  We present a method of endowing agents in an agent-based model (ABM) with
sophisticated cognitive capabilities and a naturally tunable level of
intelligence. Often, ABMs use random behavior or greedy algorithms for
maximizing objectives (such as a predator always chasing after the closest
prey). However, random behavior is too simplistic in many circumstances and
greedy algorithms, as well as classic AI planning techniques, can be brittle in
the context of the unpredictable and emergent situations in which agents may
find themselves. Our method, called agent-centric Monte Carlo cognition
(ACMCC), centers around using a separate agent-based model to represent the
agents' cognition. This model is then used by the agents in the primary model
to predict the outcomes of their actions, and thus guide their behavior. To
that end, we have implemented our method in the NetLogo agent-based modeling
platform, using the recently released LevelSpace extension, which we developed
to allow NetLogo models to interact with other NetLogo models. As an
illustrative example, we extend the Wolf Sheep Predation model (included with
NetLogo) by using ACMCC to guide animal behavior, and analyze the impact on
agent performance and model dynamics. We find that ACMCC provides a reliable
and understandable method of controlling agent intelligence, and has a large
impact on agent performance and model dynamics even at low settings.
