Abstract:
Crowd simulation is listed under many
practical applications in computer industry; such as
safety modeling, pre-planning building architectures,
urban modeling and entertainment software. Most of
these existing simulations are created by implementing
computer algorithms based on extending deterministic
models such as particle systems, clustering, cellular
automata and fluid motion. However, extending a
crowd model to simulate uncertainty of crowd
behaviour during panic still remains a key challenge;
since a computer algorithm approaches a solution by
parameterizing predictable elements within a problem.
It is evident from literature about the proven
success of multi-agent technology behind modeling
complex systems; comprising of many distributed
entities interacting with each other and operated under
lot of uncertainty. Thus it can be postulated that multiagent
technology provides a basis to model the
uncertainty raised within a crowd during panic. Our
proposed solution simulates this uncertainty by
considering evacuation of a crowd from a building
during fire. Each individual in the crowd is modeled as
an agent associated with a local ontology. The local
ontology of an agent is a collection of rules, representing
the knowledge known to each individual prior to
occurring fire. Rules embedded within local ontologies
are shared among individuals as they interact with each
other. As a result non-anticipated global behaviours
arise within the crowd leading to emerging uncertainty
during fire. Output of the system is a visualization of
crowd behaviour during fire with recorded statistics.
The statistics recorded during each simulation session
indicate evacuation related information per each
individual; providing a basis for evaluation by
comparison with real world observations.
