How game complexity affects the playing behavior of synthetic agents

TL;DR

This paper investigates how varying levels of complexity in social environments influence the learning and playing strategies of synthetic agents in turn-based zero-sum games, highlighting the need for adaptive behaviors.

Contribution

It introduces an analysis of the relationship between environmental complexity and agent behavior, demonstrating the necessity for adaptive learning strategies in complex social simulations.

Findings

Agent behavior adapts to environmental complexity

Performance can be maintained through strategy adjustment

Complex environments require more sophisticated learning tactics

Abstract

Agent based simulation of social organizations, via the investigation of agents' training and learning tactics and strategies, has been inspired by the ability of humans to learn from social environments which are rich in agents, interactions and partial or hidden information. Such richness is a source of complexity that an effective learner has to be able to navigate. This paper focuses on the investigation of the impact of the environmental complexity on the game playing-and-learning behavior of synthetic agents. We demonstrate our approach using two independent turn-based zero-sum games as the basis of forming social events which are characterized both by competition and cooperation. The paper's key highlight is that as the complexity of a social environment changes, an effective player has to adapt its learning and playing profile to maintain a given performance profile