A Genetic Programming Framework for 2D Platform AI

TL;DR

This paper introduces a genetic programming framework for creating 2D platform game AI that incorporates human input, enabling designers to craft and optimize game characters more efficiently and intuitively.

Contribution

The paper presents a novel GP-based system that integrates human controller input for developing and refining game AI, reducing manual effort and maintaining designer involvement.

Findings

Framework successfully models player behavior

Allows designers to include their play style easily

Provides insights into player expression in games

Abstract

There currently exists a wide range of techniques to model and evolve artificial players for games. Existing techniques range from black box neural networks to entirely hand-designed solutions. In this paper, we demonstrate the feasibility of a genetic programming framework using human controller input to derive meaningful artificial players which can, later on, be optimised by hand. The current state of the art in game character design relies heavily on human designers to manually create and edit scripts and rules for game characters. To address this manual editing bottleneck, current computational intelligence techniques approach the issue with fully autonomous character generators, replacing most of the design process using black box solutions such as neural networks or the like. Our GP approach to this problem creates character controllers which can be further authored and developed by a designer it also offers designers to included their play style without the need to use a programming language. This keeps the designer in the loop while reducing repetitive manual labour. Our system also provides insights into how players express themselves in games and into deriving appropriate models for representing those insights. We present our framework, supporting findings and open challenges.