Combinational machine learning creativity

Abstract

Computational creativity is a field focused on the study and development of behaviors in computers an observer would deem creative. Traditionally, it has relied upon rules-based and search-based artificial intelligence. However these types of artificial intelligence rely on human-authored knowledge that can obfuscate whether creative behavior arose due to actions from an AI agent or its developer. In this dissertation I look to instead apply machine learning to a subset of computational creativity problems. This particular area of research is called combinational creativity. Combinational creativity is the type of creativity people employ when they create new knowledge by recombining elements of existing knowledge. This dissertation examines the problem of combining combinational creativity and machine learning in two primary domains: video game design and image classification. Towards the goal of creative novel video game designs I describe a machine-learning approach to learn a model of video game level design and rules from gameplay video, validating the accuracy of these with a human subject study and automated gameplaying agent, respectively. I then introduce a novel combinational creativity approach I call conceptual expansion, designed to work with machine-learned knowledge and models by default. I demonstrate conceptual expansion’s utility and limitations across both domains, through the creation of novel video games and applied in a transfer learning framework for image classification. This dissertation seeks to validate the following hypothesis: For creativity problems that require the combination of aspects of distinct examples, conceptual expansion of generative or evaluative models can create a greater range of artifacts or behaviors, with greater measures of value, surprise, and novelty than standard combinational approaches or approaches that do not explicitly model combination.