If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Procedural Level Generation via Machine Learning (PLGML), the study of generating game levels with machine learning, has received a large amount of recent academic attention. For certain measures these approaches have shown success at replicating the quality of existing game levels. However, it is unclear the extent to which they might benefit human designers. In this paper we present a framework for co-creative level design with a PLGML agent.
We introduce the problem of generating Let's Play-style commentary of gameplay video via machine learning. We propose an analysis of Let's Play commentary and a framework for building such a system. To test this framework we build an initial, naive implementation, which we use to interrogate the assumptions of the framework. We demonstrate promising results towards future Let's Play commentary generation.
Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning. One of the benefits of PCGML is that, unlike search or grammar-based PCG, it does not require hand authoring of initial content or rules. Instead, PCGML relies on existing content and black box models, which can be difficult to tune or tweak without expert knowledge. This is especially problematic when a human designer needs to understand how to manipulate their data or models to achieve desired results. We present an approach to Explainable PCGML via Design Patterns in which the design patterns act as a vocabulary and mode of interaction between user and model. We demonstrate that our technique outperforms non-explainable versions of our system in interactions with five expert designers, four of whom lack any machine learning expertise.
Guzdial, Matthew, Riedl, Mark
Automated game design has remained a key challenge within the field of Game AI. In this paper, we introduce a method for recombining existing games to create new games through a process called conceptual expansion. Prior automated game design approaches have relied on hand-authored or crowd-sourced knowledge, which limits the scope and applications of such systems. Our approach instead relies on machine learning to learn approximate representations of games. Our approach recombines knowledge from these learned representations to create new games via conceptual expansion. We evaluate this approach by demonstrating the ability for the system to recreate existing games. To the best of our knowledge, this represents the first machine learning-based automated game design system.
The ability to extract the sequence of game events for a given player's play-through has traditionally required access to the game's engine or source code. This serves as a barrier to researchers, developers, and hobbyists who might otherwise benefit from these game logs. In this paper we present two approaches to derive game logs from game video via convolutional neural networks and transfer learning. We evaluate the approaches in a Super Mario Bros. clone, Mega Man and Skyrim. Our results demonstrate our approach outperforms random forest and other transfer baselines.
Guzdial, Matthew James (Georgia Institute of Technology) | Chen, Jonathan (Georgia Institute of Technology) | Chen, Shao-Yu (Georgia Institute of Technology) | Riedl, Mark (Georgia Institute of Technology)
Zook, Alexander E. (Georgia Institute of Technology) | Cook, Michael (Cut Garnet Games) | Butler, Eric (Golden Glitch Studios) | Siu, Kristin (Golden Glitch Studios) | Guzdial, Matthew (Georgia Institute of Technology) | Riedl, Mark (Georgia Institute of Technology) | Ryan, James (University of California, Santa Cruz) | Samuel, Ben (University of California, Santa Cruz) | Summerville, Adam (University of California, Santa Cruz) | Mateas, Michael (University of California, Santa Cruz) | Wardrip-Fruin, Noah (University of California, Santa Cruz)
The AIIDE Playable Experiences track celebrates innovations in how AI can be used in polished interactive experiences. Four 2016 accepted submissions display a diversity of approaches. Rogue Process combines techniques for medium-permanence procedurally generated hacking worlds. Elsinore applies temporal predicate logic to enable a time-traveling narrative with character simulation. A novel level generator uses conceptual blending to translate Mario Bros. design styles across levels. And Bad News uses deep simulation of a town and it's residents to ground a mixed-reality performance. Together these playable experiences showcase the opportunities for AI in interactive experiences.
Interactive narrative is a form of storytelling in which users affect a dramatic storyline through actions by assuming the role of characters in a virtual world.This extended abstract outlines the Scheherazade-IF system, which uses crowdsourcing and artificial intelligence to automatically construct text-based interactive narrative experiences.
We introduce Dramatis, a computational model of suspense based on a reformulation of a psychological definition of the suspense phenomenon. In this reformulation, suspense is correlated with the audience’s ability to generate a plan for the protagonist to avoid an impending negative outcome. Dramatis measures the suspense level by generating such a plan and determining its perceived likelihood of success. We report on three evaluations of Dramatis, including a comparison of Dramatis output to the suspense reported by human readers, as well as ablative tests of Dramatis components. In these studies, we found that Dramatis output corresponded to the suspense ratings given by human readers for stories in three separate domains.
Li, Boyang (Georgia Institute of Technology) | Lee-Urban, Stephen (Georgia Institute of Technology) | Johnston, George (Georgia Institute of Technology) | Riedl, Mark (Georgia Institute of Technology)
Story generation is the problem of automatically selecting a sequence of events that meet a set of criteria and can be told as a story. Story generation is knowledge-intensive; traditional story generators rely on a priori defined domain models about fictional worlds, including characters, places, and actions that can be performed. Manually authoring the domain models is costly and thus not scalable. We present a novel class of story generation system that can generate stories in an unknown domain. Our system (a) automatically learns a domain model by crowdsourcing a corpus of narrative examples and (b) generates stories by sampling from the space defined by the domain model. A large-scale evaluation shows that stories generated by our system for a previously unknown topic are comparable in quality to simple stories authored by untrained humans