PDE-based simulation, in particular when simulating individuals in dynamic en-
vironments or "messy" systems, Parunak et al. [1998]; Moss [2000].
and economical systems, typically using the SWARM system, Hiebeler [1994].
Some examples are agent-based simulation of a suburban sprawl (Rand et al.
[2003], agent-based computational economics (Tesfatsion [2002]) and agent-based
modeling of biological communities (Hraber and Milne [1996]).
been used to model and simulate network security attacks (Gorodetski et al.
[2003]), and modeling and simulating of (small world) social networks of web
surfers (Haridi [2002]).
primarily focusing on the modeling of intelligent non-personal characters in games
with a relatively low number of players and non-personal characters (typically
less than 100), e.g.
simulation game (Huber and Hadley [1997]), and complexity comparison of a
state-machine model with an agent-based BDI model for modeling intelligence in
games (Bartish and Thevathayan [2002]).
little published work on (agent-based) simulation of them. A partially related
areas with more activity include animation of large crowds for movies (e.g. the
MASSIVE system for simulating a large orc army in the Lord of Rings trilogy).
One exception is the FreeMMG platform that simulates players of massively
multiplayer online games using agents in a hybrid peer-to-peer and client-server
multiplayer game simulation model Cecin et al. [2003]. FreeMMG's purpose is to
provide a generic simulation platform that allows cheaper and easier performance
and reliability testing of MMOGs.
(game mining) by drawing parallels to the established field of web mining. This
approach is partially implemented in the MMOG agent-based simulation platform
called Zereal presented in paper C (Tveit et al. [2003b]), and empirically tested for
computational performance in paper D (Tveit [2003b]) and preliminary testing
of player category classification from Zereal logs in paper I (Tveit [2003a]).