Agent cognition through micro-simulations: Adaptive and tunable intelligence with NetLogo LevelSpace

TL;DR

This paper introduces ACMCC, a novel method for enhancing agent cognition in agent-based models by using micro-simulations within NetLogo, allowing for adaptable and interpretable intelligence that improves agent behavior and model dynamics.

Contribution

The paper presents ACMCC, a new approach that uses separate agent-based models for cognition, implemented with NetLogo LevelSpace, to provide tunable and realistic agent intelligence.

Findings

ACMCC improves agent decision-making and performance.

The method significantly impacts model dynamics.

ACMCC offers a reliable and understandable way to control agent intelligence.

Abstract

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.