly realistic
simulation by incrementally adding new modules, created
by members of a large distributed community interested
in the application area, without placing restrictions on the
paradigms and data models to be used by each module.
Bushfire BLOCKS1 is an extensible agent-based bushfire
simulation currently consisting of three individual modules,
developed independently and run on separate machines,
integrated into a single global simulation via central co-
ordination services and a custom interface. The aim of
Bushfire BLOCKS is to let users experiment interactively
with bushfire evacuation strategies, to appreciate how their
choices unfold throughout the simulation. Residents of the
coastal town of Anglesea are threatened by a bushfire which
spreads through the town during the simulation. Based
on various personal factors, and public information, each
resident may – at some point during the simulation – decide
to go to an evacuation point. The chosen route of an
evacuating resident depends on information available to
them both visually and via radio announcements. Besides
setting the initial attributes of residents, Bushfire BLOCKS
allows users to change the location of roads and evacuation
points, sound a warning system, and make announcements
on the local radio to inform people of the current location of
fire, road blocks, etc. Clearly, this has the potential to help
predict similar patterns and situations arising in real-life fire
emergencies, concurrently teaching simulation users about
complex choices, such as when to retreat to an evacuation
point or shelter in place.
It should be noted how the fidelity of the simulation is
achieved via real-data collections such as vegetation infor-
mation retrieved by the State of Victoria in the coastal town
Anglesea, and real road maps, conditions and topography.
Moreover, the fire module uses Fire Behaviour SDK fire
spread libraries as used in FARSITE 2, incorporating surface
and crown fires thus further increasing the accuracy of the
simulation offered by Bushfire BLOCKS.
Whilst there exists various significant frameworks for
modular distributed simulation, none are entirely appro-
1Building Large, Open, Complex, Knowledge-Based Simu-
lations (BLOCKS).
2http://firemodels.fire.org/content/view/112/143/
priate for application to multi-agent simulation and the
type of modularisation we are targeting. In particular,
difficulties arise when multiple modules affect the same
variable at the same logical time, in particular if this
variable is part of an atomic action involving multiple
variables. A detailed discussion of the problems can be
found in [3]. The Bushfire BLOCKS system employs an
architecture we developed specifically for the integration
of multiple modules, including agent-based simulations,
into a single global simulation [3]. Each module captures
some specialised aspect of the scenario – human behaviour,
evacuating traffic, fire spread – and multiple modules can
potentially affect the same aspect of an environment at the
same logical time, e.g. traffic and behaviour modules both
depend on and influence human agents’ levels of panic.
2. INDIVIDUAL MODULES
Bushfire BLOCKS currently consists of three modules,
each capturing a different aspect of the bushfire response
scenario: Fire Spread, Traffic Evacuation and Human
Behaviour.
The Human Behaviour module captures the decision pro-
cess surrounding the question to “stay or go”, for individuals
exposed to a bushfire threat. Each person is modeled
by a single agent with attributes such as age, gender,
health, energy, velocity, panic level, access to vehicle, family
members, perceived distance from hazards (e.g fire, smoke),
visibility, and area fire rating. An agent’s decision to
shelter at their property, or evacuate, is based on: the
aforementioned attributes; other people leaving the area
(herding behaviour); and, any external help available. Panic
level greatly affects an agent’s decision, and is based on
factors such as distance from the fire, emergency level
declared by local authority, knowledge of road blocks, family,
access to a vehicle, and the time of the fire.
The Traffic Evacuation module adapts and extends
Nick Malleson’s Repast City program3 to simulate the
evacuation of all agents that have decided to leave their
property. The module takes in a GIS map of roads and
houses, and information regarding the location of people,
smoke and fire. When an agent decides to leave, they are
made to follow the shortest path to an evacuation point,
along which the individual agent is not aware of any hazard
(e.g. fire, road block). Whilst moving, agents continue to
scan their surrounding area and listen to the radio, updating
their knowledge accordingly. If they become aware of a
hazard in their planned path they calculate a new route.
Agents also keep some distance between each other and try
to avoid collisions, and travel faster on roads.
The Fire Spread module utilises the widely used Fire
Behaviour SDK to model the growth of a bushfire through
a rural area. It incorporates topographical, vegetation and
wind and weather information to realistically simulate the
behaviour of the fire in a specific environment. Surface fires,
crown fires and spotting are all modelled separately within
the module and smoke and heat data is also produced.
Each of these modules have been developed independently,
using the paradigm and data structures considered to be
most appropriate for their individual purpose. For example
the Traffic Evacuation module uses a coordinate-based
map of Anglesea whereas the Human Behaviour simulation
3http://portal.ncess.ac.uk/access/wiki/site/mass/
repastcity.html
is cell-based. Whilst both these modules use lightweight
reactive agents, implemented in Repast4, we are currently
developing a Country Fire Authority (CFA) module – to
capture the service provided by this fire fighting organisa-
tion – which uses the Belief Desire Intention (BDI) agent
paradigm. This will allow us to assign CFA agents more
complex reasoning and planning abilities, and more easily
include team-oriented behaviour.
3. INTEGRATED SIMULATION
The architecture of Bushfire BLOCKS provides centralised
services which integrate the above modules into a common
environment. Two main services are the Time Manager
(TM) and the Conflict Resolver (CR). The TM ensures
that all modules advance time in a consistent way despite
having potentially different time scales. This is implemented
in much the same way as the conservative algorithm of
the High Level Architecture [1]. The CR manages module
interaction with shared data to avoid the potential problems
identified in [3]. For example, the Evacuating Traffic and
Human Behaviour module may concurrently access agents’
panic level; the CR resolves any conflicts between logically
concurrent updates or value-based decisions.
The most important aspects of each module are integrated
into a single custom interface, to allow viewing and control
from a single screen where appropriate. However, individual
modules can run their own user display. The use of multiple
displays allows analysis of the simulation data from the
different points of view offered to the user For instance,
in the Traffic Evacuation module the user can choose
to view the perspective of an individual agent, which can
give a better understanding of the reasoning behind their
movements, or the entire road network. This ability to zoom
in and out of the data allows the user to concentrate on
specific details of the simulation, or grasp the bigger picture.
The modularity of the Bushfire BLOCKS framework means
that we can incrementally increase the complexity or focus of
the simulation, by extending an existing module, or adding
an entirely new, possibly pre-existing, module. This aids
the creation of more complex global simulations. Moreover,
modules can be re-used in a different scenario. For example
the Fire Spread module may be part of a simulation which
looks at longer term adaptive responses to bushfire such
as town planning. Finally, the architecture of Bushfire
BLOCKS enables us to run each module on a separate machine.
In these ways, both the conceptual and computational
complexity of the bushfire scenario is addressed.
4. ACKNOWLEDGMENTS
ARC grant DP1093290; NCCARF grant EM09056; Nick
Malleson (Repast City).
5. REFERENCES
[1] R. M. Fujimoto. Time Management in The High Level
Architecture. Simulation, 71(6):388–400, 1998.
[2] G. Gilbert and K. Troitzsch. Simulation for the social
scientist. Open University Press, 2005.
[3] D. Scerri, S. Hickmott, L. Padgham, and A. Drogoul. An
architecture for modular distributed simulation with
agent-based models. In Ninth International Joint
Conference on Autonomous Agents and Multiagent Systems
(AAMAS ’10). ACM, 2010.
4http://repast.sourceforge.net/


Bushfire blocks: A modular agent-based simulation

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Bushfire blocks: A modular agent-based simulation

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