VGLC SMB + KI Levels Generalized SMB + KI Levels Combined Generator Sequence Classifier

Recent work has shown machine learning approaches to be effective in training models on existing game data for informing procedural generation of novel content. Specifically, ML techniques have successfully used existing game levels to train models for generating new levels and blending existing ones. Such blending of not just levels, but entire games, has also been proposed as a possible means of generating new games. In this paper, we build on these works by training Long Short Term Memory recurrent neural networks on level data for two separate platformer games—Super Mario Bros. and Kid Icarus—and use these models to create new levels that are blends of levels from both games, thus creating a level generator for a novel, third game. Introduction Procedural content generation (PCG) is the automatic creation of game content via procedural or algorithmic methods (Shaker, Togelius, and Nelson 2016). Since its use in games like Elite (Braben and Bell 1984) and Rogue (A.I. Design 1980), PCG has been widely adopted for generating levels, maps, weapons, rules, terrain, etc., and has been an active field of games research. Traditional PCG methods include search-based optimization (Togelius et al. 2011), constraint satisfaction (Smith and Mateas 2011) and grammar-based methods (Smith, Whitehead, and Mateas 2011), to name a few. However, these often rely on designer-authored rules, parameters and constraints to guide the generation process and ensure that the generated content has desired properties and characteristics. Aside from being time consuming, such methods may unintentionally capture designer biases. PCG via Machine Learning (PCGML) has thus emerged as an alternative to the traditional methods to help overcome their limitations. Summerville et al. (2017) define PCGML as “the generation of game content using models that have been trained on existing game content. ” By training on game data that one wishes to emulate or create novel variations of, one can capture the desired properties within the trained model and sample from it to generate new content. Recent work has shown that models trained on existing Super Mario Bros. levels can generate new levels via both sequence prediction (Summerville and Mateas 2016) as Copyright c © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. well as blending existing levels (Guzdial and Riedl 2016b). Moreover, Gow and Corneli (2015) proposed a theoretical framework for generating new games by blending not just levels but entire games, showing that it is possible to blend two games to create a third whose aesthetics and mechanics are combinations of those of the original two games. In this work, we take a step towards implementing this framework by leveraging PCGML and concept blending. Specifically, we train Long Short Term Memory recurrent neural networks (LSTMs) on existing levels of the platformers Super Mario Bros. and Kid Icarus. We then sample from the trained models to generate new levels that encode structural characteristics and properties of levels from both games. We thus create a level generator that can produce levels for a third game whose levels contain properties of levels from the two games used for training. We also implement a parametrized version of the generator that allows a designer to control the approximate amount of each original game desired in the final blended levels. Related Work PCG via Machine Learning (PCGML). PCGML has emerged as a promising research area as evidenced by many successful applications of ML for content generation. Neural networks in particular have seen wide use for level and map generation. Hoover, Togelius, and Yannakakis (2015) generated Super Mario Bros. levels by combining a music composition technique (functional scaffolding) with a method for evolving ANNs (NeuroEvolution of Augmenting Topologies (Stanley and Miikkulainen 2002)). Autoencoders have also found related applications, with Jain et al. (2016) training one on existing levels to generate new levels and repair generated levels that were unplayable. N-gram models have also been used by Dahlskog, Togelius, and Nelson (2014) for generating Super Mario Bros. levels. Guzdial and Riedl (2016a) used probabilistic graphical models and clustering to create levels for Super Mario Bros. by training on gameplay videos. Besides platformers, Summerville et al. (2015) used Bayes Nets for creating The Legend of Zelda levels. They also used Principal Component Analysis to interpolate between existing Zelda dungeons to create new ones (Summerville and Mateas 2015). Markov models have also found use in generating content. Snodgrass and Ontañón have done extensive work in generating levels for Super Mario Bros. , Kid Icarus and Lode Runner (Broderbund 1983) using multi-dimensional Markov chains (Snodgrass and Ontañón 2014), with hierarchical (Snodgrass and Ontañón 2015) and constrained (Snodgrass and Ontañón 2016) extensions as well as using Markov random fields. In particular, our work in blending levels is most similar to their work on domain transfer using Markov chains for mapping levels from one game to another (Snodgrass and Ontañón 2017). Finally, Long Short Term Memory recurrent neural networks (LSTMs) have been particularly effective in generating game levels. Summerville and Mateas (2016) used LSTMs to generate Mario levels by treating each level as a character string and using these strings to train the network. The trained network could then use an initial string as a starting seed to generate new characters, thereby producing new levels. Though most level generation using LSTMs focuses primarily on Super Mario Bros. , the method can be used to create generators for any game whose levels are represented as sequences of text. The success of such techniques for level generation informed our use of LSTMs in this work. Mixed-Initiative PCG. This refers to content generation by harnessing the capabilities of a procedural generator and a human designer working in concert. Such generators, like Tanagra (Smith, Whitehead, and Mateas 2011), combine the generator’s ability to rapidly produce multiple levels with the human ability to evaluate them using superior creativity and judgment. Authorial control in procedural generators allows designers to guide generation towards desired content. Thus, we implemented a parametrized variant of the blended level generator which lets the designer control the percentage of either game in the final blend. Yannakakis, Liapis, and Alexopoulos (2014) offer an extensive analysis of mixedinitiative design tools and their effects on creativity. Concept Blending. Concept blending states that novel concepts can be produced by combining elements of existing concepts. The “four space” concept blending theory was proposed by Fauconnier and Turner (1998; 2008) and describes a conceptual blend as consisting of four spaces: • Two input spaces constituting the concepts prior to being combined • A generic space into which the input concepts are projected and equivalence points are identified • A blend space into which the equivalent points from the generic space are projected and new concepts are evolved Goguen (1999) offers a related formalization of conceptual blending which forms the basis of the COINVENT computational model (Schorlemmer et al. 2014). This aims to develop a “computationally feasible, formal model of conceptual blending” and has applied conceptual blending in music (Cambouropoulos, Kaliakatsos-Papakostas, and Tsougras 2014) and mathematics (Bou et al. 2015). Blending Game Levels and Game Generation. In games, Guzdial and Riedl (2016b) used conceptual blending to blend level generation models of Super Mario Bros. and also looked at different blending approaches to generate levels (Guzdial and Riedl 2017). Additionally, Gow and Corneli (2015) proposed applying conceptual blending not just to ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( Q Q S ? S Q S < > E [ ] X X X X X X X X X X X X X X X X X Figure 1: Super Mario Bros. Level 1-1 and its text representation. # T T T T T T T T T ( # ( # ( # D # ( # D # # ( # # # # # # # # # # ( # # # # ( # # # # # # # ( # # # # # # # # ( # # # # ( # T # # # ( # # # # # # # # ( # # # # ( # T # # # ( # # # # # # # # ( # # # # ( # T # # # ( Figure 2: Kid Icarus Level 1 and its text representation. levels of a game, but to games in their entirety. They presented a framework for generating a novel game by using the four space blending process of selecting two input games to blend, creating a generic game concept and then producing a new blended game by combining the generalized elements of the input games. They used the Video Game Description Language (VGDL) (Schaul 2014) to generate a novel game by combining VGDL specifications of The Legend of Zelda (Nintendo 1986b) and Frogger (Konami 1981).