  Agent-based modeling is a paradigm of modeling dynamic systems of interacting
agents that are individually governed by specified behavioral rules. Training a
model of such agents to produce an emergent behavior by specification of the
emergent (as opposed to agent) behavior is easier from a demonstration
perspective. Without the involvement of manual behavior specification via code
or reliance on a defined taxonomy of possible behaviors, the demonstrator
specifies the desired emergent behavior of the system over time, and retrieves
agent-level parameters required to execute that motion. A low time-complexity
and data requirement favoring framework for reproducing emergent behavior,
given an abstract demonstration, is discussed in [1], [2]. The existing
framework does, however, observe an inherent limitation in scalability because
of an exponentially growing search space (with the number of agent-level
parameters). Our work addresses this limitation by pursuing a more scalable
architecture with the use of neural networks. While the (proof-of-concept)
architecture is not suitable for many evaluated domains because of its lack of
representational capacity for that domain, it is more suitable than existing
work for larger datasets for the Civil Violence agent-based model.
